[2 of 4]

• How curious are you? Do you have more ideas then you can execute? What are your curiosities about? What are your ideas about - is it environmental or conceptual, and can you please elaborate?
  
  • I think this is something I struggle with on a daily basis. I like many things, or so I like to believe. Like I feel that everything’s interesting and everything is connected somehow through symbols. I like thinking about these symbols and connections constantly. So my ideas are about concepts mostly. I can’t remember facts if I can’t attach them to concepts that make sense to me.
    
  • This has been my latest conflict I have to say. I started a career in EE, and then I shifted to computer science. I’ve wanted since I was an undergrad to start a research path, but I’ve been struggling to find something I really really love. I am not good at taking decisions, but an academic path looks now like my best bet for not working in a desk never again (I like having my own desk at home, though).
    
  • I’m confident everything will be good at the end, and I am confident I can do almost anything. Not trying to be cocky, is just that I know I’m physically and mentally capable of learning anything (in the realm of normal stuff, of course I won’t build a heavy falcon myself), so unless that does not change, I’m good. On the other hand, being so certain about that backfires at me, filling my head with “what ifs”
    
  • I have this bad habit of reading (and most of the time not finishing) books in parallel, now I’m reading about
    
    • Carl Jung (https://www.amazon.com/Jung-Short-Introduction-Anthony-Stevens/dp/0192854585 mostly motivated by understanding this mbti thingy)
      
    • About network science (https://www.oreilly.com/library/view/complex-network-analysis/9781680505399/ and https://www.amazon.com/Networks-Introduction-Mark-Newman/dp/0199206651 because network analysis can be applied to almost anything)
      
    • Machine Learning (http://shop.oreilly.com/product/0636920052289.do) because is what the cool kids do in IT now or so I’ve learned, I’m more into software architecture though, and
      
    • EGB (https://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567) which is a beast and I wanted to get my feet wet about cognitive science and semiotic. I am at about 40% of all of them,
      
  • I pick a chapter until I finish it, and then I move on to the next book, when I have time. I’ve lost interest in reading fiction, I get that from reading graphic novels and manga, mostly. If it matters something, currently ongoing mangas I like are Hajime no Ippo, One Piece, Vinland Saga and The Promised Neverland.
    
• Would you enjoy taking on a leadership position? Do you think you would be good at it? What would your leadership style be?
  
  • I’m not very good at getting stuff done so I would probably suck as a leader of anything. But hey, I am good listening to people and helping them improve. I also don’t think I’m a good teamplayer. I’m bad at following instructions if I don’t trust them. During college I was the guy that ended redoing the work of others during group assignments, because I either I was not satisfied with their work or I was not good at giving instructions. I didn’t know at that moment I was being a dick and I know now, and it’s not something I’m proud of. I'm working on it.
    
• Are you coordinated? Why do you feel as if you are or are not? Do you enjoy working with your hands in some form? Describe your activity?
  
  • I used to draw more when I was younger, and did a bit of woodwork also. I had plants. I like to cook, and have strong opinions on food. I like creating stuff with my hands, I consider myself a creative person. In short, I am coordinated, but not so with team activities like team sports.
    
• Are you artistic? If yes, describe your art? If you are not particular artistic but can appreciate art please likewise describe what forums of art you enjoy. Please explain your answer.
  
  • It’s hard to pin down what kind of art I like, I just know I like something after I’ve seen it or told about, with no particular topic. I don’t understand sculpture, and I vaguely get poetry. Regarding drawing, I appreciate the flow and light in shapes. I was into human figure for some years, and I did a lot of drawings that were good.
    
  • I know a bit of guitar and ukulele, but I never played for others than girls I like. I am too shy of my voice, my singing and technique, I know it needs improving. I took singing classes once but with only the gist of it I got it’s something that requires more discipline and time than what I’m willing to spend.
    
• What's your opinion about the past, present, and future? How do you deal with them?
  
  • uhm, now I strive to live a life that maximises happiness and minimizes regret. At my age I think I know enough about the things I can control, and play along with that hand, always with the best intentions, and I am optimist about the future.
    
  • Sometimes I regret not being like this in the past, however, and I see myself revisiting things I would have done better, like studying more, eating better, loved more.
    
• How do you act when others request your help to do something (anything)? If you would decide to help them, why would you do so?
  
  • I always help, I believe in karma as a thing (I mean, not religiously) and that life has been really good to me. I don’t help when I know I can’t help, or when I’m being ordered to or asked in a bad way i.e. makes me feel bad. I have trouble noticing these situations though.

I watched this interview earlier today, so after reading this article, I'm a tad disappointed. Artificial intelligence and a Brain Machine Interface are two things I'm super interested in, and this particular technology editor wrote one of the crappiest articles I've read over it.

So here is the article, points, counterpoints, the whole shebang.

---

Article

> Elon Musk smoked pot and drank whiskey on the Joe Rogan podcast..."

He did indeed smoke pot and drink whiskey on the podcast. He had one puff of the pot, and drank one glass of the whiskey. And the pot was near the end. Nothing really serious about this, insofar as I am aware.


> "... and said he's going to soon announce a new "Neuralink" product that can make anyone superhuman."

Outright fabrication. Elon did not remotely say that he's going to soon announce a new Neuralink product that can make anyone superhuman, or suggest that anyone will have anything like that soon.


> "'I think we'll have something interesting to announce in a few months ... that's better than anyone thinks is possible,' the Tesla CEO said on 'Joe Rogan Experience.' 'Best case scenario, we effectively merge with AI.'"

Alright. Those are two actual quotes!

The first quote-- yes, Elon said that he'll have something interesting, possibly, in a few months. Specifically, he says that it is about an order of magnitude better than anyone thinks is possible.

The second sentence is a mostly unrelated part of the conversation about different ways to counter Artificial General Intelligence, which may be an existential threat to humanity and is a possibility. More on this at the end.


> Musk, whose enterprises include a company called Neuralink, says his new technology will be able to seamlessly combine humans with computers, giving us a shot at becoming "symbiotic" with artificial intelligence.

He does not say this at all in the interview. He suggests that becoming symbiotic with an interface that is like an AI is likely the best way forward for mankind, out of the different options. He goes on to explain, though he doesn't use the term, of how an emergent consciousness would work.


> Musk argued that since we're already practically attached to our phones, we're already cyborgs. We're just not as smart as we could be because the data link between the information we can get from our phones to our brains isn't as fast as it could be.

Accurate reporting here, and in the spirit of the actual interview. It doesn't really explain what he means by this, but that'd be a bit much for an article, wouldn't it?


ARTICLE BREAK FOR A QUICK PICTURE IN THE ARTICLE!

> Picture of Elon hitting a blunt

I think it's a blunt, not a spliff. Perfectly alright explaining my thought process if asked.


> "It will enable anyone who wants to have superhuman cognition," Musk said. "Anyone who wants."

I'll have to rewatch the interview to get the exact wording, but I watched it earlier today. I'm pretty confident Elon said 'would', not 'will'. Which doesn't seem like much, but makes a world of difference.

At this point, he is describing what it would be like to have an interface that you could control by thought.


> "Rogan asked how much different these cyborg humans would be than regular humans, and how radically improved they might be."

> "'How much smarter are you with a phone or computer or without? You're vastly smarter, actually,' Musk said. 'You can answer any question pretty much instantly. You can remember flawlessly. Your phone can remember videos [and] pictures perfectly. Your phone is already an extension of you. You're already a cyborg. Most people don't realize you're already a cyborg. It's just that the data rate ... it's slow, very slow. It's like a tiny straw of information flow between your biological self and your digital self. We need to make that tiny straw like a giant river, a huge, high-bandwidth interface.'"

At this point, the cyborg thing is explained a little bit better. The article times it and changes the order of the interview a bit to make him look like a crackpot idiot, but this part is pretty true to form. It doesn't really give much context around the rest of the conversation in the interview, that led up to that, explained ideas before, that sort of thing. But a good paragraph for the article.


> "Musk, who spoke about Neuralink before he smoked pot on the podcast..."

We know he smoked pot.


> "...said this sort of technology could eventually allow humans to create a snapshot of themselves that can live on if our bodies die."

> "'If your biological self dies, you can upload into a new unit. Literally,' Musk said."

This was definitely mentioned as an aside, and as a possibility, by Elon. It did actually explain how it would work. Also, it wasn't a snapshot-- people who study this know there is a big difference between a transition and a snapshot, and Elon did not at all imply it was a snapshot, it was spoken of as if it was a transition-- which is key. But not really something the average person studies, either-- so of course not explaining it.


> "Musk said he thinks this will give humans a better chance against artificial intelligence."

> "'The merge scenario with AI is the one that seems like probably the best. If you can't beat it, join it,' Musk said."

The article manages to make this, which is perhaps the most important section of the interview and a terribly important part of humanity, two short lines with no explanation in such a way that makes the person look like an idiot, ignoring everything he otherwise explained.


> "Tesla's stock took a hit after the bizarre appearance and revelations Friday that two Tesla executives are leaving."

Tesla's stock did indeed take a hit. It's an extremely volatile stock with good and bad news constantly. I personally fail to see how it relates to this article, though-- much like a hit of pot and a glass of whiskey.

---

An actual explanation

Elon Musk started a company called Neuralink somewhat recently. It brought together a board which consists of doctors, engineers, scientists, surgeons-- and in particular, people who were studied in multiples of those fields.

The end goal of Neuralink is to create a low-cost non-invasive brain machine interface (BMI), which would allow you to basically access the internet by thought. Notable is that you would both send and receive messages that your brain could then directly interpret.

With your phone, you can access most of the world's knowledge at your fingertips. The catch with that is that it is a tad slow. You have to pull your phone out, type out words with two thumbs, have pages load slowly, that sort of thing. In this way, you can think of your phone as an extension of yourself, and yourself as a sort of clumsy cyborg.

The company isn't far. I believe I read somewhere that its current goals range on medical uses. Elon mentioned in the interview that they might have something to announce (not even necessarily a product) in a few months. He also uses one of his favorite terms-- it will be an order of magnitude better than anything currently thought possible (by the general public). It will likely be medical in nature and impressive, but not revolutionary.

Actual success is a long, long way off, and nothing Elon said in the interview suggests otherwise.

So that's the gist of the article. As for the actual interview.

Joe Rogan interviewed Elon Musk on his podcast recently, where they discussed lots of things (The Boring Machine, AI, Neuralink, Tesla, SpaceX-- those sorts of things.)

They spent about three hours talking about things, Elon and Joe had a cup of whiskey, Elon had a hit from a blunt, Joe a few hits-- the entire interview was a pretty casual thing. Not a product announcement, nothing like that.

Not at all like this particular technology editor made it out to be.

And that's about it. I have some links on actually interesting reading for this down below.

---

Some resources!

http://podcastnotes.org/2018/09/07/elon/ - Some notes about the interview, and good summary.

https://www.youtube.com/watch?v=ycPr5-27vSI - The actual interview, tad long. AI stuff is the first topic and ends at roughly 33 minute mark.

https://waitbutwhy.com/2017/04/neuralink.html - Article over Neuralink, explaining the company and goal from pretty simple beginnings. Easy to read, wonderfully explanatory.

https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/1501227742 - Superintelligence: Paths, Stranger, and Strategies. Covers artificial general intelligence, why it is a threat, and ways to handle it. Pretty much the entire goal of Neuralink is based off of this book, and it's a very reasonable and quality book.

Unity is the bee's knees.
I've been messing with it casually for several years, and got serious in the last 2-ish years. I like it because I get to use C#, and that's the language I know best. Only problem in it's using some weird limbo version of .NET 2, that's not actually 2.0 but is also 3.0 is some places? I think it's because it's using Mono 2.0, which is some subset of .NET. It's weird. They're moving to 4.5 soon anyways so I'm hype for that. I'ts been a lot of fun regardless, I get to apply a different knowledge and tool set from my day job. Not to mention it feels great when you actually get something to build and actually work.

So anyways here's a list of resources I've found over the years to be super helpful:

Things on Reddit

• r/Unity3D
  
  
• r/GameDev
  
  
• r/GameDesign
  
  
• r/Blender
  
  
• This comment on mathematic resources for games, by u/jozborn.
  
  Books
  
  
• C# in Depth. My favorite when it comes to C# knowledge. Written by one of the architects/designers of the C# language. (Companion site)
  
  
• The Art of Game Design: A Book of Lenses. Havent gotten too deep into this one, but I've been really liking it so far.
  
  
• Programming Game AI by Example. I've read quite a bit of this one, and it's been a great resource for AI stuff.
  
  
• Game Programming Patterns. This one is free if you read it online, but I suggest buying it. The examples are in C++, but the ideas can be applied in any language.
  
  
• Game Engine Architecture: 2^nd Edition. Probably something to look into down the line, but great to keep track of. Dives deep into the structure of a game engine, how it works, and some theory behind it. I've only gotten about half way, but it's my favorite book so far (for game dev).
  
  Videos
  
  
• Cooking with Unity. I went through basically all of these, and they helped me a lot when I was starting out.
  
  
• Pretty much anything this guy does is super great. I've used his videos a lot for ideas, inspiration, and tutorials.
  
  
• This guy is great too. Similar to the above in why I look to his works.
  
  
• This set of vids is for general C#, but he also has stuff for OpenGL. Plus I felt he has a great way of explaining and illustrating new ideas and concepts.
  
  Other Things
  
  
• Thu Unity3D Patch Notes. Great to see the new stuff the engine can do.
  
  
• An ton of (mostly) free books on github. General computer/programming stuff, but there is some good stuff in there for gamedev.
  
  
• This set of blog posts about memory/performance in Unity. Great general knowledge of things to watch out for.
  
  
• The Book of Shaders. More stuff for general graphics knowledge. Been looking through it a bit, and it's been really insightful.
  
  
  That's all I can think of right now. I know it's a lot, but hopefully you can take away at least one of these resources.
  
  Happy dev-ing!

I've posted this before but I'll repost it here:

Now in terms of the question that you ask in the title - this is what I recommend:

Job Interview Prep

1. Cracking the Coding Interview: 189 Programming Questions and Solutions
   
2. Programming Interviews Exposed: Coding Your Way Through the Interview
   
3. Introduction to Algorithms
   
4. The Algorithm Design Manual
   
5. Effective Java
   
6. Concurrent Programming in Java™: Design Principles and Pattern
   
7. Modern Operating Systems
   
8. Programming Pearls
   
9. Discrete Mathematics for Computer Scientists
   
   

   Junior Software Engineer Reading List
   

   
   

   Read This First
   

   
   

10. Pragmatic Thinking and Learning: Refactor Your Wetware
    
    

    Fundementals
    

    
    

11. Code Complete: A Practical Handbook of Software Construction
    
12. Software Estimation: Demystifying the Black Art
    
13. Software Engineering: A Practitioner's Approach
    
14. Refactoring: Improving the Design of Existing Code
    
15. Coder to Developer: Tools and Strategies for Delivering Your Software
    
16. Perfect Software: And Other Illusions about Testing
    
17. Getting Real: The Smarter, Faster, Easier Way to Build a Successful Web Application
    
    

    Understanding Professional Software Environments
    

    
    

18. Agile Software Development: The Cooperative Game
    
19. Software Project Survival Guide
    
20. The Best Software Writing I: Selected and Introduced by Joel Spolsky
    
21. Debugging the Development Process: Practical Strategies for Staying Focused, Hitting Ship Dates, and Building Solid Teams
    
22. Rapid Development: Taming Wild Software Schedules
    
23. Peopleware: Productive Projects and Teams
    
    

    Mentality
    

    
    

24. Slack: Getting Past Burnout, Busywork, and the Myth of Total Efficiency
    
25. Against Method
    
26. The Passionate Programmer: Creating a Remarkable Career in Software Development
    
    

    History
    

    
    

27. The Mythical Man-Month: Essays on Software Engineering
    
28. Computing Calamities: Lessons Learned from Products, Projects, and Companies That Failed
    
29. The Deadline: A Novel About Project Management
    
    

    Mid Level Software Engineer Reading List
    

    
    

    Read This First
    

    
    

30. Personal Development for Smart People: The Conscious Pursuit of Personal Growth
    
    

    Fundementals
    

    
    

31. The Clean Coder: A Code of Conduct for Professional Programmers
    
32. Clean Code: A Handbook of Agile Software Craftsmanship
    
33. Solid Code
    
34. Code Craft: The Practice of Writing Excellent Code
    
35. Software Craftsmanship: The New Imperative
    
36. Writing Solid Code
    
    

    Software Design
    

    
    

37. Head First Design Patterns: A Brain-Friendly Guide
    
38. Design Patterns: Elements of Reusable Object-Oriented Software
    
39. Domain-Driven Design: Tackling Complexity in the Heart of Software
    
40. Domain-Driven Design Distilled
    
41. Design Patterns Explained: A New Perspective on Object-Oriented Design
    
42. Design Patterns in C# - Even though this is specific to C# the pattern can be used in any OO language.
    
43. Refactoring to Patterns
    
    

    Software Engineering Skill Sets
    

    
    

44. Building Microservices: Designing Fine-Grained Systems
    
45. Software Factories: Assembling Applications with Patterns, Models, Frameworks, and Tools
    
46. NoEstimates: How To Measure Project Progress Without Estimating
    
47. Object-Oriented Software Construction
    
48. The Art of Software Testing
    
49. Release It!: Design and Deploy Production-Ready Software
    
50. Working Effectively with Legacy Code
    
51. Test Driven Development: By Example
    
    

    Databases
    

    
    

52. Database System Concepts
    
53. Database Management Systems
    
54. Foundation for Object / Relational Databases: The Third Manifesto
    
55. Refactoring Databases: Evolutionary Database Design
    
56. Data Access Patterns: Database Interactions in Object-Oriented Applications
    
    

    User Experience
    

    
    

57. Don't Make Me Think: A Common Sense Approach to Web Usability
    
58. The Design of Everyday Things
    
59. Programming Collective Intelligence: Building Smart Web 2.0 Applications
    
60. User Interface Design for Programmers
    
61. GUI Bloopers 2.0: Common User Interface Design Don'ts and Dos
    
    

    Mentality
    

    
    

62. The Productive Programmer
    
63. Extreme Programming Explained: Embrace Change
    
64. Coders at Work: Reflections on the Craft of Programming
    
65. Facts and Fallacies of Software Engineering
    
    

    History
    

    
    

66. Dreaming in Code: Two Dozen Programmers, Three Years, 4,732 Bugs, and One Quest for Transcendent Software
    
67. New Turning Omnibus: 66 Excursions in Computer Science
    
68. Hacker's Delight
    
69. The Alchemist
    
70. Masterminds of Programming: Conversations with the Creators of Major Programming Languages
    
71. The Information: A History, A Theory, A Flood
    
    

    Specialist Skills
    

    
    In spite of the fact that many of these won't apply to your specific job I still recommend reading them for the insight, they'll give you into programming language and technology design.
    
    

72. Peter Norton's Assembly Language Book for the IBM PC
    
73. Expert C Programming: Deep C Secrets
    
74. Enough Rope to Shoot Yourself in the Foot: Rules for C and C++ Programming
    
75. The C++ Programming Language
    
76. Effective C++: 55 Specific Ways to Improve Your Programs and Designs
    
77. More Effective C++: 35 New Ways to Improve Your Programs and Designs
    
78. More Effective C#: 50 Specific Ways to Improve Your C#
    
79. CLR via C#
    
80. Mr. Bunny's Big Cup o' Java
    
81. Thinking in Java
    
82. JUnit in Action
    
83. Functional Programming in Scala
    
84. The Art of Prolog: Advanced Programming Techniques
    
85. The Craft of Prolog
    
86. Programming Perl: Unmatched Power for Text Processing and Scripting
    
87. Dive into Python 3
    
88. why's (poignant) guide to Ruby

Some CL-specific resources:

• The book Land of Lisp has some sections specifically on functional programming, and answers some of these questions. It goes into more detail on the philosophy and spirit of separating effects and organizing code, albeit for a limited example. Chapter 14 introduces it (in the context of CL), then implements the core of the game Dice of Doom in a functional style in chapter 15.
  
  
• On Lisp discusses programming in the functional style early on in Ch2/3, (with an emphasis on bottom-up programming). I think Graham uses a functional style more-or-less throughout, except for performance optimizations or where the imperative imperative implementation is actually more desirable for clarity.
  
  
• Peter Norvig similarly leverages a bit of a functional style throughout PAIP, and he has several remarks about leveraging higher order functions, recursion, and small, composeable functions throughout the text. FP isn't the focus, but it's discussed and present.
  
  
• Practical Common Lisp has some brief mentions and examples in chapters 5 and 12.
  
  Non-CL:
  
  
• SICP starts off with functional programming from the start. Although it's scheme, the ideas are similarly portable to CL. It's an excellent resource in general, regardless of language interest IMO.
  
  
• There's a chapter in the free Clojure For the Brave and True that more-or-less covers the bases and builds a small game functionally. Due to its prevalence, you pretty much find articles/blogs/chapters on FP in every clojure resource. I found the ideas generally portable when revisiting CL (absent reliance on persistent structures with better performance profiles than lists and balanced binary trees).
  
  
• Joy of Clojure Ch7 specifically focuses on a FP concepts and applies them to implement a functional version of A* search. They run through simple functions, function composition, partial function application, functions as data, higher order functions, pure functions / referential transparency, closures, recursive thinking, combining recursion with laziness, tail calls, trampolines, and continuation passing style.
  
  Others:
  
  
• http://learnyouahaskell.com/chapters
  
  I flip back and forth between Clojure and CL periodically (CL is for hobbies, clojure is for work and hobbies), and have mucked with scheme and racket a bit (as well as decent mileage in F#, Haskell, and a little Ocaml from the static typed family). IME, you can definitely tell the difference between a language with support for FP strapped on after the fact, vs. one with it as a core design (preferably with mutable/imperative escape hatches). CL supports FP (closures/functions are values (e.g. lambda), there's a built-in library of non-destructive pure functions that typically operate on lists - or the non-extensible sequence class, and non-standard but general support for optimizing tail recursive functions into iterative ones enables pervasive use of recursion in lieu of iteration), but I think it's less of a default in the wild (not as unfriendly as Python is to FP though). Consequently, it's one paradigm of many that show up; I think there's likely as much if not more imperative/CLOS OOP stuff out there though. I think the alternate tact in clojure, scheme, and racket is to push FP as the default and optimize the language for that as the base case - with pragmatic alternative paradigm support based on the user's desire. Clojure takes it a step farther by introducing efficient functional data structures (based on HAMTs primarily, with less-used balanced binary trees for sorted maps and sets) so you can push significantly farther without dropping down to mutable/imperative stuff for performance reasons (as opposed to living and dying by the performance profiles of balanced binary trees for everything). You'll still find OOP and imperative support, replete with mutation and effects, but it's something to opt into.
  
  In the context of other FP langs, F# and Ocaml do this as well - they provide a pretty rigorous locked-down immutable approach with functional purity as the default, but they admit low-hanging means to bypass the purity should the programmer need to. Haskell kinda goes there but it's a bit more involved to tap into the mutable "escape hatches" by design.
  
  In the end, you can pretty much bring FP concepts into most any languages (e.g. write in a functional style), although it's harder to do so in languages that don't have functions/closures as a first class concept (to include passing them as values). Many functional languages have similar libraries and idioms for messing with persistent lists or lazy sequences as a basic idiom; that's good news since all those ideas and idioms or more or less portable directly to CL (and as mentioned here are likely extant libraries to try to bring these around in addition to the standard map,filter, reduce built-ins). For more focused FP examples and thinking, clojure, racket, and scheme are good bets (absent an unknown resource that exclusively focuses on FP in CL, which would seem ideal for your original query). I think dipping into the statically typed languages would also be edifying, since there are plenty of books and resources in that realm.

If you really want to think this through, I recommend Superintelligence.

How

Imagine that you are held in a prison run only by five year old children. Five year old jailers come by to feed you, guard you, and watch your cell. Do you think you could escape?

Of course you could escape this prison - five year olds aren't that smart. You could likely instruct them to release you with a stern voice and they would let you out. You could scold them for keeping you, make them feel guilty, you could threaten them, you could make them like you, you could offer them things if they released you, you could trick them into leaving the door unlocked, you file the bars while they weren't looking, and so on - you'd have endless opportunities. The five year olds would have to thwart your every effort to keep you successfully locked up while you would only have to find a single instance of carelessness or an exploitable mistake on the part of the five year olds.

The point of the metaphor is that, as you are more intelligent than children, so too could an advanced AI be more intelligent than you or humans generally. We can't easily imagine exactly what the AI would do in any specific scenario, because we lack that intelligence, but we can understand the relationship between more intelligent and less intelligent beings is such that the more intelligent, especially the much more intelligent, can usually come out on top against their less intelligent competitors.

When

Think about the difference between the village idiot and noted non-idiot Albert Einstein. This is a vast intelligence difference - but actually, only kind of. Einstein and the idiot have roughly the same brains - it's not like Einstein's brain had an extra lobe, or extra structure that other humans lack. Morphologically, they are very similar. Like Einstein, the idiot can walk, run, talk, read, throw a ball, love, fear, and do all these things that humans can do.

If you put Einstein and the idiot on a spectrum of intelligence that included something like a mouse, you'd find that there is a vast gulf between mice and the idiot, and only a short distance between Einstein and the idiot. Whereas the idiot has a much larger brain, different neural structures and densities compared to the mouse, and can do lots the mouse can't even conceive of, the differences between the idiot and Einstein are more modest.

It's important to understand this point about the intelligence spectrum because it will help you keep things in perspective. If you're observing the intelligence gains of modern AI, and trying to place it on a spectrum, then you must notice that AI is far dumber than even dumb humans at the moment for the moment. However - two observations are important. First, that AI is steadily moving along this spectrum whereas the intelligence of humans is relatively fixed. Consider all the new things that AI has been able to do in your life time, compared to the new things that humans have been able to do. Second, the moment AI overtakes the dumbest human on the intelligence spectrum, you may think it's time to start worrying - but actually, if AI reaches that level it will already be almost super intelligent - a tiny additional movement along the intelligence spectrum will cary its intelligence beyond the range of humanity, and then we will be in the situation with the five year old run prison, only we will be the children trying to contain an entity which will be more intelligent than we will be able to comprehend.

Plausible scenario

China is investing heavily in AI - so are Silicon Valley companies. Imagine a Silicon Valley company gets something like a general intelligence.

Now, because this is Silicon Valley, obviously nothing can go wrong. So lets grant that there are no bugs, the friendliness of the AI has been well thought out, the AI knows how to understand what humans mean - not just the literal meaning of your words, but what you actually mean, and the AI is perfectly obedient. Of course, in reality, none of this is granted, or even likely, but let's just say it is.

First order of business? Let's ask the AI to improve itself as much as possible. Assign it to work on its own code, get it running on a server farm, heck, maybe we'll even have it design its own hardware. Pretty soon we've turned our general intelligence into a superintelligence.

What's next? Why not make ourselves rich? The AI can produce things that intelligence can produce. It can make movies, TV shows, computer software, argue legal cases, parse documents, provide analytics, and on and on.

Great, now we have all the money and entertainment we could ever want. How about power? Well, autonomous drones and weapon systems already exist. How about some designed and operated by our superintelligence?

Okay, now our Silicon Valley entrepreneur is king of the world, because he has a limitless, super intelligent, perfectly obedient, robot army operated by a superintelligence. Oh, and because he has command of superintelligence, he has perfect medicine and is biologically immortal too - he can reign forever.

What if the superintelligence isn't controlled by Silicon Valley, but by totalitarian China? What if the person or people running it are sadists? What if there is a "bug" in the parts of the code that control obedience, preferences, or comprehension of humans?

Book suggestions? Now that's my jam.

Out of all the books i've read, here are my recommendations regarding game programming:

Eric Lengyel's Books (only one out so far). This is aimed at game engine development, but if the 2nd onward are as indepth as the first, they will be amazing fundamental knowledge. Also, they're not thick, and jam packed with information.

Game Programming Patterns. The only book that comes more recommended than this is the one right below it by Jesse Schell. This book is fantastic, but you should write one or two small games to really get the most out of this book. You can also read it online on his website free, but then you don't get a pic of him and his dog on the back cover.

Book of Lenses. This is your intro/intermediate dive into game design. There are a lot of game design books, if you only read one, it should be this one.

Gane AI By Example. This book is a hodgepodge of fantastic techniques and patterns by those in AAA. There are other books on the series (like Game AI Pro) which are similar, but in my opinion (at least when I read AI PRO 3), they're not as good. But more knowledge is never bad.

Truthfully, as I sit here looking over all my books, those are the only ones i'd consider mandatory for any seasoned developer. Of course plenty of developers get by without reading these books, but they likely pick up all the principles listed herein elsewhere, in bits and pieces, and would likely have benefited having read them early on.

Here are a few others that I do recommend but do NOT consider mandatory. Sorry, no links.

Unity in Action. Personally, I recommend this or a more interactive online course version (udemy.com/unitycourse) if you want to learn unity while having a resource hold your hand. Having read the book, taken the course, AND taken Unity's own tutorials on the matter, i'd order them in order from Course being best, book second, videos from unity third. But none of them are bad.

Game Engine Architecture. This is the king for those who want a very broad introduction to making a game engine. It comes highly recommended from nearly anyone who reads it, just so long as you understand it's from a AAA point of view. Game Code Complete is out of print and unlikely to be revisited, but it is similar. These are behemoths of books.

Realtime rendering. This is one I haven't read, but it comes very highly recommended. It is not an intro book, and is also over 1000 pages, so you want this along side a more introductory book like Fundamentals of computer graphics. Truth be told, both books are used in courses in university at the third and fourth year levels, so keep that in mind before diving in.

Clean code. Yeah yeah it has a java expectation, but I love it. It's small. Read it if you understand Java, and want to listen to one of the biggest preachers on how not to write spaghetti code.

Rimworld guy, Tynaan sylvester I believe, wrote a book called Designing Games. I enjoyed it, but IMO it doesn't hold a candle to Jesse Schell's book. Either way, the guy did write that book after working in AAA for many years, then went on to create one of the most successful sim games in years. But yeah, I enjoyed it.

Last but not least, here are some almost ENTIRELY USELESS but interesting diagrams of what some people think you should read or learn in our field:

https://github.com/miloyip/game-programmer

https://github.com/utilForever/game-developer-roadmap

https://github.com/P1xt/p1xt-guides/blob/master/game-programming.md

First of all, thanks for sharing. Code & idea implementation sucks, but it might turn into a very interesting discussion! By admitting that your trade idea is far from being unique and brilliant, you make a super important step in learning. This forum needs more posts like that, and I encourage people to provide feedback!

Idea itself is decent, but your code does not implement it:

• You want to holds stocks that are going up, right? Well, imagine a stock above 100ma, 50ma, 20ma, but below 20ma and 10ma. It is just starting to turn down. According to your code, this stock is labeled as a 'rising stock', which is wrong.
  
  
• SMAs are generally not cool. Not cool due to lag of 1/2 of MA period.
  
  
• Think of other ways to implement your idea of gauging "going up stocks". Try to define what is a "stock that is going up".
  
  
• Overbought/oversold part. This part is worse. You heard that "RSI measures overbought/oversold", so you plug it in. You have to define "Overbought/oversold" first, then check if RSI implements your idea of overbought/oversold best, then include it.
  
  
• Since you did not define "overbought / oversold", and check whether RSI is good for it, you decided to throw a couple more indicators on top, just to be sure =) That is a bad idea. Mindlessly introducing more indicators does not improve your strategy, but it does greatly increase overfit.
  
  
• Labeling "Sell / Neutral / Buy " part. It is getting worse =)) How did you decide what thresholds to use for the labels? Why does ma_count and oscCount with a threshold of 0 is the best way to label? You are losing your initial idea!
  Just because 0 looks good, you decide that 0 is the best threshold. You have to do a research here. You'd be surprised by how counter intuitive the result might be, or how super unstable it might be=))
  
  
• Last but not least. Pls count the number of parameters. MAs, RSI, OSC, BBand + thresholds for RSI, OSC + Label thresholds ... I don't want to count, but I am sure it is well above 10 (maybe 15+?). Now even if you test at least 6-7 combinations of your parameters, your parameter space will be 10k+ of possible combinations. And that is just for a simple strategy.
  
  
• With 10k+ combinations on a daily data, I can overfit to a perfect straight line pnl. There is no way with so many degrees of freedom to tell if you overfit or not. Even on a 1min data!
  
  The lesson is: idea first. Define it well. Then try to pick minimal number of indicators (or functions) that implement it. Check for parameter space. If you have too many parameters, discard your idea, since you will not be able to tell if it is making/losing money because it has an edge or just purely by chance!
  
  What is left out of this discussion: cross validation and picking best parameters going forward
  
  Recommended reading:
  
• https://www.amazon.com/Building-Winning-Algorithmic-Trading-Systems/dp/1118778987/
  
• https://www.amazon.com/Elements-Statistical-Learning-Prediction-Statistics/dp/0387848576/

> Fast forward into the future... our system speaks very well. Fluent English. There is no self. There is no awareness. But it's an incredibly effective chatbot. The best ever created. It learned from scratch. Naturally. No programming involved, other than the basic conditions for the neural network to start developing.

You got your order backwards. That's impossible without context / experience to go with it. You can say, "Oh, well it's taking in a bunch of data, that's it's context." but at that point it is identifying self and other already to build context.

Like a baby, it identifies self before it can speak well.

Have you read any of Douglas Hofstadter's work? You would really like his writing. It's all about this sort of stuff. His least popular book (that I wouldn't recommend as a starting read) is Le Ton beau de Marot which explains the difficulty of translating language without context, and is surprisingly relevant to the struggles of Google Translate and the like.

>Gradually it starts developing a self. How wouldn't this happen? Learning how to speak is a process tightly related to learning how to think. Can you imagine thinking without language? Can you imagine fully-fledged human-like communication without some basic underlying thinking? Can you imagine being in love without language? Can you imagine getting attached to your girlfriend, or worrying about death, without language? Take a moment to picture that. A language-less mind.

Yes, I do it all the time, though depending on what you call language. Most of my thoughts are not linguistic which imho is probably why I struggle with English so much.

I've also written AI that pattern matches visual information, like charts of data, mostly for the stock market. So actually, yes, I do know exactly what you mean.

>More complex hierarchies were built. That is how human thinking emerges. Slowly, over the years. Developing such a deep level of understanding of language that it can encode complex thoughts and emotions.

Yes abstractions and recursion. It's not that complicated.

>Back to our system. It's proficient in a language, not hard-coded; but self-taught. It developed not only great grammar and vocabulary, but great communication skills.

I have horrible communication skills. Can you teach me how to be a better communicator?

>You think you can encode emotions without language? Well, sure. But I bet you've never thought of the concept of "Doing your very best effort, to the point of challenging your very own mental and physical limits, going beyond what you thought you were capable of, to push yourself forward and improve" in one single word. You might have thought about this, but... in a single word? Well... you have, if you speak Finnish and know the word "Sisu". Non-Finnish-Speakers lack this. The same applies to tons of languages.

Ahh, but did you ever think about boiling that concept down into a single word itself? (The concept of boiling down concepts.)

There is this thing called Domain-Driven Design in the software engineer architecture world. One of the key aspects of it is called "Ubiquitous Language". It's kind of the heart of business terminology (though technically incorrect from a historical perspective). The idea is you make up a word that represents a concept and then casually use it in the work place in such a way that the sales people, managers, and engineers use it. This way casually everyone syncs up to the same terminology bridging communication between individuals of different backgrounds.

Sadly, ubiquitous language is often used as an ego boost by senior engineers to maintain seniority. Please, if you ever use this technology, explain how it works to the juniors clearly, so they are kept within the loop. Too often seniors will use it as a way to choose who is in the loop, so please "with power comes responsibility". We could use more kindness in the engineering discipline, or at least out here in silicon valley.

>This system I've described in this whole post is actually a human being.

Pretty awesome write up.

You're pretty smart. Are you on hacker news by any chance? Or and totally checkout GEB, you'll like it.

Rasputin did give you good advice. But just so you know, there's an /r/mathbooks. It includes a textbook on set theory. Though, don't be intimidated if it's too advanced for you.

I'd recommend some of the more popular math books as well.

Flatterland is a fun, quirky adventure through some advanced geometric concepts. Very readable.

Euler's Gem is a book I've never read, but might some day. I thumbed through it, and it seemed like a good enough summary of topology for a laymen. The amazon reviews agree. Be warned: there are equations. But you're trying to discover the beauty of math, so equations are probably good!

Goedel Escher and Bach is . . . Famous. I'll leave it at that. I thought it was a good and simple, but it's too close to my primary area of interest for me to recommend in good faith.

That's about all of my experience with popular math books . . .

Euclid's elements (you can google it and find it online) is great for an introduction to mathematical proof using something highly diagramable (unlike set theory). But, I would definitely scope out my interests before putting down any money. Perhaps check out the library. And don't get intimidated if anything is over your head. When that happens to me, I tend to get a little excited in all honesty :).

Physicist here so don't pretend I don't know what science is. (Though like the ancient Pythagoreans I'm sure as soon as I discuss something that has been proven that goes against a purely scientific worldview out comes the pitchforks.) And though I love science, unlike some people here I am willing to admit to the limits of science. Science can lead to all truth in the same way that rational numbers define all numbers: it can't! and Godel proved it.

The real problem with science is that it has been mathematically proven by Godel that there are more things that are true then are provable and thus you can't ever have a scientific theory that can determine the truth or falsity of all things. As soon as you write down that theory, assuming it allows for arithmetic, Godel's incompleteness theorem immediately shows if the theory is true there will be true statements about reality that are beyond provability. Read Godel Esher Bach or Incompleteness or work through it yourself in this textbook as I have.

So like I said above, science is great in it's sphere (and in that sphere let me emphasize it is awesome!) but leads to all truth in the same way that rational numbers leads to all numbers. (And the analogy is precise since Godel used the famous diagonizational argument in his proof.) Russell and Whitehead set out to show in the early 1900s that if we could determine the axioms of reality then through logic work out everything that was true and Godel spoiled the party.

It it would be one thing if these truths were trivial things, but they are not. Some examples of true or false statements that may fall into this category of being unprovable are:

• Goldbach's conjecture and an uncountable number of mathematical theorems (by the diagonalization argument) for that matter.. (Search the pdf for Goldbach)
  
  
• Issues related to the halting problem in computer science.
  
  
• Issues related to recursive logic and artificial intelligence.
  
  
• And again, this list goes on uncountably.
  
  Now, at this point critics almost always tell me: but Joe, Godel's incompleteness theorem is only relative to your set of logic. (Ie... we can prove Goldbach by just adding axioms needed to do so.) Fine. But two things: (first) adding axioms to prove what you want willy nilly is not good science. (Two) You now have a new set of axioms and by Godel's theorem there is now a new uncountable set of things that are true (and non-trivial things like I listed) that are beyond proof.
  
  Now usually comes the second critique: But Joe, this doesn't prove God exists. And this is true. But at least it has been proven God gives you a chance. It has been proven that an oracle machine is free from the problems that hold science and logic back from proving the truth of all things. At least something like God gives you a chance (whereas science falls short).
  
  Or, like Elder Maxwell says so well: it may only be by the "lens of faith" that we can ever know the truth of all things. He maybe be right, and hence the importance to learn by study, and also by faith...

You have to be above the bar to begin with. If you can understand exactly what intelligence is then you can increase it.

Meditation can be used as a way to gain insight. This is not all types of meditation, but there are definitely types of meditation with the goal of enlightenment in mind. Using the Buddhist definition of enlightenment and overly simplified explanation is insight, specifically the type of lower level type of insight that not everyone can get to and for the most part needs to be unlocked. Once it is unlocked, how one utilizes it can be a large intelligence booster, but you have to be able to comprehend how your mind works. If you can't fully recognize a lot of advanced and abstract concepts then knowledge gain is possible but hardly any intelligence gain.

Using the example you mention, math is utilized on the other part of the brain in such a way that you can multitask while solving advanced math problems. A way this can be figured out is solving math problems in your sleep. It is like a piece of your brain is a math coprocessor and it can chug along while you are talking to someone, reading writing, sleeping, or generally not paying attention to it, much like cooking something in the oven.

It depends what you want to learn. The most direct path is raw insight. For advanced logic, paradoxes, and other mathy nerdy stuff you might want to checkout GEB. Meditation doesn't skip the learning step. You still have to learn things the same way everyone else does. Meditation just helps you realize you can utilize your brain to a more full potential.

If you are really interested and think you can can push forward, I highly recommend you try a 300µg+ dose of lsd. Tripping is the same thing as a deep meditation state, but it doesn't stay. It is like driving a car over the mountain instead of walking. In a deep state under the influence you can do all of the more insightful things one can do in a deep meditative headspace. However, figuring it out could take multiple trips as sometimes insight will take 6 hours to come full circle. When meditating in a deep headspace the answer can come much quicker.

The idea is if you can figure it out while tripping, then you can remember what you've learned and migrate it into meditative practices, as it can literally take a life time to get to the level of meditation skill as one night of dropping acid will bring you to.

It is definitely possible. If you don't ask very specific detailed questions about how your brain works, I will not be able to explain in detail, and without asking yourself you can't move towards figuring things out either.

An efficient way to get to a deep headspace from meditation is a map, so you have an idea of which direction to go in. This tends to be pretty good.

Computer scientist here... I'm not a "real" mathematician but I do have a good bit of education and practical experience with some specific fields of like probability, information theory, statistics, logic, combinatorics, and set theory. The vast majority of mathematics, though, I'm only interested in as a hobby. I've never gone much beyond calculus in the standard track of math education, so I to enjoy reading "layman's terms" material about math. Here's some stuff I've enjoyed.

Fermat's Enigma This book covers the history of a famous problem that looks very simple, yet it took several hundred years to resolve. In so doing it gives layman's terms overviews of many mathematical concepts in a manner very similar to jfredett here. It's very readable, and for me at least, it also made the study of mathematics feel even more like an exciting search for beautiful, profound truth.

Logicomix: An Epic Search for Truth I've been told this book contains some inaccuracies, but I'm including it because I think it's such a cool idea. It's a graphic novelization (seriously, a graphic novel about a logician) of the life of Bertrand Russell, who was deeply involved in some of the last great ideas before Godel's Incompleteness Theorem came along and changed everything. This isn't as much about the math as it is about the people, but I still found it enjoyable when I read it a few years ago, and it helped spark my own interest in mathematics.

Lots of people also love Godel Escher Bach. I haven't read it yet so I can't really comment on it, but it seems to be a common element of everybody's favorite books about math.

I am not totally sure what you are asking for actually exists in book form...which is odd, now that I think about it.

If it were me, I would think about magazines instead. And if you really want to push him, think about the following options:

1. Science News, which is very similar to the front-matter of the leading scientific journal Science. This includes news from the past month, and some in-depth articles. It is much better written -- and written at a much higher level -- than Scientific American or Discover. For a very intelligent (and science-interested) high school student, this should pose little difficulty.
   
2. The actual journal Science. This is weekly, which is nice. In addition to the news sections, this also includes editorials and actual science papers. While many of the actual papers will be beyond your son, he can still see what passes for presentation of data in the sciences, and that is cool.
   
3. The actual journal Nature. This is also weekly, and is the british version of the journal Science. In my opinion, the news section is better written than Science, which is important as this is where your kid's reading will be mostly done. IN addition, Nature always has sections on careers and education, so that your son will be exposed to the more human elements of science. Finally, the end of nature always has a 1-page sci-fi story, and that is fun as well.
   
4. If you must, you could try Scientific American or Discover, but if you really want to give your kid a cool gift, that is a challenge, go for one of the top three here. I would highly recommend Nature.
   
   If you insist on books...
   
   I see you already mentioned A Brief History of the Universe, which is an excellent book. However, I am not sure if you are going to get something that is more "in depth." Much of the "in depth" stuff is going to be pretty pop, without the rigorous foundation that are usually found in textbooks.
   
   If I had to recommend some books, here is what I would say:
   
   
5. The selfish gene is one of the best "rigorous" pop-science books out there. Dawkins doesn't really go into the math, but other than that he doesn't shy away from the implications of the work.
   
6. Darwin's Dangerous Idea by Dennett is a great book. While not strictly science, per se, it does outline good philosophical foundations for evolution. It is a dense read, but good.
   
7. On the more mathematical side, you might try Godel, Escher, Bach, which is a book that explores the ramifications of recrusiveness and is an excellent (if dense) read.
   
8. You could also consider books on the history of science -- which elucidate the importance of politics and people in the sciences. I would recommend any of the following: The Double Helix, A man on the moon, The making of the atomic bomb, Prometheans in the lab, The alchemy of air, or A most damnable invention. There are many others, but these came to mind first.
   
   Hope that helps! OH AND GO WITH THE SUBSCRIPTION TO NATURE
   
   edit: added the linksssss

I've posted a similar answer before, but can't find the comment anymore.

If you are interested in doing your own statistics and modeling (like regression modeling), learn R. It pays amazing dividends for anyone who does any sort of data analysis, even basic biostats. Excel is for accountants and is terrible for biological data. It screws up your datasets when you open them, has no version control/tracking, has only rudimentary visualization capabilities, and cannot do the kind of stats you need to use the most (like right-censored data for Cox proportional hazards models or Kaplan-Meier curves). I've used SAS, Stata, SPSS, Excel, and a whole bunch of other junk in various classes and various projects over the years, and now use only R, Python, and Unix/Shell with nearly all the statistical work being in R. I'm definitely a biased recommender, because what started off as just a way to make a quick survival curve that I couldn't do in Excel as a medical student led me down a rabbit hole and now my whole career is based on data analysis. That said, my entire fellowship cohort now at least dabbles in R for making figures and doing basic statistics, so it's not just me.

R is free, has an amazing online community, and is in heavy use by biostatisticians. The biggest downsides are

• R is actually a strange and unpopular general programming language (Python is far superior for writing actual programs)
  
• It has a steep initial learning curve (though once you get the basics it is very easy to learn advanced techniques).
  
  Unfortunately learning R won't teach you actual statistics.... for that I've had the best luck with brick-and-mortar classes throughout med school and later fellowship but many, many MOOCs, textbooks, and online workshops exist to teach you the basics.
  
  If I were doing it all over again from the start, I would take a course or use a textbook that integrated R from the very beginning such as this.
  
  Some other great statistical textbooks:
  
  
• Introduction to Statistical Learning -- free legal PDF here -- I can't recommend this book enough
  
• Elements of Statistical Learning -- A masterpiece of machine learning and modeling. I can't pretend to understand this whole book, but it is a frequent reference and aspirational read.
  
  Online classes:
  So many to choose from, but I am partial to DataCamp
  
  Want to get started?
  
  
• Download R directly from its host, CRAN
  
• Download RStudio (an integrated development environment for R that makes life infinitely easier) from its website (also free)
  
• Fire up RStudio and type the following commands after the > prompt in the console:
  
  install.packages("swirl")

library("swirl")

swirl()
  
  And you'll be off an running in a built-in tutorial that starts with the basics (how do I add two numbers) and ends (last I checked) with linear regression models.
  
  ALL OF THAT SAID ------
  
  You don't need to do any of that to be a good doctor, or even a good researcher. All academic institutions have dedicated statisticians (I still work with them all the time -- I know enough to know I don't really know what I am doing). If you can do your own data analysis though, you can work much faster and do many more interesting things than if you have to pay by the hour for someone to make basic figures for you.

As one of the people who commented on that thread, I feel the need to respond to this as rationally as humanly possible.

For starters, let's clear up the difference between fractal mathematics, fractal woo, and what Douglas Hofstadter might call fractal analogy.

1. From the wiki - Fractal Mathematics would be the study of "natural phenomena or a mathematical sets that exhibits repeating patterns that display at every scale" as well as the study of self similarity and iterated functions. While it has grown complex and vast, the studies of fractals and their geometry started out as literally what you say it isn't: people asking questions about self-similarity in nature and asking how to describe it mathematically.
   
   
2. Fractal Woo would be, as OP said:
   
   >“Everything big is just like everything small!” they exclaim, “the universe is self-similar!”
   
   ...and then using such logic to thereby justify whatever silly energy-Reiki-mystical-connectedness-telepathy-de-jour they want.
   
   
3. Fractal Analogy (my term, but run with it) would be seeing patterns in the world which are, indeed, self similar, as tons of stuff in nature is. This includes plant and animal system, as well as consciousness and human experience. The reason I mention Douglas Hofstadter is that he is a PhD physicist who literally used fractal mathematics to predict some pretty nifty real world stuff 35 years before it was confirmed - but Mr. Hofstadter is also an incredibly enjoyable author who muses at length about cognitive science and AI research, often using the analogy of self-similar shapes to help describe what we understand of consciousness in a way that most layman readers can understand. Even if you are not a very capable mathematician, I highly recommend his Godel Escher Bach, which uses fractals and loads of other creative stuff to help conceptualize how the "mind" arises from the brain.
   
   As well, Chaos Theory - the study of how immensely complex patterns emerge from seemingly simple preconditions - is full of fractal mathematics. Given that the universe is absolutely packed with iterated functions and self-similarity almost everywhere we look, I think you can absolutely take the point of view that the universe is fractal in nature, especially when you are in a self-induced state where your brain makes a lot of connections you might normally overlook or not even bother to think about.
   
   My point is that discussing things in the universe as self-similar is useful to mathematicians and non-mathematicians alike; using the word "fractal" to describe natural systems that exhibit those familiar patterns might not be perfectly correct, but it's not itself offensive or an affront to reasonable discourse. I manage a business; so what's your problem if I visualize the structure of my company as a fern leaf with departments and employees as branches off the main stem? What would be the issues of discussing how incredible human cellular morphology really is with my biologist roommate, and citing some cool research someone decided to do about fractal geometry in the way our bodies build themselves?
   
   EDIT: OP's edit makes it more clear his statements were more about irrational folk seeing the universe as a single continuous fractal (that would be the "fractal woo"), and that he is not denying the existence of fractal-like patterns in nature, or that using fractal models can be useful in understanding phenomena. Sorry for any confusion and thanks for the discussion!
   
   EDIT2: /u/ombortron commented pretty well in regards to the utility of the concept of fractals in scientific discourse and otherwise:
   
   >The universe itself doesn't have to be a fractal for fractals to be important.
   
   >Fractals are quite common in our reality, and as a result, that means they are an important facet of reality, and as such they are a legitimate and common topic of discussion amongst people, and this is particularly true of people who do psychedelics.
   
   >Does this mean the universe is 100% fractal in nature? No.
   
   

I did this, but I came to data science in the final year of my PhD when I got a job at a startup. I started with R, then SQL, then Python. I currently work in data science, moving internal ML products into production settings. I also do research - and knowing how to conduct proper trials is great if the company you work for gives you freedom in how something you've built is rolled out. I can also blend my degree with ML, e.g. designing batteries of questions to identify 'good fit' candidates for a given role - I combine the battery results with future performance data and continually refine the question set / improve the model. As well, I'm a good fit for UX and dabble in that. The combo skillset will give you the ability to produce value in many different ways.

The things that helped me most were:

• Early on, Programming for Everybody - very gentle intro, and well taught.
  
  
• Andrew Ng's machine learning course.
  
• SQLzoo.
  
• The Introduction to Statistical Learning course and book then, later, The Elements of Statistical Learning.
  
• Buying big fat books about the things I wanted to learn, and working through them (e.g., Probabilistic Graphical Models, Pattern Recognition).
  
• Coding algorithms from scratch, starting with linear regression and working my way to DNNs and RNNs. Do it in R, then Python, then Scala if you're ambitious.
  
• Doing the Kaggle intro competitions in R and then translating to Python - Titanic, census dataset, etc, and using a variety of approaches for each (i.e. xgboost, sklearn, tensorflow).
  
  It can be overwhelming, but don't worry. Do one course to completion, with that as your only goal. Then do the next. Then work on a kaggle thing. Then work through a book. One thing at a time - you might get anxious or be uncertain or want to do multiple things at once, but just start with one thing and focus on that and that alone. You'll get where you want to go.
  
  I also brushed up on my linear algebra / probability using MITs open courses and khanacademy.
  
  Beyond all this, I found that learning a lot about ML/AI really expanded my thinking about how human beings work and gave me a new and better lens through which to view behaviour and psych research/theories. Very much recommend to all psychologists.
  
  Good luck!

some amazing books I would suggest to you are:

• Godel Escher Bach
  
  
• Road to Reality By Roger Penrose.
  
  
• Code by
  Charles Petzold.
  
  
• Pi in the Sky by John Barrow.
  
  All of these I would love to read again, if I had the time, but none more so than Godel, Escher, Bach, which is one of the most beautiful books I have ever come across.
  
  Road to Reality is the most technical of these books, but gives a really clear outline of how mathematics is used to describe reality (in the sense of physics).
  
  Code, basically, teaches you how you could build a computer (minus, you know, all the engineering. But that's trivial surely? :) ). The last chapter on operating systems is pretty dated now but the rest of it is great.
  
  Pi in the Sky is more of a casual read about the philosophy of mathematics. But its very well written, good night time reading!
  
  You have a really good opportunity to get an intuitive understanding of the heart of mathematics, which even at a college level is somewhat glossed over, in my experience. Use it!

There are two books I recommend to everyone who is frustrated and/or saddened by the state of the world and has lost hope for a better future.

The first is The Better Angels of Our Nature by Stephen Pinker. It lays out how violence in human societies has been decreasing for centuries and is still declining.

Despite the prevalence of war and crime in our media, human beings are less likely to suffer violence today than at any point in our prior history. The west suffered an upswing in social violence from the 1970s -1990s, which has since been linked to lead levels, but violence in the west has been declining since the early 90s.

Put simply the world is a better place than most media coverage would have you believe and it's getting better year by year.

The second book I recomend is The Singularity is Near by Ray Kurzweil. It explains how technology has been improving at an accelerating rate.

Technological advances have already had major positive impacts on society, and those effects will become increasingly powerful over the next few decades. Artificial intelligence is already revolutionizing our economy. The average human life span is increasing every year. Advances in medicine are offering hope for previously untreatable diseases.

Basically, there is a lot of good tech coming which will significantly improve our quality of life, if we can just hang on long enough.

Between those two forces, decreasing violence and rapidly advancing technology, the future looks pretty bright for humanity. We just don't hear that message often, because doom-saying gets better ratings.

I don't know what disability you're struggling with but most people have some marketable skills, i.e. they aren't "worthless". Based on your post, you clearly have good writing/communicating skills. That's a rare and valuable trait. You could look into a career leveraging those skills (e.g. as a technical writer or transcriptionist) which your disability wouldn't interfere with to badly (or which an employer would be willing to accommodate).

As for being powerless to change the world, many people feel that way because most of us are fairly powerless on an individual level. We are all in the grip of powerful forces (social, political, historical, environmental, etc.) which exert far more influence over our lives than our own desires and dreams.

The books I recommended post convincing arguments that those forces have us on a positive trend line, so a little optimism is not unreasonable. We may just be dust on the wind, but the wind is blowing in the right direction. That means the best move may simply be to relax and enjoy the ride as best we can.

Escher's work with tessellation and other mathematical ideas are fairly well-known and documented so I'll try to mention a few examples of things I learned in an art history course a while ago.


DaVinci's Vitruvian Man used Phi in the calculation of ratios. Example: the ratio of your arm to your height or your eyes to your face is nearly always Phi. I'm not sure if I'm correct in the body parts mentioned, my art history class was nearly 6 years ago so I'm a bit rusty. I'll try to think of some more examples and post.


EDIT: a few more examples have come back from memory. DaVinci was a master of perspective as well. As you can see DaVinci used linear lines to draw attention to the subject of his works. In the case of The Last Supper, the lines from the structure of the building, to the eyes and gestures of the disciples aim towards Jesus.


Botticelli's Birth of Venus uses a triangle to bring the subject into the viewer's mind. The two subjects on the left and right form the lines that meet at the middle of the top and close off a triangle with the bottom of the work. Venus herself is in the middle of the triangle which brings your attention to her immediately upon viewing the work.


Michelangelo's Pieta also uses a triangle to highlight its subjects. Mary's figure creates a triangle (which is considered to be quite intentional based upon her size, both in relation to Jesus, a full grown man, and from her upper and obviously enlarged lower body). Her triangle makes the outline for the subject, Jesus. He is nearly in the center of both the horizontal and vertical axises. The way he is laying, from near the top of the left and then draping to the bottom of the right, depicts a very lifeless form because of the unnatural laying. Moving the viewer's gaze from the top to the bottom of the triangle strengthens the emotion of the scene.



Moving on to architecture, vaulted ceilings also use triangles to draw your eyes down a line also make an awe-inspiring impression.


In contrast to the European's love of straight lines and geometric figures, the traditional Japanese architectural style was opposed to using straight lines. As you can see, nearly every line in a traditional Japanese building is curved. The traditional belief was that straight lines were evil because they thought evil spirits could only travel in straight lines. This design criteria made for very interesting formations and building methods which I would encourage you to check out because of the sheer dedication to the matter.


The Duomo in Florence is a great example of Renaissance architecture and has a really cool octagonal shaped dome. I could go on and on about how awesome Brunelleschi's design was, but I'll just let you read about it here.


I could talk all day about this sort of stuff, just let me know if you want anything else or have any questions. Good luck with your class!


EDIT2: I've found some more links about the subject of mathematics in art and architecture. It looks like University of Singapore actually has a class on the subject. There's also a good Wikipedia page on it as well. This article is pretty lengthy and knowledgeable, but doesn't include pictures to illustrate the topics. Finally, as almost anybody in r/math will testify, Godel, Escher, Bach by Douglas Hofstadter is a fantastic read for anybody interested in mathematics and cool shit in general.



EDIT3: LITERATURE: I know we've all heard what a badass Shakespeare was, but it really hits you like a bus when you find out that how well the man (or for you Shakespeare conspiracy theorists, men) could use words in rhyme and meter. Here's a Wikipedia article about his use of iambic pentameter and style. Nothing else really comes to mind at the moment as far as writers using math (other than using rhyme and meter like I mentioned Shakespeare doing); however, I can think of a few ways to incorporate math. If you would like to go into any sort of programming during the class, you could show how to make an array out of a word. Once that concept is understood, you could make them solve anagrams or palindromes with arrays... a favorite of mine has always been making [ L , I , N , U , X ] into [ U , N , I , X ] ( [ 3 , 2 , 1 , 4 ] for the non-array folks ).

Bishop's book Pattern Recognition and Machine Learning is pretty great IMHO, and is considered to be the Bible in ML - although, apparently, it is in competition with Murphy's book Machine Learning: A Probabilistic Approach. Murphy's book is also supposed to be a gentler intro. With an ECE background the math shouldn't be too difficult to get into in either of these books. Depending on your background (i.e. if you've done a bunch of information theory) you might also like MacKay's book Information Theory, Inference and Learning Algorithms. MacKay's book has a free digital version and MacKay's 16 part lecture series based on the books is also available online.

While those books are great, I wouldn't actually recommend just reading through them, but rather using them as references when trying to understand something in particular. I think you're better off watching some lectures to get your toes wet before jumping in the deep end with the books. MacKay's lectures (liked with the book) are great. As are Andrew Ng's that @CatZach mentioned. As @CatZach mentioned Deep Learning has had a big impact on CV so if you find that you need to go that route then you might also want to do Ng's DL course, though unlike the courses this one isn't free :(.

Finally, all of the above recommendations (with the exception of Ng's ML course) are pretty theory driven, so if you are more of a practical person, you might like Fast.AI's free deep learning courses which have very little theory but still manage to give a pretty good intuition for why and how things work! You probably don't need to bother with part 2 since it is more advanced stuff (and will be updated soon anyways so I would try wait for that if you do want to do it :))

Good luck! I am also happy to help with more specific questions!

Ok then, I'm going to assume that you're comfortable with linear algebra, basic probability/statistics and have some experience with optimization.

• Check out Hastie, Tibshirani, & Friedman - Elements of Statistical Learning (ESLII): it's basically the data science bible, and it's free to read online or download.
  
• Andrew Gelman - Data Analysis Using Regression and Multilevel/Hierarchical Models has a narrower scope on GLMs and hierarchical models, but it does an amazing treatment and discusses model interpretation really well and also includes R and stan code examples (this book ain't free).
  
• Max Kuhn - Applied Predictive Modeling is also supposed to be really good and should strike a middle ground between those two books: it will discuss a lot of different modeling techniques and also show you how to apply them in R (this book is essentially a companion book for the caret package in R, but is also supposed to be a great textbook for modeling in general).
  
  I'd start with one of those three books. If you're feeling really ambitious, pick up a copy of either:
  
  
• Christopher Bishop - Pattern Recognition and Machine Learning - Bayes all the things.
  
• Kevin Murphy - Machine Learning: A Probabilistic Perspective - Also fairly bayesian perspective, but that's the direction the industry is moving lately. This book has (basically) EVERYTHING.
  
  Or get both of those books. They're both amazing, but they're not particularly easy reads.
  
  If these book recommendations are a bit intense for you:
  
  
• Pang Ning Tan - Introduction to Data Mining - This book is, as it's title suggests, a great and accessible introduction to data mining. The focus in this book is much less on constructing statistical models than it is on various classification and clustering techniques. Still a good book to get your feet wet. Not free
  
• James, Witten, Hastie & Tibshirani - Introduction to Statistical Learning - This book is supposed to be the more accessible version (i.e. less theoretical) version of ESLII. Comes with R code examples, also free.
  Additionally:
  
  
• If you don't already know SQL, learn it.
  
• If you don't already know python, R or SAS, learn one of those (I'd start with R or python). If you're proficient in some other programming language like java or C or fortran you'll probably be fine, but you'd find R/python in particular to be very useful.

Grad school for machine learning is pretty vague, so here's some general resources I think would be good for an incoming CS grad student or undergraduate CS researcher with a focus on deep learning. In my opinion, the courses you mentioned you've done should be a sufficient foundation to dive into deep learning, but these resources cover some foundational stuff as well.

• Kaggle is for machine learning in general. It provides datasets and hardware. It has some nice tutorials and you can look at what other people did.
  
• Google has an online crash course on Machine Learning.
  
• Hands-On Machine Learning with Scikit-learn and Tensorflow is a great book for diving into machine learning with little background. The O'Reilly books tend to be pretty good.
  
• MIT Intro to Deep Learning provides a good theoretical basis for deep learning specifically.
  
• MIT Intro to AI. This is my favorite online lecture series of all time. It provides a solid foundation in all the common methods for AI, from neural nets to support vector machines and the like.
  
• Tensorflow is a common framework for deep learning and provides good tutorials.
  
• Scikit-learn is a framework for machine learning in python. It'd be a good idea to familiarize yourself with it and the algorithms it provides. The link is to a bunch of examples.
  
• Stanford's deep learning tutorial provides a more mathematical approach to deep learning than the others I've mentioned--which basic vector calc, linear algebra, and stats should be able to handle.
  
• 3Blue1Brown is a math youtuber that animates visual intuitions behind many rather high-level concepts. He has a short series on the math of neural networks.
  
• If you are going to be dealing with hardware for machine learning at all, this paper is the gold standard for everything you'd need to know. Actually, even if you aren't dealing with the hardware, I'd recommend you look at the seconds on software. It is fairly high level, however, so don't be discouraged if you don't get some of it.
  
• Chris Olah's Blog is amazing. His posts vary from explanations of complex topics very intuitively to actual research papers. I recommend "Neural Networks, Manifolds, and Topology".

Depends on how old you are.

• Middle school: I really enjoyed this, this, and this, but I don't think I can read those books now (29) without cringing a little bit. Especially, Getting Things Done because I already know how to make to do list, but I still flip through all 3 books occastionally.
  
  
• High school: I really enjoyed this, this, and this, but if you're a well adjusted human and responsible adult, then I don't think you'll find a lot of helpful advice from these 6 books so far because it'll be pretty basic information.
  
  
• College: I really enjoyed this, this, and started doing Malcolm Gladwell books. The checklist book helped me get more organized and So Good They Can't Ignore You was helpful starting my career path.
  
• Graduate School: I really enjoyed this, this, and this. I already stopped with most "self help" books and reading more about how to manage my money or books that looked interesting like Stiff.
  
  
• Currently: I'm working on this, this, and this. Now I'm reading mostly for fun, but all three of these books are way out of my league and I have no idea what their talking about, but they're areas of my interest. History and AI.

Probably start with Artificial Intelligence: a modern approach. This is the state of the art AI as of 2009, of course in AI years that's ancient history but it's background you must know if you're serious about AI.

Following on from that you have the very popular statistical techniques, you can read about these in Pattern Recognition and Machine Learning. These are a wide range of statistical models and algorithms that allow machines to infer, classify and predict. Another very important concept is Chapter 14 on combining models. IBM's Watson for example uses a complex network of "simple" models who combine their answers to form the final responses.

From all the techniques in the previous book, neural networks from Chapter 5 have become the most popular and powerful. These are covered in Deep Learning, and are currently the cutting edge of machine learning. They are extremely general models that seem to be highly successful at a range of tasks. In particular their popularity comes from their amazing accuracy in image recognition, which really challenged past algorithms.

Ultimately nothing you can learn from anyone is sure to bring you close to sci-fi AI. The techniques to produce such an AI eludes even the foremost experts. You may also become disillusioned with your dream as you realise just how mechanical and constrained AI is. I personally think we'd have better luck genetically engineering intelligence in a random animal/insect than creating true intelligence in silicon and circuits.

Jaap Schroder wrote a book detailing his study of the Solo violin works, and he's recorded the concertos as well. That's a good place to begin. There are some really brilliant insights that most students would never consider.

Don't get caught up thinking you are handcuffed and can only imitate an anemic baroque style or a warbly, romantic style. This video is one sort of hybrid, where the soloist and conductor are very aware of performance practice, but modern instruments and techniques are relied upon heavily. Remember that no recordings exits before 1900ish. There's still a lot of personal judgment in a good historically informed performance.

There are many great Bach interpretations, and you should listen to many recordings (Grumiaux is often held in high esteem, and Schroder, as good models) to find out where your preferences lie. You should attempt to play with all sorts of expressive devices (Non vib, lots of decay, faster bow, different bow strokes, bowing patterns, holding the bow higher, gut strings?, baroque bow) and find out what you have to say about Bach. I think any successful interpretation will at least have two major things: a tremendous sense of line (form, rhythm, a large-scale view) and an expressive use of the tone color (bright, warm, deep, thick, feathery, etc.).

Leopold Mozart also wrote a treatise on violin playing. In terms of playing style, he was more familiar with the Baroque than with the music of W.A mozart. He wrote about a sense of "affect" in Baroque music. He wrote that overall, there is one overriding feeling that should come across in Barque works (especially dances and binary form movements.) In the E major Bach, I bet it would be helpful to decide what the "affect" is for each movement. Is there only one, is the narrative single-minded? More simply, come up with something other than "happy" or "sad."

Don't let anyone tell you Bach was a stodgy, strict person. He was ridiculously smart, as shown by his ability to improvise multi-voice fugues. Hofstader wrote eloquently about Bach's puzzles and intellectualism. He was a jokester - the crab canon and the Coffee Cantata or good examples. He was sometimes compensated for his work with large amounts of beer. Bach had somewhere around 20 children, about half of which survived childhood. Bach was a very complex person, with lots of life experience. Don't let a careless caricature influence how you think about his music.

This was originally posted as an image but got deleted for IMO in this case, the irrelevant reason that picture posts are not allowed, though this was all about the text. We had an interesting discussion going: http://www.reddit.com/r/Futurology/comments/2mh0y1/elon_musks_deleted_edge_comment_from_yesterday_on/

I'll just post my relevant contributions to the original to maybe get things started.



---------------------------

And it's not like he's saying this based on his opinion after a thorough study online like you or I could do. No, he has access to the real state of the art:

> Musk was an early investor in AI firm DeepMind, which was later acquired by Google, and in March made an investment San Francisco-based Vicarious, another company working to improve machine intelligence.

> Speaking to US news channel CNBC, Musk explained that his investments were, "not from the standpoint of actually trying to make any investment return… I like to just keep an eye on what's going on with artificial intelligence. I think there is potentially a dangerous outcome there."

*Also I love it that Elon isn't afraid to speak his mind like this. I think it might well be PR or the boards of his companies that reigned him in here. Also in television interviews he is so open and honest, too bad he didn't speak those words there.

----------------------------

I'm currently reading Superintelligence which is mentioned in the article and by Musk. One of the ways he describes an unstoppable scenario is that the AI seems to function perfectly and is super friendly and helpful.

However on the side it's developing micro-factories which can assemble from a specifically coded string of DNA (this is already possible to a limited extent). These factories then use their coded instructions to multiply and spread and then start building enormous amount of nanobots.

Once critical mass and spread is reached they could instantly wipe out humanity through some kind of poison/infection. The AI isn't physical, but the only thing it needs in this case is to place an order to a DNA printing service (they exist) and then mail it to someone it has manipulated into adding water, nutrients, and releasing the DNA nanofactory.

If the AI explodes in intelligence as predicted in some scenarios this could be set up within weeks/months of it becoming aware. We would have nearly no chance of catching this in time. Bostrom gives the caveat that this was only a viable scenario he could dream up, the super intelligence should by definition be able to make much more ingenious methods.

Beyond the obvious choices, Watts' The Book, Ram Dass' Be Here Now, Huxley's Doors of Perception, Leary’s The Psychedelic Experience, and of course Fear and Loathing (all of these should be on the list without question; they’re classics), here are a some others from a few different perspectives:

From a Secular Contemporary Perspective

Godel Escher Bach by Douglass Hofstadter -- This is a classic for anyone, but man is it food for psychedelic thought. It's a giant book, but even just reading the dialogues in between chapters is worth it.

The Mind’s Eye edited by Douglass Hofstadter and Daniel Dennett – This is an anthology with a bunch of great essays and short fictional works on the self.

From an Eastern Religious Perspective

The Tao is Silent by Raymond Smullyan -- This is a very fun and amusing exploration of Taoist thought from one of the best living logicians (he's 94 and still writing logic books!).

Religion and Nothingness by Keiji Nishitani – This one is a bit dense, but it is full of some of the most exciting philosophical and theological thought I’ve ever come across. Nishitani, an Eastern Buddhist brings together thought from Buddhist thinkers, Christian mystics, and the existentialists like Neitzsche and Heidegger to try to bridge some of the philosophical gaps between the east and the west.

The Fundamental Wisdom of the Middle Way by Nagarjuna (and Garfield's translation/commentary is very good as well) -- This is the classic work from Nagarjuna, who lived around the turn of the millennium and is arguably the most important Buddhist thinker after the Buddha himself.

From a Western Religious Perspective

I and Thou by Martin Buber – Buber wouldn’t approve of this book being on this list, but it’s a profound book, and there’s not much quite like it. Buber is a mystical Jewish Philosopher who argues, in beautiful and poetic prose, that we get glimpses of the Divine from interpersonal moments with others which transcend what he calls “I-it” experience.

The Interior Castle by St. Teresa of Avila – this is an old book (from the 1500s) and it is very steeped in Christian language, so it might not be everyone’s favorite, but it is perhaps the seminal work of medieval Christian mysticism.

From an Existentialist Perspective

Nausea by Jean Paul Sartre – Not for the light of heart, this existential novel talks about existential nausea a strange perception of the absurdity of existence.

The Myth of Sisyphus by Albert Camus – a classic essay that discusses the struggle one faces in a world inherently devoid of meaning.

----
I’ll add more if I think of anything else that needs to be thrown in there!

Most of my stuff is going to focus around consciousness and AI.

BOOKS

Ray Kurzweil - How to Create a Mind - Ray gives an intro to neuroscience and suggests ways we might build intelligent machines. This is a fun and easy book to read.

Ray Kurzweil - TRANSCEND - Ray and Dr. Terry Grossman tell you how to live long enough to live forever. This is a very inspirational book.

*I'd skip Kurzweil's older books. The newer ones largely cover the stuff in the older ones anyhow.

Jeff Hawkins - On Intelligence - Engineer and Neuroscientist, Jeff Hawkins, presents a comprehensive theory of intelligence in the neocortex. He goes on to explain how we can build intelligent machines and how they might change the world. He takes a more grounded, but equally interesting, approach to AI than Kurzweil.

Stanislas Dehaene - Consciousness and the Brain - Someone just recommended this book to me so I have not had a chance to read the whole thing. It explains new methods researchers are using to understand what consciousness is.

ONLINE ARTICLES

George Dvorsky - Animal Uplift - We can do more than improve our own minds and create intelligent machines. We can improve the minds of animals! But should we?

David Shultz - Least Conscious Unit - A short story that explores several philosophical ideas about consciousness. The ending may make you question what is real.

Stanford Encyclopedia of Philosophy - Consciousness - The most well known philosophical ideas about consciousness.

VIDEOS

Socrates - Singularity Weblog - This guy interviews the people who are making the technology of tomorrow, today. He's interviewed the CEO of D-Wave, Ray Kurzweil, Michio Kaku, and tons of less well known but equally interesting people.

David Chalmers - Simulation and the Singularity at The Singularity Summit 2009 - Respected Philosopher, David Chalmers, talks about different approaches to AI and a little about what might be on the other side of the singularity.

Ben Goertzel - Singularity or Bust - Mathematician and computer Scientist, Ben Goertzel, goes to China to create Artificial General Intelligence funded by the Chinese Government. Unfortunately they cut the program.



PROGRAMMING

Daniel Shiffman - The Nature of Code - After reading How to Create a Mind you will probably want to get started with a neural network (or Hidden Markov model) of your own. This is your hello world. If you get past this and the math is too hard use this

Encog - A neural network API written in your favorite language

OpenCV - Face and object recognition made easy(ish).

I'm assuming you're looking for things geared toward a layman audience, and not textbooks. Here's a few of my personal favorites:

Sagan

Cosmos: You probably know what this is. If not, it is at once a history of science, an overview of the major paradigms of scientific investigation (with some considerable detail), and a discussion of the role of science in the development of human society and the role of humanity in the larger cosmos.

Pale Blue Dot: Similar themes, but with a more specifically astronomical focus.


Dawkins

The Greatest Show on Earth: Dawkins steers (mostly) clear of religious talk here, and sticks to what he really does best: lays out the ideas behind evolution in a manner that is easily digestible, but also highly detailed with a plethora of real-world evidence, and convincing to anyone with even a modicum of willingness to listen.


Hofstadter

Godel, Escher, Bach: An Eternal Golden Braid: It seems like I find myself recommending this book at least once a month, but it really does deserve it. It not only lays out an excruciatingly complex argument (Godel's Incompleteness Theorem) in as accessible a way as can be imagined, and explores its consequences in mathematics, computer science, and neuroscience, but is also probably the most entertainingly and clearly written work of non-fiction I've ever encountered.


Feynman

The Feynman Lectures on Physics: It's everything. Probably the most detailed discussion of physics concepts that you'll find on this list.

Burke

Connections: Not exactly what you were asking for, but I love it, so you might too. James Burke traces the history of a dozen or so modern inventions, from ancient times all the way up to the present. Focuses on the unpredictability of technological advancement, and how new developments in one area often unlock advancements in a seemingly separate discipline. There is also a documentary series that goes along with it, which I'd probably recommend over the book. James Burke is a tremendously charismatic narrator and it's one of the best few documentary series I've ever watched. It's available semi-officially on Youtube.

I had the same problem. I ride the subway every day and have a ton of time to read, so I've been trying to collect similar resources.

Here are some resources I found really helpful:

1. Beginners book on Unity - http://www.amazon.com/Development-Essentials-Community-Experience-Distilled/dp/1849691444
   
   This is a VERY basic (think: learn how to code!) introduction to Unity. I personally found it too elementary, having coded in a few different languages, but it might be a good place to start as it explains basic Unity design concepts like Components, Materials, Colliders, etc.
   
   
2. Your first Unity project (helps to have Unity accessible to follow alone) - Building a 2D RPG in Unity: http://pixelnest.io/tutorials/2d-game-unity/
   
   This is by fast the best 'getting started' tutorial I've found. It walks you through creating a really basic project from scratch using Unity basics and scripts. This is what I based most of my code off of when I first started my project.
   
   
3. REALLY great book on game design/physics and AI - http://www.amazon.com/Programming-Example-Wordware-Developers-Library/dp/1556220782
   
   This has been the most helpful resource for me. It's not Unity specific but will teach you A TON of great fundamentals for things like how to move a character, common patterns like StateMachines, how to handle AI, etc.... All of these concepts will be relevant and many are already in place in Unity so you'll recognize them right away.
   
   Advanced: Game Programming Patterns - http://gameprogrammingpatterns.com/
   
   This is a book (online/pdf/epub) that teaches the more advanced patterns you'll be applying in your code. I'd suggest this once you finish with the above resources and have been working through your game for a bit.
   

My suggestion is to opensource it under the GPL. That would mean people can use your GPL code in commercial enterprises, but they can't resell it as commercial software without paying for a license.

By opensourcing it, people can verify your claims and help you improve the software. You don't have to worry about languishing as an unknown, or taking venture capital and perhaps ultimately losing control of your invention in a sale or IPO. Scientists can use it to help advance knowledge, without paying the large license fees that a commercial owner might charge. People will find all sorts of uses for it that you never imagined. Some of them will pay you substantial money to let them turn it into specialized commercial products, others will pay you large consulting fees to help them apply the GPL version to their own problems.

You could also write a book on how it all works, how you figured it out, the history of your company, etc. If you're not a writer you could team up with one. Kurzweil and Jeff Hawkins have both published some pretty popular books like this, and there are others about non-AGI software projects (eg. Linux, Doom). If the system is successful enough to really make an impact, I bet you could get a bestseller.

Regarding friendliness, it's a hard problem that you're probably not going to solve on your own. Nor is any large commercial firm likely to solve it own their own; in fact they'll probably ignore the whole problem and just pursue quarterly profits. So it's best to get it out in the open, so people can work on making it friendly while the hardware is still weak enough to limit the AGI's capabilities.

This would probably be the ideal situation from a human survival point of view. If someone were to figure out AGI after the hardware is more powerful than the human brain, we'd face a hard takeoff scenario with one unstoppable AGI that's not necessarily friendly. Having the software in a lot of hands while we're still waiting for Moore's Law to catch up to the brain, we have a much more gradual approach, we can work together on getting there safely, and when AGI does get smarter than us there will be lots of them with lots of different motivations. None of them will be able to turn us all into paperclips, because doing that would interfere with the others and they won't allow it.

Reading some books would be a good idea.

• Superintelligence: Paths, Dangers, Strategies by Nick Bostrom is a good introduction to some philosophy and general tech behind AI. It's really a lovely read.
  
  The following are textbooks:
  
  General AI
  
  
• Artificial Intelligence: A Modern Approach by Russell and Norvig is a conical book on AI. It has an AMAZING companion site ran by UC Berkeley
  
  
• Artifical Intelligence for Humans by Heaton has some overlap with Artificial Intelligence: A Modern Approach, but is also a good overview
  
  Machine Learning
  
  
• Deep Learning by Goodfellow, Bengio, and Courville is a great book on deep learning
  
  
• Introduction to the Math of Neural Networks by Heaton is a great book on the math behind neural networks
  
  
• Make Your Own Neural Network by Rashid is a good guide to making your own neural network
  
  
• Reinforcement Learning: An Introduction by Sutton and Barto
  
  Statistics for Machine Learning
  
  
• Machine Learning: A Probabilistic Perspective by Murphy
  
  
• The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman
  
  There are many other topics within AI which none of these books focus on, such as Natural Language Processing, Computer Vision, AI Alignment/Control/Ethics, and Philosophy of AI. libgen.io may be of great help to you.

That's one of my favorite popular science books, so it's wonderful to hear you're getting so much out of it. It really is a fascinating topic, and it's sad that so many Christians close themselves off to it solely to protect their religious beliefs (though as you discovered, it's good for those religious beliefs that they do).

As a companion to the book you might enjoy the Stated Clearly series of videos, which break down evolution very simply (and they're made by an ex-Christian whose education about evolution was part of his reason for leaving the religion). You might also like Coyne's blog, though these days it's more about his personal views than it is about evolution (but some searching on the site will bring up interesting things he's written on a whole host of religious topics from Adam and Eve to "ground of being" theology). He does also have another book you might like (Faith Versus Fact: Why Science and Religion are Incompatible), though I only read part of it since I was familiar with much of it from his blog.

> If you guys have any other book recommendations along these lines, I'm all ears!

You should definitely read The Selfish Gene by Richard Dawkins, if only because it's a classic (and widely misrepresented/misunderstood). A little farther afield, one of my favorite popular science books of all time is The Language Instinct by Steven Pinker, which looks at human language as an evolved ability. Pinker's primary area of academic expertise is child language acquisition, so he's the most in his element in that book.

If you're interested in neuroscience and the brain you could read How the Mind Works (also by Pinker) or The Tell-Tale Brain by V. S. Ramachandran, both of which are wide-ranging and accessibly written. I'd also recommend Thinking, Fast and Slow by psychologist Daniel Kahneman. Evolution gets a lot of attention in ex-Christian circles, but books like these are highly underrated as antidotes to Christian indoctrination -- nothing cures magical thinking about the "soul", consciousness and so on as much as learning how the brain and the mind actually work.

If you're interested in more general/philosophical works that touch on similar themes, Douglas R. Hofstadter's Gödel, Escher, Bach made a huge impression on me (years ago). You might also like The Mind's I by Hofstadter and Daniel Dennett, which is a collection of philosophical essays along with commentaries. Books like these will get you thinking about the true mysteries of life, the universe and everything -- the kind of mysteries that have such sterile and unsatisfying "answers" within Christianity and other mythologies.

Don't worry about the past -- just be happy you're learning about all of this now. You've got plenty of life ahead of you to make up for any lost time. Have fun!

Programming Game AI by Example has a great, easy-to-understand explanation and walkthrough for learning ANNs: http://www.amazon.com/Programming-Game-Example-Mat-Buckland/dp/1556220782

Once you've learned at least ANNs, you can delve into the popular approaches to GAI:

• Deep Learning (training an ANN with 6 layers of hidden nodes): http://deeplearning.net/tutorials/
  
  
• Neuroevolution (evolving ANNs): http://www.cs.ucf.edu/~kstanley/neat.html
  
  
• Hierarchical Temporal Memory (pattern recognition leading to prediction): https://grok.numenta.com/htm-overview/education/HTM_CorticalLearningAlgorithms.pdf
  
  
• There's more approaches out there, but these are the popular ones nowadays
  
  My biggest recommendation: read about all of them! Try to understand why scientists pursue each approach. Having a solid understanding of the motivation behind each approach will make it much easier for you to decide which path to pursue. I recommend the following books:
  
  
• On Intelligence: http://www.amazon.com/Intelligence-Jeff-Hawkins/dp/B000GQLCVE/
  
  
• How the Body Shapes the Way We Think: A New View of Intelligence: http://www.amazon.com/How-Body-Shapes-Way-Think/dp/0262162393
  
  It looks like the Deep Learning folks have already pitched in, so I'll trust their recommendations are good on that end. Read their stuff too, then decide for yourself. You'll find that there's fanatics from every branch, and they'll claim that their way is the only way. This is the only thing I can tell you for certain: no one has proven that any approach is better than the other just yet, and anyone who claims they have needs to remind themselves of the no free lunch theorem [http://en.wikipedia.org/wiki/No_free_lunch_in_search_and_optimization].

I started by showing my son Scratch when he was 9.5yo and helping him make a couple of arcade games with it. He was never all that interested in Logo, but got really turned on by Scratch. After a couple of months he was frustrated by Scratch's limitations, and so I installed Ubuntu on an old computer, showed him pygame/python and worked through a couple of online tutorials with him, and let him loose.

He learned to use Audacity to edit files from Newgrounds, and Gimp to edit downloaded graphics as well as create his own. He made a walk around, rpg-like adventure game, a 2D platformer, and then decided he wanted to learn pyggel and has been working on a 3D fps since last summer.

Soon, I'm going to get him started on C++ so we can work through a book on game AI (which uses C++ for all its examples). He's 13.5 now, and thinks programming is great and wants to grow up to be a programmer like his mom :)

I highly recommend a simple language like python for a beginner, but Scratch is wonderful for learning all the basic concepts like flow control, variables, objects, events, etc, in a very fun and easy way. The Scratch web site also makes it simple to share or show off because when you upload your program it gets turned into a Java applet, so anyone with a browser can see what you've done.

I recommend reading: The User Illusion by Tor Norretranders, Gödel, Escher, Bach by Douglas R. Hofstadter, and I Am a Strange Loop also by Douglas R. Hofstadter for some interesting reading on the subject (Warning: Gödel, Escher, Bach isn't for everyone- it's a bit strange, but I love it). I read a lot of books on science in general and, based on that, it seems like many believe consciousness and also free will is just an illusion. In fact, just a few days ago, physicist Brian Greene sorta-kinda said as much in his AMA - granted, he's talking specifically about free will and not consciousness per se, but I think the two must be very related.

I, too, believe in God and also have a very strong belief in and enthusiasm for science, so this is an especially fascinating question for me.

BTW: if you're interested in the way the brain works in general, I highly recommend How the Mind Works by Steven Pinker.

Sometimes depression isn't about circumstances or perspective, it's entirely chemical.

I've thought of the answers to these questions hundreds of times. But the thing about depression is that sometimes it makes it impossible to find a happy answer.

Here are my thoughts:

1. I have a goal. I'm going to school for Computer Science and Philosophy. I'm hoping to go into AI research. I want to improve the world through new discoveries. That's not my reason to live, though. The only reason I'm alive is because I don't want to hurt the people who love me. I only feel more awful when I think of the wasted potential that my death would cause, which makes me feel even more depressed. Having a goal and dreams does little to curb my depression, and only increases my anxiety.
   
   
2. I'm doing absolutely everything I can to get better. I'm taking my meds, I'm going through therapy, I'm doing everything I'm supposed to do. I think every person deserves to be happy, including me. Happiness is just much, much, harder for some to achieve than other. It's frustrating when so much work doesn't get you very far.
   
   
3. I am connected to everything around me, I am a part of this universe. Even when my consciousness ceases, I (well, maybe not "I") will continue to impact everything around whether through ideas or the physical decomposition of my body. I think I contribute a lot even while alive, and the world has so much to offer. I just lack the capacity to enjoy so much of it.
   
   
   I'll try to keep the philosophical bit short, because I could really lose myself in a rant otherwise :p
   
   I think that an existentialist philosophy can have a lot to offer on a human level. In a way, everything is functionally meaningless, in the sense that so much meaning is beyond our understanding. However, I think that rather than a complete lack of meaning in the universe, meaning is an inherent part of the universe.
   
   To quote Hofstadter (taken from P-6 of Godel, Escher, Bach:
   >Shouldn't meanings that one choooses to read into strings of meaningless symbols be totally without consequence?
   
   
   >Something very strange thus emerges from the Godelian loop: the revelation of the causal power of meaning in a rule-bound but meaning-free universe.
   
   Basically, by the way that matter is related to other matter, meaning emerges from even the tiniest connection. And that meaning can push matter around in the same way that matter can cause meaning. Ideas are not only meaningful, they have causative power. I think that's pretty cool!
   
   So basically, I agree that humans may have insignificance on some scale. But in the grand scheme of things, there is something so much more magnificent that we are a part of.
   
   Anyway...while I appreciate your thoughts and respect your desire to help others, I think that you are a bit misinformed. That's okay! It's nearly impossible for someone who hasn't experienced depression to know what it's like. But there are ways to better understand and help. Here is a great resource from /r/SuicideWatch that shares some ways that you can connect with depressed or suicidal people. I think it may help a lot!
   
   Oh, and sorry for what turned out to be a philosophical rant anyways; I just can't resist invoking Hofstadter and isomorphism in the face of existentialism :p

I think this might be beyond what you're looking for, but I really enjoyed Pattern Recognition and Machine Learning. It's very heavy on statistics, and if you're looking into machine learning methods, it has a wonderful amount of mathematical information given in a fairly clear manner. It might be severe overkill if this isn't your field, but I thought I'd mention it since you said AI.

For AI in general, I see Artificial Intelligence: A Modern Approach used a lot. It gives some solid basic concepts, and will be helpful in getting you started writing basic AI in your applications.

I can't really recommend discrete math because, despite enjoying it quite a bit, I haven't found a textbook that I like enough to endorse. My textbook for it in college was by Rosen, and I despised it.

edit:
Just double checked it, and I would stay far away from the first recommendation unless you have a very extensive knowledge of sophisticated statistics. I like it because it gives the math that other books gloss over, but it's not good for an introduction to the subject. It's almost like going through a bunch of published papers on some new cutting edge methods. The ever popular Machine Learning by Thomas Mitchell is a much better introduction to machine learning. If you want to obtain the mathematical depth necessary for your own research into the field, go with the other book after you've gotten acquainted with the material. I'll leave my suggestion up anyway in case anyone here might find it interesting.

One book that i didnt see mentioned in a casual skim of the posts is Off to be the Wizard
https://www.amazon.co.uk/Off-Be-Wizard-Magic-2-0/dp/1612184715

A very silly series where a modern day guy ends up in an alternate dimension where he can do magic/control the world via programming. Super light reads, fun and funny, and pulls in your computer interest. If you enjoy the first one, you can pick up the others.

If you want something a bit meatier, check out some Douglas Hofstadter.

Le Ton Beau de Marot (it's in English) is about the process and problems of translating languages, and makes surprisingly good bathroom reading because the chapters are short. He starts the scope small, talking about whether to focus on literal meaning or the spirit of the words, and then brings in more concepts like artificial constraints (poetry, or even writing without certain letters, for one example). It is philosophical, informative, and amusing. https://www.amazon.co.uk/dp/B012HVQ1R0/ref=cm_sw_r_cp_awdb_L2sgAbDYFK1XK

He also wrote Godel Escher Bach: an Eternal Golden Braid. https://www.amazon.co.uk/dp/0465026567/ref=cm_sw_r_cp_awdb_b3sgAbQ79TTGS better writers than I have written reviews (this one is from Amazon)

>Twenty years after it topped the bestseller charts, Douglas R Hofstadter's Gödel, Escher, Bach: An Eternal Golden Braid is still something of a marvel. Besides being a profound and entertaining meditation on human thought and creativity, this book looks at the surprising points of contact between the music of Bach, the artwork of Escher, and the mathematics of Gödel. It also looks at the prospects for computers and artificial intelligence (AI) for mimicking human thought. For the general reader and the computer techie alike, this book still sets a standard for thinking about the future of computers and their relation to the way we think.

I'll start off with some titles that might not be so apparent:

Unexpected Fundamentals

These 2 books provide much needed information about making reusable patterns and objects. These are life saving things! They are not language dependent. You need to know how to do these patterns, and it shouldn't be too hard to figure out how to implement them in your chosen language.

• Design Patterns: Elements of Reusable Object-Oriented Software
  
  
• Game Programming Paterns
  
   
  
  Good General book
  
  
• Effective C++: 55 Specific Ways to Improve Your Programs and Designs
  
   
  
  This book is great if you're going to make a browser based game
  
  
• HTML5 Canvas Cookbook
  
   
  
  General Knowledge books
  
  
• Pro HTML5 Games (Expert's Voice in Web Development)
  
   
  
  Provide a working moveable 3D model with C++ and DirectX, Very cool.
  
  
• Character Animation With Direct3D
  
   
  
  More general game base building
  
  
• Game Engine Architecture, Second Edition
  
  
• Programming Game AI By Example
  
   
  
  Working product results books, little if any modification needed
  
  Releasing in a couple months (hopefully) 2 Very good books using C++ to develop by.
  
  
• Introduction to 3D Game Programming with DirectX 12
  
  
• Introduction to 3D Game Programming with DirectX 11
  
   
  
  Not presented in the best manner but still noteworthy:
  
  
• DirectX 11.1 Game Programming
  
   
  
  I used to love XNA...but now it's not feasible for commercial development. If you're a beginner to game design...starting out with XNA might actually be useful. It's easy to pickup and put out a working product. XNA is C#
  
   
  
  
• Learning XNA 4.0: Game Development for the PC, Xbox 360, and Windows Phone 7
  
  
• XNA Game Studio 4.0 Programming: Developing for Windows Phone 7 and Xbox 360
  
  
• XNA 4.0 Game Development by Example: Beginners Guide
  
  
• Beginning XNA 3.0 Game Programming: From Novice to Professional (Expert's Voice in XNA)
  
  
• XNA 3.0 Game Programming Recipes: A Problem-Solution Approach (Expert's Voice in XNA)
  
  
• Building XNA 2.0 Games: A Practical Guide for Independent Game Development (Books for Professionals by Professionals)
  
   
  
  Working product books, modification needed to make run on current systems
  
  
  Provides a working FPS game in C++ on DirectX 9. Good for some starting out knowledge for an FPS
  
  
• Programming Mutliplayer FPS Direct X
  
   
  
  Good for 3D Terrain rendering in DX9...however much of this is outdated...some concepts still apply, and it's not the worst idea to see a working example.
  
  
• Real-Time 3D Terrain Engines Using C++ and DirectX9 (Charles River Media Game Development)
  
   
  
  TLDR: Click links starting at top, buy, read, profit

Thank you so much for your reply. I actually do plan on taking Andrew Ng's course just cause the book I am talking about is very limited to Python but I've heard great things about it. However, the Stanford course I was referring to was the Statistical Learning course based on the ISL book.

Yes I plan on doing some kaggle challenges once I feel comfortable with my skills to build up my portfolio or see if I can find some other novel projects to work on.


Ideally I'd like to be in a data science consultancy type role where I get to work on different kinds of projects and don't necessarily need very specialized domain knowledge. But at this point I think more direction as to what kind of roles exits would also be helpful. I just don't know what the field is actually like and I've never really met anyone doing data science for a living.

Thank you again for your reply. It was very helpful.

I personally really benefitted from Jose Portilla's udemy class on python for Data Science: https://www.udemy.com/python-for-data-science-and-machine-learning-bootcamp. It deals with the machine learning algorithms at a pretty basic level but he does a good job overviewing things and this course personally gave me more confidence. He also wrote a helpful overview for how to become a data scientist: https://medium.com/@josemarcialportilla/how-to-become-a-data-scientist-2d829fa33aba

Additionally, I found this podcast episode from Chris Albon helpful: http://partiallyderivative.com/podcast/2017/03/28/learning-machine-learning

Finally, I have just started going through Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems and I love it. It's very easy to read and applicable: https://www.amazon.com/dp/1491962291/_encoding=UTF8?coliid=I1VIM81L3W5JUY&colid=2MMQRCAEOFBAX

Hope this helps.

Machine learning isn't a cloud thing. You can do it on your own laptop, then work your way up to a desktop with a GPU, before needing to farm out your infrastructure.

If you're serious about machine learning, you're going to need to start by making sure your multivariate calculus and linear algebra is strong, as well as multivariate statistics (incl. Bayes' theorem). Machine learning is a graduate-level computer science topic, because it has these heady prerequisites.

Once you have these prereqs covered, you're ready to get started. Grab a book or online course (see links below) and learn about basic methods such as linear regression, decision trees, or K-nearest neighbor. And once you understand how it works, implement it in your favorite language. This is a great way to learn exactly what ML is about, how it works, how to tweak it to fit your use case.

There's plenty of data sets available online for free, grab one that interests you, and try to use it to make some predictions. In my class, we did the "Netflix Prize" challenge, using 100MM Netflix ratings of 20K different movies to try and predict what people like to watch. Was lots of fun coming up with an algorithm that wrote its own movie: it picked the stars, the genre and we even added on a Markov chain title generator.

Another way to learn is to grab a whitepaper on a machine learning method and implement it yourself, though that's probably best to do after you've covered all of the above.

Book: http://www-bcf.usc.edu/~gareth/ISL/

Coursera: https://www.coursera.org/learn/machine-learning

Note: this coursera is a bit light on statistical methods, you might want to beef up with a book like this one.

Hope this helps!

Depends on what you are interested in.

If you are interested in games, pick a game and do it. Most board games are not that hard to do a command line version. A game with graphics, input, and sound isn't too bad either if you use something like Allegro or SDL. Also XNA if you are on windows. A lot of neat tutorials have been posted about that recently.

If you are more interested in little utilities that do things, you'll want to look at a GUI library, like wxWidgets, Qt and the sort. Both Windows and Mac have their own GUI libraries not sure what Windows' is called, but I think you have to write it with C++/CLI or C#, and Mac is Cocoa which uses Objective-C. So if you want to stick to basic C++ you'll want to stick to the first two.

Sometimes I just pick up a book and start reading to get ideas.

This is a really simple Game AI book that is pretty geared towards beginners. http://www.amazon.com/Programming-Game-Example-Mat-Buckland/dp/1556220782/

I enjoyed this book on AI, but it is much more advanced and might be kind of hard for a beginner. Although, when I was first starting, I liked getting in over my head once in a while. http://www.amazon.com/Artificial-Intelligence-Modern-Approach-2nd/dp/0137903952/

Interesting topics to look up.

Data Structures

Algorithms

Artificial Intelligence

Computer Vision

Computer Graphics

If you look at even simple books in these subjects, you will usually find tons of small manageable programs that are fun to write.

EDIT: Almost forgot, I think a lot of these are Java based, but you can usually find a way to do it in C++. http://nifty.stanford.edu/ I think I write Breakout whenever I am playing with a new language. heh

> Why?

First, let's define what we're talking about. I haven't seen this Anime, but it shows someone jacking into VR via a plug in the back of their neck. So for the purposes of discussion, let's assume this is Matrix-level VR. That means a virtual reality that is literally indistinguishable from actual reality, via a plug into the back of the head.

In this exact form, this is impossible (see below). More extreme mechanisms (brain in a jar) might be possible, but that's currently a total unknown. And we won't have the tech for hundreds of years.

Why?

For starters, our command of biology is currently profoundly limited and progress is slow. We're justifiable proud of how far we've come, but we're still crude butchers. In the last few hundred years we discovered anesthesia and antibiotics, but we still fix people but cutting them with knives and literally sewing them back together. We don't understand how the brain works, much less have the ability to send it accurate information or read information back out.

So what do we need for Matrix-level VR? We need to:

Intercept and replace all information going into and out of the brain non-destructively.

This in itself is probably impossible. It's likely that Matrix-level VR will require removing the brain from a body, or severing the spinal column and doing all manner of damage to the face. Note that in the Matrix (and the OP's video), we bypass the senses via a plug in the back of the neck, with the idea being that you intercept all data going to and from the body via the spinal chord. But most of the data going into and out of your brain doesn't happen via the spinal chord. The eyes, ears, and nose have direct links to your brain. For instance, a jack on the back of your neck can't intercept and replace signals coming from your optic nerve. So that's just a bit of science-fiction fantasy that makes for convenient story telling, like faster than light travel.

Perfectly replicate the entire nervous system and musculature of the human body in a computer and flawlessly simulate bidirectional nerve impulses to the brain.

In the Matrix, you can feel every muscle in your body. You can feel that hot sauce you just ate, or the need to take a shit or a piss. Your entire body is simulated and the nerve impulses going to the brain are indistinguishable from those of a real body. Moreover, the entire network of nerve firings required to say, walk, is flawlessly interpreted by the virtual body -- contracting all the correct muscle fibers and resulting in you having the grace to dance, or do Kung Fu.

We're probably 50 years to even having the compute power to model that, much less the technology to perfectly interface it with a human brain, assuming we had the brain in a vat and didn't have to figure out how to intercept all replace those nerve signals without harming the person.

Perfectly replicate the entire world in a machine.

We've been working on computer graphics for over 50 years, and we still haven't achieved real time photorealism, especially in stereo, at retinal resolution, at human max FOV. Let's say we get there in the next 20 years or so (highly optimistic), now we have the surface of things. We can render an apple that looks 100% convincing. The next 100 years or so will be doing everything else.

What's inside the apple? In order to full simulate what can happen to an apple -- how it responds to a knife, or a tooth, the skin of it, the juice inside, the way it will bruise or rot, what a slice of it looks like under a microscope, so on and so forth, you have to simulate it from first principles. Now imagine that you also have to perfectly replicate the way it feels in a virtual mouth, and the way it tastes, the way it smells. Again, you have to simulate it from first principles. You have basically build a model of the entire chemistry of an apple (not to mention perfectly simulate bacteria) to cover all possible cases.

And that's just an apple. What about everything else? We basically need to be able to simulate a universe from first principles. We don't even know if that's possible. Clock speeds for our current technology stagnated a decade ago. We're about to run into a quantum mechanical limitation for transistor size. We assume we'll find a way around it, but that's currently unknown.

We know that computing power has been rising exponentially, and we expect it to continue to do so for a while, but there's no guarantee that it will do so forever. Bacteria in a petri dish multiply exponentially, too. If some early generation noticed this trend, they might be tempted to imagine that the bacteria will eventually take over the entire universe. But their exponential growth hits a hard limit (when they run out of space/food). It could be there's a similar limit on computing power. We don't know. In any case, the kind of power we need for the Matrix is at best centuries away, if it will ever exist at all. That's not even counting the biological engineering involved.

There's only one way I could see it coming any sooner (again, assuming it's even possible): we develop a superhuman AI which can do our research for us at vastly accelerated subjective timeframes. But then we have much bigger problems.

>What brought you that position?

My path included convert parents, BIC, a very happy childhood in a huge loving family, RM, 30ish years TBM, 10 years agnostic (closeted 7 or 8 years), going on I think 7ish years now as a Christian. I'm bookish - PhD in engineering. My agnostic period kind of grew out of the full term surprise stillbirth of our second child. I was already starting to question BoM historicity, I had issues with the whole "I know the church is true" thing / epistemology, and it was a fairly quick worldview failure after that. Then with the discovering church history. You get the idea. During my agnostic period, I held a position pretty much identical to what I hear you describing: I cared deeply about truth (still do, very much), I knew logic works (still a big fan, but more aware of its limits now), eventually felt called to try to 'get off the fence' of agnosticism, if I could do it authentically. My approach was to start reading more. Things I read: Godel Escher Bach, Tolstoy's War and Peace, Pilgrim's Progress, a bunch of CS Lewis, the bible in modern english, and a bunch of other stuff I can't remember. Some things that most impressed me about the bible: stories about what goes on in people's hearts that I could see in myself, in my loved ones, and around me in the world, the coherence of the entire narrative around the theme of redemption, the concept of Grace implied in God's relationship to His people and later extended to the individual by Paul.

> what makes you believe in the Christian God?

Here is one thing I wrote about that before.

The Machine Intelligence Research Institute is putting out a call for intelligent stories illustrating concepts related to (artificial or natural) intelligence. Guidelines are quite specific; read below.

• 
  -Pay Rate: 8c/word, up to 5000 words.
  
• 
  -Multiple Submissions ok
  
• 
  -Simultaneous Submissions ok
  
• 
  -Submissions window: Until July 15
  
   
  
  This call is intended to reward people who write thoughtful and compelling stories about artificial general intelligence, intelligence amplification, or the AI alignment problem. We're looking to appreciate and publicize authors who help readers understand intelligence in the sense of general problem-solving ability, as opposed to thinking of intelligence as a parlor trick for memorizing digits of pi, and who help readers intuit that non-human minds can have all sorts of different non-human preferences while still possessing instrumental intelligence.
  
  The winning stories are intended to show (rather than tell) these ideas to an intellectually curious audience. Conscious attempts to signal that the ideas are weird, wonky, exotic, or of merely academic interest are minuses. We're looking for stories that just take these ideas as reality in the setting of the story and run with them. In all cases, the most important evaluation criterion will just be submissions’ quality as works of fiction; accurately conveying important ideas is no excuse for bad art!
  
  -
  
  To get a good sense of what we're looking for—and how not to waste your time!—we strongly recommend you read some or all of the following.
  
  
• 
  Superintelligence
  
• 
  Smarter Than Us
  
• 
  Waitbutwhy post 1, Waitbutwhy post 2 (with caveats)
  
   
  
  Withdrawal policy:
  
  After you submit a story, we prefer you don't withdraw it. If you withdraw a story, we won't consider any version of that story in the future. However, if you do need to withdraw a story (because, for example, you have sold exclusive rights elsewhere), please send an e-mail telling us that you need to withdraw ASAP.
  
   
  
  Important Notes:
  
  MIRI is neither a publishing house nor a science fiction magazine and cannot directly publish you. However, MIRI will help link a large number of readers to your story.
  
  We frankly do not know whether being selected by MIRI will qualify as a Professional Sale for purposes of membership in the SFWA. We suspect, through readership numbers and payscale, that it will, but we have not spoken to the SFWA to clarify this.
  
  If you have a work of hypertext fiction you think might be a good fit for this call, please query us to discuss how to submit it.
  
   
  
  How to Contact Us:
  
  To contact us for any reason, write to intelligenceprize@gmail.com with the word QUERY: at the beginning of your subject line. Add a few words to the subject line to indicate what you're querying about.
  
   
  
  (We've discontinued the previous, smaller monthly prize in favor of this more standard 'Publishing House Call' model.)

I can't offer you a lot in the way of non-fiction. If you haven't read it, Gödel, Escher, Bach by Douglas Hofstadter is a good read. It is very dense and slow reading, but can be rewarding. If you like computer science, biology, math, or music in any combination, this could be a good book for you.

The secret to picking good non-fiction is to find something you're interested in or curious about and read a book about it. Things like neuro-linguistic programming, cryptography, riding horses, biking, running, cacti of the saguaro desert, Trees of the Eastern Forests, Scuba diving, Lockpicking, Prestidigitation (aka "magic tricks"), etc.

Of other books I've loved but could not mention in my top 3, I include:

• Kushiel's Dart by Jacqueline Carey
  
• Black Sun Rising by C.S. Friedman
  
• Cryptonomicon by Neal Stephenson
  
• Slaughterhouse Five by Kurt Vonnegut (or any book by Kurt Vonnegut)
  
  That's all I can think of right now at work, but if you want more, PM me and I'll see what I can dig up.

It sounds like you have identified your weakness. Presently, that is programming in python, and using the sklearn library.

I would recommend taking a MOOC on python first. Lynda.com has a free trial and python videos. datacamp is another good start. It has a free trial and mayybe some python basics, but definately something on sklearn. and you can get some pandas training or R training there. (the kaggle libraries, most likely).

At that point, if you are going the tensorflow route, Aurelion has a great hands-on book called Learning Tensorflow with sci-kit learn

If you’re going with pyTorch I dunno.

Your mileage is going to vary, you could always use a book to learn python, or whatever.

Just make sure you learn to program first, you’d be surprised how much 2 weeks of very hard work will earn you. Don’t expect it to be ‘easy’ ever tho.

Also, if you’re not formally educated in statisics, keep an eye out for statistics advice until you have the time to work on it. (like in a MOOC, course, or blog). Learning some real analysis will make understanding the papers a real possibility (once again it will probably never be easy)

It is truly stunning how many years of preparation it takes to become competent in this. It’s a lovely science, but the competent ones have generally been on a mathematical/science track since 5th grade. Doesn’t mean we can’t become competent but it takes time. Imagine the equivalent of an undergraduate degree just devoted to ML and you’re about there.

This is a popular topic but I don't often see a comprehensive answer. I'm by no means an expert and currently learning myself.

There's two key stepping stones before jumping into AI, that being learning Python and data science. Python has wide support and a host of libraries reflecting the latest research on AI development.

There is also R, Octave and Java depending on the libraries you're looking to use, but they aren't nearly as popular as python. Note that if you want to embed your AI scripts into web apps or apps, then you'll need to learn javascript and java respectively.

The best resources for Python are

1. Automate the Boring stuff - Al Sweigert
   
   
2. Hitch hikers guide to Python
   
   
3. Dive into python
   
   Great resources can be found here:
   
   The next step is to get a brief grasp of data science. You can learn these from:
   
   
4. www.datacamp.com for Python and R
   
   
5. Coursera course on data science
   
   
6. Udemy courses in Python and R (Note these would most likely be paid courses so wait for the monthly discounts to kick in to purchase them for $10-$15)
   
   I wouldn't recommend codeacadmy since it's dated written in Python v2.x whereas Python 3.6x is more widely used
   
   Then I would consider AI Specific courses found online. Theres two routes again here, there's the heavily academic route that delves into the theory and mathematics then there;s the practical route. Depends on the speed and pace you want to learn at because it's a massive field.
   
   Theoretical
   
   
7. Udacity - Introduction to Artificial Intelligence (standford course)
   
   
8. Coursera - Andrew Ng's Deep Learning specialization course. Note the course uses octave which is similar to Matlab style programming. The courses when accessed individually are for free or you can pay for a certification.
   
   
9. Various lectures on youtube for MIT and Stanford's Artificial Intelligence courses.
   
   
10. A really good text book to check out is Artificial Intelligence - A modern Approach. AI was traditionally scripted in Lisp or prolog. This has been coverted into Python over here
    
    Practical:
    
    
11. Krill Ermenko - AI, Machine Learning and DEEP Learning from A-Z
    
    
12. Fast.ai Dives into keras a top level library
    

> If it is about how we interpret our experiences, we can't but fall prey to confirmation bias.

When you're aware of confirmation bias, it is possible to overcome it. That is one of the most important parts of the scientific method: that it accounts for and eliminates the problem of confirmation bias. We can take lessons from the scientific method and eliminate our own confirmation bias in the same way. How we do this is by not trying to prove what we want to prove, but by trying our hardest to disprove it with objective tests, then have others check our work (like peer review).

> Assuming they existed, if you were to witness the sighting of a real ghost, you would likely interpret it as a wisp of fog.

Actually you're just projecting. I would not jump to any conclusions if I didn't know what it was. If it disappeared before I could investigate, the most I could conclude would be that it was something unexplained that disappeared before I could investigate it. I might say that it was probably a wisp of fog and I could back that up with evidence and reasoned arguments, but I wouldn't just jump to whatever conclusion happens to fit my beliefs best as you clearly would.

> It doesn't matter which view you favor, for confirmation bias to occur. It only matters that you favor one over the others and that this can influence your perception of the event.

Which is why, for important conclusions, we should make sure to eliminate confirmation bias using the methods I described above, or some other method that would also take account of it and correct for it. When we do this for any supernatural claims, they quickly fall apart or retreat to the untestable.

> You don't tell people who don't share your basic assumptions to look at things objectively, because it will mean different things to both of you.

I don't think you quite know what the word "objective" means. If it means different things to both of you, it's subjective, not objective.

> If you tell someone who believes in God to look at this objectively, it isn't an unfounded leap at all.

Yes it is. Asserting otherwise doesn't make it true. It does not logically follow from the fact that a dying man sees some light and feels calm that he is seeing an afterlife. There is no logical connection between the two. The only thing that connects them is pure speculation and wishful thinking.

> It is actually very reasonable: A belief predicts that after death my soul will go to heaven, a place filled with light, warmth, and joy. When I am close to death I experience a feeling of floating up, out of my body, and sensations just as predicted by my faith.

Actually I would be willing to bet that near death experiences far out-date religion. But even if not, there is a rather large disconnect between "Seeing some tunnel of light and feeling high" and "Seeing the afterlife". You are making a completely unfounded assumption (actually quite a lot of them), but you are too steeped in your own web of beliefs and superstitions to see it.

The absolute most you can reasonably conclude is that there is an unknown reason why people close to death experience what you described above. To conclude anything above that without demonstrable evidence is unfounded.

> Where a leap of faith is necessary, is at the level of basic assumptions: Belief in God and truth of the Bible. And nobody even disputes that...

They do, actually. Plenty of people, especially on Reddit, claim that their beliefs in God and the truth of the Bible are supported by evidence and reasoning, just as you are doing right now. Like you, they are too tangled in their own web of beliefs and confirmation bias to see that they are just making unfounded leaps.

> Welcome to the mind-body problem, and the deep waters of philosophy.

You need not welcome me. My girlfriend is pursuing a PhD in this very problem, and I myself have been interested in it for a long time. I've already heard and read far more about it than I could possibly digest. The more I've researched it, by the way, the more it seems to be true that there is no such thing as a separate soul or some entity that can survive the death of our physical brains. All of the evidence points away from such a thing. As a starting point, may I recommend Consciousness Explained by philosopher, Daniel Dennet. It attempts to provide a possible explanation of how consciousness is a distributed process in the brain with each part working together, rather than centered in some Cartesian theater. For a heavier read, I would recommend Gödel Escher Bach by physicist, Douglas Hofstadter. It explores the deep meaning that comes from recursion and self reference in art, music, mathematics, logic and in the world itself, and explains how consciousness could be the result of many layers of self referential recursive systems and structures.

>There have been some excellent trading opportunities with returns as high as 30% to your overall portfolio! Crypto is providing big returns that are uncommon in traditional markets.

I guess you have a good intention, Mr. Hustle, but I'd hate to see the kind shibes here being taken advantage of again. You should be more objective and also warn people that they can as easily lose that much of money when trading, especially when they don't know what they are doing initially.

And the effectiveness of technical 'analysis' is a highly debatable issue. I'd just leave this quote from Wikipedia:

> Technical analysis is widely used among traders and financial professionals and is very often used by active day traders, market makers and pit traders. In the 1960s and 1970s it was widely dismissed by academics. In a recent review, Irwin and Park[13] reported that 56 of 95 modern studies found that it produces positive results but noted that many of the positive results were rendered dubious by issues such as data snooping, so that the evidence in support of technical analysis was inconclusive; it is still considered by many academics to be pseudoscience.[14] Academics such as Eugene Fama say the evidence for technical analysis is sparse and is inconsistent with the weak form of the efficient-market hypothesis.[15][16] Users hold that even if technical analysis cannot predict the future, it helps to identify trading opportunities.[17]

...

> Whether technical analysis actually works is a matter of controversy. Methods vary greatly, and different technical analysts can sometimes make contradictory predictions from the same data. Many investors claim that they experience positive returns, but academic appraisals often find that it has little predictive power.[51] Of 95 modern studies, 56 concluded that technical analysis had positive results, although data-snooping bias and other problems make the analysis difficult.[13] Nonlinear prediction using neural networks occasionally produces statistically significant prediction results.[52] A Federal Reserve working paper[21] regarding support and resistance levels in short-term foreign exchange rates "offers strong evidence that the levels help to predict intraday trend interruptions," although the "predictive power" of those levels was "found to vary across the exchange rates and firms examined".

I'm not saying not to take coaching from DogeHustle, just that if people want to do it, be aware of its 'limitation' too and have fun doing it with your disposable money only. As an alternative, I strongly suggest shibes who want to try predicting the future based on pattern analysis to do it in a principled manner and learn math, stats and machine learning. It won't be easy, but it will have a wide application beyond trading (so-called data 'science' is the hot job nowadays). It will also teach you the limitation of such methods, and when it might fail, especially in such a manipulated market like crypto. This is a good book to start with:

http://www.amazon.co.uk/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020

A good textbook will do you wonders. Get one that is fairly general and includes exercises. Do the exercises. This will be hard, but it'll make you learn an enormous amount faster.

My personal favourite book is Christopher Bishop's Pattern Recognition and Machine Learning. It's very comprehensive, has a decent amount of maths as well as good examples and illustrations. The exercises are difficult and numerous.

That being said, it is entirely Machine Learning. You mention wanting to learn about 'AI' so potentially you may want to look at a different book for some grounding in the wider more classical field of AI than just Machine Learning. For this I'd recommend Russel and Norvig's [AI: A Modern Approach](https://smile.amazon.co.uk/Artificial- Intelligence-Modern-Approach-Global/dp/1292153962). It has a good intro which you can use to understand the structure and history of the field more generally, and following on from that has a load of content in various areas such as search, logic, planning, probabilistic reasoning, Machine Learning, natural language processing, etc. It also has exercises, but I've never done them so I can't comment much on them.

These two books, if you were to study them deeply would give you at least close to a graduate level of understanding. You may have to step back and drill down into mathematical foundations if you're serious about doing exercises in Bishop's book.

On top of this, there are many really good video series on youtube for times when you want to do more passive learning. I must say though, that this should not be where most of your attention rests.

Here are some of my favourite relevant playlists on YouTube, ordered in roughly difficulty / relevance. Loosely start at the top, but don't be afraid to jump around. Some are only very tenuously related, but in my opinion they all have some value.

Gilbert Strang - Linear Algebra

Gilbert Strang - Calculus Overview

Andrew Ng - Machine Learning (Gentle coursera version)

Mathematical Monk - Machine Learning

Mathematical Monk - Probability

Mathematical Monk - Information Theory

Andrew Ng - Machine Learning (Full Stanford Course)

Ali Ghodsi - Data Visualisation (Unsupervised Learning)

Nando de Freitas - Deep Learning

The late great David MacKay - Information Theory

Berkeley Deep Unsupervised Learning

Geoff Hinton - Neural Networks for ML

Stephen Boyd - Convex Optimisation

Frederic Schuller - Winter School on Gravity and Light

Frederic Schuller - Geometrical Anatomy of Theoretical Physics

Yaser Abu-Mostafa - Machine Learning (statistical learning)

Daniel Cremers - Multiple View Geometry

The problem with ANSI CL is that I could never shake the feeling that Graham wants Lisp in general to maintain some mystique as language only suited for the very clever, and he teaches the language with intent on keeping it that way. I really enjoyed PCL, but I really do think that Paradigms of Artificial Intelligence Programming needs to get more attention. Granted that I haven't yet finished the mammoth volume, Norvig introduces the language in a clear way that makes it seem more natural (perfect example is that he prefers 'first' and 'rest' rather than the more esoteric 'car' 'cdr'), but additionally he has great 'hand holding' examples that show exactly what makes Common Lisp so powerful and how to organize largers programs in language as well as going over a ton of interesting CS related things. Having gone through these 3 books while I was learn I can definitely say that each had a lot to offer, but I think if I was trapped on an island with just one I would definitley take PAIP.

Well, I skimmed through most of your entries there, and a couple things stood out to me:

> "I wanna be rich enough to fund team of best engineers to do things to make my life
better, to make'the world better"

-- [journal 1 abriged, pg 3]

Why not become one of those engineers yourself? Seriously. If you're an INTP, I'm sure you'll enjoy any kind of engineering you get into, those fields are nice and logical and everything, and then you could actually produce something with your knowledge besides just "ideas".

Study up on programming, read up on transhumanism, and go work on some novel AI stuff, cause that seems to be vaguely where your interests are pointing towards. And then change the world with your creations. Ideas are a dime a dozen, so you can't just expect people to 'engineer' them for you. And I say this as a working entrepreneur / software developer / musician, so I'm not just pulling all this out of my ass.

also:
> "I want to be either a genius or insane, not anonymous"

-- [same source/page]

This stands out because at the moment, it seems like you're focusing too hard on the not anonymous part, rather than the actual building of genius part. That's totally natural and understandable, but realize that it's like endlessly chasing a girl; as romantic as it may seem from your point of view, it's often just interpreted as a turn-off. You don't need to rely on others to provide that 'genius' status for you, if you build it up yourself, it will come naturally.

Define for yourself (concretely!) what 'genius' looks like for you, within your primary area of focus. Then break it down into a roadmap of actionable steps that you could accomplish as if you were the only one that could ever possibly give 2 shits about your idea. Only then will you gain enough automaticity to fuel your journey and possibly succeed to the point where others might recognize your work. Because trying to start from the top and work your way down doesn't usually work.

You have a long road ahead and this is all just a blip on the radar, so don't worry about making mistakes or taking detours, just focus on structuring something solid for yourself, and the rest will logically fall in place later.

Oh, I have a bunch of recommendations.

First, I really think you should read Elkhonon Goldberg's The New Executive Brain. Goldberg was the student of neuropsychology legend Alexander Luria. He was also a good friend of Oliver Sacks, whose books are both informative and highly entertaining (try The Man who Mistook his Wife for a Hat).

I also think Jeff Hawkins' On Intelligence is a great read. This book focuses on the neocortex.

I think you'll also appreciate Sapolsky's Why Zebras Don't Get Ulcers. Sapolsky is a great storyteller. This book is a pretty good primer on stress physiology. Stress affects the brain in many ways and I'm sure this book will be very eye-opening to you!

More suggestions:

The Age of Insight and In Search of Memory by Eric Kandel are good. The Tell-Tale Brain and Phantoms of the Brain by Ramachandran are worth checking out. If you are interested in consciousness, you should check out Antonio Damasio and Michael Graziano. And Giulio Tononi and Gerald Edelman.

If you're up for a challenge I recommend Olaf Sporn's Networks of the Brain and Buzsáki's Rhythms of the Brain.

First 6 weeks of Andrew Ng's [basic ML course] (https://www.coursera.org/learn/machine-learning), while reading Intro to Statistical Learning, for starters (no need to implement exercises in R, but it is a phenomenal book).

From there you have choices (like taking the next 6 weeks of Ng's basic ML), but for Deep Learning Andrew Ng's [specialization] (https://www.coursera.org/specializations/deep-learning) is a great next step (to learn CNNs and RNNs). (First 3 out of 5 courses will repeat some stuff from basic ML course, you can just skip thru them).
To get into the math and research get the Deep Learning book.

For Reinforcement Learning (I recommend learning some DL first), go through this [lecture series] by David Silver (https://www.youtube.com/watch?v=2pWv7GOvuf0) for starters. The course draws heavily from this book by Sutton and Barto.

At any point you can try to read papers that interest you.

I recommend the shallower, (relatively) easier online courses and ISLR because even if you are very good at math, IMO you should quickly learn about various topics in ML, DL, RL, so you can hone in on the subfields you want to focus on first. Feel free to go through the courses as quickly as you want.

I was in your shoes not long ago, though a much diff background and job situation.

> I guess maybe my question boils down to do I need to at some point go to grad school?

Yes but don't worry about grad school right now. It's expensive and you'll do better with it once you've been working in the real world. Try and get work to pay for it too.

>I'm not against it, but would rather learn on my own and make it that way, is that feasible?

Yes you can start using ML techniques at work without formal training. Don't let it stop you. Get a good book - I use Kevin Murphy's and also have a copy of EoSL on my desk from the work library (its free online pdf though).

ML is a somewhat broad and growing field. So if you have the mindset that you need to cover it all before you start using it you'll be sticking thumbs up your ass for a few years.

A better approach will be what is your specific data. Just like you're probably familiar with from using SQL, standard textbook techniques or something in a research paper rarely applies exactly to you what you're working with. So it's almost better to approach your problem directly. Explore the data, look at the data, study the data (in a stats fashion) and then look into what could an intelligent program do to better analyze it. And then in the meantime you can study more general ML topics after work.

First book I recommend to any programmer, no matter what they're working on, is The Pragmatic Programmer. Excellent stuff.

If you don't get a shot at low-level coding at work, get yourself an Arduino kit and just hack away. Apparently the language is similar to / based on the C programming language. I use C every day.

To do well with embedded systems, real-time, device driver, or kernel type stuff, you have to really, really, really, understand what the hardware is doing. I was able to learn gradually because I started programming when there was one CPU and no cache memory. Each hardware operation was straightforward. Now with multi-core CPUs, multi-level cache memory, multiple software threads, it becomes a bit more complex. But something like the Arduino will teach you the basics, and you can build on that.

Every day I have to think asynchronously - any operation can happen any time, and if they share memory or other resources, they can't step on each other. It can get hairy - but it's really fun to reason about and I have a blast.

There's a lot more I'm sure, but get started with some low-level hacking and you can build from there.

If you want to get meta, many of the best programmers I know love Godel, Escher, Bach because it widens your mental horizons. It's not about programming per se, but I found that it helps my programming at a meta level. (and it'll give you a lot to meditate on when you're baked!)

/r/ControlProblem

The Control Problem:

How do we ensure that future artificial superintelligence has a positive impact on the world?

"People who say that real AI researchers don’t believe in safety research are now just empirically wrong." - Scott Alexander

"The AI does not hate you, nor does it love you, but you are made out of atoms which it can use for something else." - Eliezer Yudkowsky

Check out our new wiki

Some Guidelines

1. Be respectful, even to people that you disagree with
   
2. It's okay to be funny but stay on topic
   
3. If you are unfamiliar with the Control Problem, read at least one of the introductory links before submitting a text post.
   
   

   Introductory Links
   

   
   

• Talks at Google: Ensuring Smarter-than-Human Intelligence has a Positive Outcome
  
  
• WaitButWhy on Superintelligence (Lengthy but entertaining & accessible. Also read part 2)
  
  
• Three areas of research on the Control Problem (in-depth, organizes different topics w/ links to academic papers)
  
  
• [Nick Bostrom's TED Talk] (http://www.ted.com/talks/nick_bostrom_what_happens_when_our_computers_get_smarter_than_we_are)
  
  
• MIRI's Intelligence Explosion FAQ
  
  
• Wikipedia on the risk of Artificial Intelligence
  
  

  Recommended Reading
  

  
  

• Superintelligence by Nick Bostrom (2014)
  
• Our Final Invention by James Barrat (2013)
  
• Smarter Than Us: The Rise of Machine Intelligence by Stuart Armstrong (2014)
  
  

  Important Organizations
  

  
  

• Machine Intelligence Research Institute
  
• Future of Humanity Institute
  
• Center for the Study of Existential Risk
  
  

  Related Subreddits
  

  
  

• /r/AIethics
  
• /r/Artificial
  
• /r/decisiontheory
  
• /r/effectivealtruism
  
• /r/existentialrisk
  
• /r/Singularity
  
  *****
  ^(Bot created by /u​ /el_loke - )^Feedback

wow, yeah, 10 years is closer than 50 that's for sure. If you are right, that is something to be very excited about for sure. Just think of the possibilities. Can I ask where you get the estimate of 10 years? I am fairly uneducated on the subject, and admittedly I haven't even read the book about it therefore I am hesitant to even mention it, but I am familiar with Ray Kurzweil and his theories about The Singularity (basically when man and machine combine, hence "redefining" what it means to be human). I found his recent comments on nano-bots in our brains making us "God-like" intriguing to say the least, and if ever we will be able lay back, close our eyes, and experience some sort of virtual reality, it just makes sense to me that the most likely time when that will happen is when we have super intelligent nano-bots inside of our brains manipulating the way they work. I, personally, can't wait for this to happen, but I also know that I will be very apprehensive when it will come down to willfully injecting into my body millions and millions of nano-bots that have been specially designed to 'hijack' my brain, and make it work better. I think I will probably wait 10 years or so after people start doing it, maybe longer.

Here is Ray Kurzweil's book I was referring to that I really want to read: The Singularity Is Near: When Humans Transcend Biology

EDIT: I forgot to mention why I really brought up the singularity-- Mr. Kurzweil initially predicted that the singularity would occur sometime before 2030 (aka in the 2020's), but I believe he has now modified that to say that it will occur in the 2030's. Either way, that is not far away, and, being a pretty tech-savvy person myself (I pay attention to a thing or two) I think the 2030's is a reasonable estimate for something like this, but, as I mentioned earlier, I think it is the ethics of such a thing that will slow down true VR development (see: how the world responded to cloning)

double EDIT: just another thought (albeit quite a tangent)-- once a true singularity has been achieved (if ever?), 'transplanting' our consciousnesses into another body all of a sudden becomes quite a bit less sci-fi and altogether a more realistic possibility...

Gödel, Escher, Bach: An Eternal Golden Braid

It's a book you have to work through (or think through) but it's extremely rewarding and entertaining. It'll make you feel dumb and confused dozens of times, and then give you triumphant moments of discovery where everything you just read makes sense and you feel like a genius. He does this intentionally, and the effect is amazing.

This will make you a smarter person, and it'll make math and thought and life and biology and chemistry and physics and music and art and language more provocative. You'll feel like you're seeing the world in new colors.

Here are the review counts on amazon:


5 star
255

4 star
38

3 star
20

2 star
16

1 star
15

It's hard not to love this book.

You can get it at your library, they all have it. It's been a best seller since the 80s.

Some context, I've been living in this house for about 3 years now, my girlfriend and i moved in to take care of the owner of the house. Turns out that he was a big lisp / scheme hacker back in the 80s-90s and had developed a lot of cutting edge tech in his hay day. Anyway, these books have been hiding in his library downstairs...

It was like finding a bunch of hidden magical scrolls of lost knowledge :)

edit: I will compile a list of the books later. I'm out doing 4th of July things.

update: List of books

• Lisp: Style and Design by Molly M. Miller and Eric Benson
  ISBN: 1-55558-044-0
  
  
• Common Lisp The Language Second Edition by Guy L. Steele
  ISBN: 1-55558-042-4
  
  
• The Little LISPer Trade Edition by Daniel P. Friedman and Matthias Felleisen
  ISBN: 0-262-56038-0
  
  
• Common LISPcraft by Robert Wilensky
  ISBN: 0-393-95544-3
  
  
• Object-Oriented Programming in Common Lisp by Sonya E. Keene
  ISBN: 0-201-17589-4
  
  
• Structure and Interpretation of Computer Programs by Harold Abelson, Gerald Jay Sussman w/Julie Sussman
  ISBN: 0-07-000-422-6
  
  
• ANSI Common Lisp by Paul Graham
  ISBN: 0-13-370875-6
  
  
• Programming Paradigms in LISP by Rajeev Sangal
  ISBN: 0-07-054666-5
  
  
• The Art of the Metaobject Protocol by Gregor Kiczales, Jim des Rivieres, and Daniel G. Bobrow
  ISBN: 0-262-11158-6
  
  
• Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp by Peter Norvig
  ISBN: 1-55860-191-0
  
  
• Practical Common Lisp by Peter Seibel
  ISBN: 1-59059-239-5
  
  
• Common Lisp The Language by Guy L. Steele
  ISBN: 0-932376-41-X
  
  
• Anatomy of Lisp by John Allen
  ISBN: 0-07-001115-X
  
  
• Lisp Objects, and Symbolic Programming by Robert R. Kessler
  ISBN: 0-673-39773-4
  
  
• Performance and Evaluation of Lisp Systems by Richard P. Gabriel
  ISBN: 0-262-07093-6
  
  
• A Programmer's Guide to Common Lisp by Deborah G. Tatar
  ISBN: 0-932376-87-8
  
  
• Understanding CLOS The Common Lisp Object System by Jo A. Lawless and Molly M. Miller
  ISBN: 0-13-717232-X
  
  
• The Common Lisp Companion by Tim D. Koschmann
  ISBN: 0-417-50308-8
  
  
• Symbolic Computing with Lisp and Prolog by Robert A. Mueller and Rex L. Page
  ISBN: 0-471-60771-1
  
  
• Scheme and the Art of Programming by George Springer and Daniel P. Friedman
  ISBN: 0-262-19288-8
  
  
• Programming In Scheme by Michael Eisenberg
  ISBN: 0-262-55017-2
  
  
• The Schematics of Computation by Vincent S. Manis and James J. Little
  ISBN: 0-13-834284-9
  
  
• The Joy of Clojure by Michael Fogus and Chris Houser
  ISBN: 1-935182-64-1
  
  
• Clojure For The Brave and True by Daniel Higginbotham
  ISBN: 978-1-59327-591-4
  
  
  
  

That is an incredibly broad question. Without knowing what you've already studied, it's hard to recommend things. Most of the aerospace and mechanical engineers I know use pre-packaged programs rather than writing their own scripts, etc.

Artificial intelligence might be the best one, though. Russel and Norvig is the standard textbook: https://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597

The plus side to learning about AI is that it is not really programming intensive - it's logic and statistics intensive.

If you want to go the programming route, it gets a little hairier. The reason is that advanced systems designs will take a lot of initial classes just to get you to a level where you are comfortable programming and can then think about design and program flow.

Take an intro course. I learned programming with C / C++ and Matlab. Recommend those since it's easier to blow your foot off when programming. Once you understand how to design programs, what functions are, how program control can be passed off, move over into Python (much easier to pick up and run with and much better supported).

You might also benefit from a databases or Big Data class due to the amount of data generated from an aircraft.

Regular expressions and scripting is another option. But that's good for anyone.

In that case...
You may want to wait for the 5th edition of UNIX and Linux System Administration, as it should release near the end of this year and they don't release new versions that often.

A good way to get started building a college library is to see what the curriculum for the school is and what books are required by professors. Often other colleges will list their book recommendations for the courses online to get an idea of where to start looking. (I know my school has an online bookstore that lists the books for each course and is open to the public)

At least one or two good books in each of those categories, to get a rough idea to start:

• General Programming (A book for each core language, though a lot of information is online as well)
  C, C++, Java, Python and Ruby are popular to start with
  
  
• Web Development (a wide area, but an html/css/javascript text is basic)
  
  
• System Programming (Windows and Linux are wildly different, should probably have both even if not taught - though you already have one linux system programming text recommended, 'the linux programming interface')
  https://www.amazon.com/Advanced-Programming-UNIX-Environment-3rd/dp/0321637739
  https://www.amazon.com/Programming-Paperback-Addison-Wesley-Microsoft-Technology/dp/0134382250
  
  
• Computer Networking
  you already have a couple here
  
  
• Algorithms - the selected book (Introduction to Algorithms) is good, sometimes used for two courses, a complementary text wouldn't hurt too
  
  
• Artificial Intelligence
  https://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597
  
  
• Numerical Methods
  
  
• System Administration (Windows and Linux are different, the recommended text is good for Linux - here's a windows one)
  https://www.amazon.com/Windows-Internals-Part-architecture-management/dp/0735684189 (coming soon)
  
  
• Parallel Computing
  
  
• Distributed Computing
  
  
• Operating Systems - I personally like Stallings' text, though Tanenbaum and Silberschatz is popular too
  
  
• Databases - Nearly all the database books are not that good, in my opinion, but still should have something
  
  
• Cryptography
  
  
• Computer Security
  
  Edit: On second thought, pretty much every college teaches system programming on linux - so perhaps just the linux texts would be adequate for that.

What a great question, and an interesting example. For those confused by OP's example, check out Gödel's Incompleteness Theorem on Wiki. Better yet, read the insightful and very trippy Pulitzer Prize winning book, Gödel, Escher, Bach. Gödel's theorem is a bit abstract but it was both a monumental and surprising discovery. It's not just mathematical -- it's meta-mathematical, in that it reveals the limitations inherent to any mathematical framework or system. From wiki:

>The first incompleteness theorem states that no consistent system of axioms...is capable of proving all truths about the relations of the natural numbers (arithmetic). For any such system, there will always be statements about the natural numbers that are true, but that are unprovable within the system. The second incompleteness theorem, an extension of the first, shows that such a system cannot demonstrate its own consistency.

I'll point out an obvious one, though it's more to do with the aesthetics of the psychedelic experience rather than insights or ideas. Psychedelic hallucinations tend to be geometric, with lattices, grids, spirals, and perhaps most intriguing of all, fractals. All these are geometric forms that can be rigorously defined and analyzed by math. Fractals are especially fascinating because they exhibit self-similarity at every scale, appear sometimes in nature (for example, coastlines), and look extremely trippy. (Seriously, just look at these zoom-ins of the Mandelbrot set, discovered in 1978.)

I first heard about these when reading Godel Escher Bach back in later high school. That book was a long, difficult read, but man did it blow my brain wide open. Quines are definitely the thing that I remember most vividly (probably because it was the easiest to understand), but that book was full of awesome stuff like this.

You should totally check it out! You can get it super cheap at used book stores since it was such a successful book.

Bostrom's Superintelligence covers gene editing very well, but let me summarize:

The singularity isn't likely going to come through gene editing. The reason is it's too difficult to improve on the brain. If you identify which genes are responsible for genius and activate them (which is difficult to say the least), you could get everyone as intelligent as the smartest person yet. But where you do you go from there? You'd have to understand the brain on a level far, far beyond what we do now.

Then if you did that, chances are you'd run up into diminishing gains. It would be a lot of work to increase everyone's IQ by 5 points once, but far more work to figure out how to do it the 10th time. Rather than getting exponentially increasing gains in intelligence, you get logarithmic increases.

Not to say I'm not a fan of gene editing. It's obviously fraught with controversy when used beyond curing disease, but compared to other forms of trans-humanistic techniques it would leave us with a lot more humanity intact.

Good ones!

I suggest trying to wear two “outer” shirts for one waking day - dress,polo, or any other type of collared shirt.

Find and buy one item solely for airplane miles arbitrage.

Watch an anime from John Siracusa and have Him as a guest. I want to hear Max and Merlin pick on John a bit, though he is almost always “good cop”.

For a serious one (if they ever want to do “serious”) I would love for all of them to expound on their thinking of how the mind handles memory/ consciousness - though this might be a Rec/Diffs topic just for John and Merlin:

I read a fascinating book (On Intelligence) that not only explained in lay terms how your brain (logically) processes inputs, but had a good theory of how a single method of working explained learning, practice, memory, and actually moving your muscles to do something - most theories can’t explain them all in a single method.

<br />
Speaking of miles arbitrage, my brother in law is a frequent traveler using arbitraged miles. He routinely buys money orders that offer some kind of very large miles bonus, then deposits it into the bank to pay the bill; the small fee for the money order is offset by the mileage gain. He has travelled more in a couple years than I have in my entire life - some of it paid, some on miles. considering he is the one who handles money responsibly, and I am most certainly not, he must be onto something. <br />
<br />
That might also be a good topic: revisit a lesson they learned about handling money. 

Artificial neural networks are great, but keep in mind that they're just a means to an end. The best way to learn them is to go through a good textbook or online course where you'll try them out on good examples that have been designed specifically to be good for beginners.

To a professional, you don't start with the tool and search for a problem - you start with a problem and figure out the best tool. Sometimes that tool is neural networks, but probably 99% of the time it's not. Even when the right tool is "machine learning", there are a lot of machine learning techniques other than ANNs.

As a beginner, the best thing you can do is start by learning about machine learning in general. You can't properly use ANNs if you don't understand the principles of machine learning in general, which is what the book or course I linked above will give you.

​

Here's my list of the classics:

General Computing

• But How Do It Know? - The Basic Principles of Computers for Everyone
  
  
• The Elements of Computing Systems: Building a Modern Computer from First Principles
  
  
• Code: The Hidden Language of Computer Hardware and Software
  
  
• Gödel, Escher, Bach: An Eternal Golden Braid
  
  Computer Science
  
  
• The New Turing Omnibus: Sixty-Six Excursions in Computer Science
  
  
• Compilers: Principles, Techniques, and Tools, 2nd Edition (aka The Dragon Book)
  
  
• The Art of Computer Programming
  
  
• Algorithms, 4th Edition
  
  
• Introduction to Algorithms, 3rd Edition (aka CLRS)
  
  
• Essential Algorithms: A Practical Approach to Computer Algorithms Using Python and C#, 2nd Edition
  
  
• Grokking Algorithms: An Illustrated Guide for Programmers and Other Curious People
  
  
• Structure and Interpretation of Computer Programs, 2nd Edition (aka SICP, available for free on the MIT website)
  
  
• Discrete Mathematics with Applications, 4th Edition
  
  Software Development
  
  
• Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann
  
  
• Design Patterns: Elements of Reusable Object-Oriented Software (aka The Gang of Four)
  
  
• Think Like a Programmer: An Introduction to Creative Problem Solving
  
  
• The Pragmatic Programmer: your journey to mastery, 20th Anniversary Edition, 2nd Edition
  
  
• The Mythical Man-Month: Essays on Software Engineering
  
  
• Code Complete: A Practical Handbook of Software Construction, 2nd Edition
  
  
• The Design of Everyday Things
  
  
• Clean Code: A Handbook of Agile Software Craftsmanship
  
  
• Programming Pearls, 2nd Edition
  
  
• Soft Skills: The Software Developer's Life Manual
  
  
• Hello, Startup: A Programmer's Guide to Building Products, Technologies, and Teams
  
  
• Hacker's Delight, 2nd Edition
  
  
• Peopleware: Productive Projects and Teams, 3rd Edition
  
  Case Studies
  
  
• The Soul of a New Machine
  
  
• Masters of Doom: How Two Guys Created an Empire and Transformed Pop Culture
  
  
• Hackers: Heroes of the Computer Revolution
  
  
• Where Wizards Stay Up Late: The Origins Of The Internet
  
  
• Fire in the Valley: The Birth and Death of the Personal Computer, 3rd Edition
  
  Employment
  
  
• Cracking the Coding Interview: 189 Programming Questions and Solutions, 6th Edition
  
  
• The Complete Software Developer's Career Guide: How to Learn Programming Languages Quickly, Ace Your Programming Interview, and Land Your Software Developer Dream Job
  
  
• The Self-Taught Programmer: The Definitive Guide to Programming Professionally
  
  
• The Passionate Programmer: Creating a Remarkable Career in Software Development
  
  Language-Specific
  
  C
  
  
• The C Programming Language, 2nd Edition
  
  Python
  
  
• Automate the Boring Stuff with Python: Practical Programming for Total Beginners, 2nd Edition
  
  
• Python Crash Course: A Hands-On, Project-Based Introduction to Programming
  
  
• Python Programming: An Introduction to Computer Science, 3rd Edition
  
  
• Effective Python: 59 Specific Ways to Write Better Python
  
  C#
  
  
• The C# Player's Guide, 3rd Edition
  
  C++
  
  
• The C++ Programming Language, 4th Edition
  
  
• Starting Out with C++: from Control Structures to Objects, 9th Edition
  
  Java
  
  
• Introduction to Java Programming and Data Structures: Comprehensive Version, 11th Edition
  
  
• [Java: A Beginner's Guide](https://www.amazon.com/Java-Beginners-Seventh-Herbert-Schildt/dp/1259589315/, 7th Edition)
  
  
• Java: The Complete Reference, 11th Edition
  
  
• Core Java Volume I - Fundamentals, 11th Edition
  
  
• Starting Out with Java: From Control Structures through Objects, 6th Edition
  
  
• Effective Java, 3rd Edition
  
  Linux Shell Scripts
  
  
• The Linux Command Line: A Complete Introduction, 2nd Edition
  
  
• Wicked Cool Shell Scripts: 101 Scripts for Linux, OS X, and UNIX Systems, 2nd Edition
  
  
• Linux Command Line and Shell Scripting Bible
  
  
• Mastering Linux Shell Scripting
  
  Web Development
  
  
• Learning Web Design: A Beginner's Guide to HTML, CSS, JavaScript, and Web Graphics, 5th Edition
  
  
• Eloquent JavaScript: A Modern Introduction to Programming, 3rd Edition (also available for free on the author's website)
  
  
• JavaScript and JQuery: Interactive Front-End Web Development
  
  
• HTML and CSS: Design and Build Websites
  
  
• You Don't Know JS: Up & Going
  
  Ruby and Rails
  
  
• Ruby on Rails Tutorial: Learn Web Development with Rails, 4th Edition (Note: this book is somewhat out of date compared to the live version on the author's website.)
  
  
• The Well Grounded Rubyist, 3rd Edition
  
  
• Practical Object-Oriented Design in Ruby: An Agile Primer
  
  
• Agile Web Development with Rails 5.1
  
  
• Programming Ruby 1.9 & 2.0: The Pragmatic Programmers' Guide, 4th Edition
  
  
• Eloquent Ruby
  
  Assembly
  
  
• Assembly Language for x86 Processors, 8th Edition

Chapter 7 of Chris Bishop's book Pattern Recognition and Machine Learning has a nice intro to SVMs.

Here is a list of papers where SVMs were used in a computational biology

> Gene Function from microarray expression data
>
> Knowledge-based analysis of microarray gene expression data by using support vector machines, Michael P. S. Brown, William Noble Grundy, David Lin, Nello Cristianini, Charles Walsh Sugnet, Terence S. Furey, Manuel Ares, Jr., David Haussler, Proc. Natl. Acad. Sci. USA, vol. 97, pages 262-267
> pdf
> http://www.pnas.org/cgi/reprint/97/1/262.pdf
>
> Support Vector Machine Classification of Microarray Gene Expression Data, Michael P. S. Brown William Noble Grundy, David Lin, Nello Cristianini, Charles Sugnet, Manuel Ares, Jr., David Haussler
> ps.gz
> http://www.cse.ucsc.edu/research/compbio/genex/genex.ps
>
> Gene functional classification from heterogeneous data Paul Pavlidis, Jason Weston, Jinsong Cai and William Noble Grundy, Proceedings of RECOMB 2001
> pdf
> http://www.cs.columbia.edu/compbio/exp-phylo/exp-phylo.pdf
>
> Cancer Tissue classification
> from microarray expression data, and gene selection:
>
> Support vector machine classification of microarray data, S. Mukherjee, P. Tamayo, J.P. Mesirov, D. Slonim, A. Verri, and T. Poggio, Technical Report 182, AI Memo 1676, CBCL, 1999.
> ps.gz
> PS file here
>
> Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data, Terrence S. Furey, Nigel Duffy, Nello Cristianini, David Bednarski, Michel Schummer, and David Haussler, Bioinformatics. 2000, 16(10):906-914.
> pdf
> http://bioinformatics.oupjournals.org/cgi/reprint/16/10/906.pdf
>
> Gene Selection for Cancer Classification using Support Vector Machines, I. Guyon, J. Weston, S. Barnhill and V. Vapnik, Machine Learning 46(1/3): 389-422, January 2002
> pdf
> http://homepages.nyu.edu/~jaw281/genesel.pdf
>
> Molecular classification of multiple tumor types ( C. Yeang, S. Ramaswamy, P. Tamayo, Sayan Mukerjee, R. Rifkin, M Angelo, M. Reich, E. Lander, J. Mesirov, and T. Golub) Intelligent Systems in Molecular Biology
>
> Combining HMM and SVM : the Fisher Kernel
>
> Exploiting generative models in discriminative classifiers, T. Jaakkola and D. Haussler, Preprint, Dept. of Computer Science, Univ. of California, 1998
> ps.gz
> http://www.cse.ucsc.edu/research/ml/papers/Jaakola.ps
>
> A discrimitive framework for detecting remote protein homologies, T. Jaakkola, M. Diekhans, and D. Haussler, Journal of Computational Biology, Vol. 7 No. 1,2 pp. 95-114, (2000)
> ps.gz
> PS file here
>
> Classifying G-Protein Coupled Receptors with Support Vector Machines, Rachel Karchin, Master's Thesis, June 2000
> ps.gz
> PSgz here
>
> The Fisher Kernel for classification of genes
>
> Promoter region-based classification of genes, Paul Pavlidis, Terrence S. Furey, Muriel Liberto, David Haussler and William Noble Grundy, Proceedings of the Pacific Symposium on Biocomputing, January 3-7, 2001. pp. 151-163.
> pdf
> http://www.cs.columbia.edu/~bgrundy/papers/prom-svm.pdf
>
> String Matching Kernels
>
> David Haussler: "Convolution kernels on discrete structures"
> ps.gz
> Chris Watkins: "Dynamic alignment kernels"
> ps.gz
> J.-P. Vert; "Support vector machine prediction of signal peptide cleavage site using a new class of kernels for strings"
> pdf
>
> Translation initiation site recognition in DNA
>
> Engineering support vector machine kernels that recognize translation initiation sites, A. Zien, G. Ratsch, S. Mika, B. Scholkopf, T. Lengauer, and K.-R. Muller, BioInformatics, 16(9):799-807, 2000.
> pdf.gz
> http://bioinformatics.oupjournals.org/cgi/reprint/16/9/799.pdf
>
> Protein fold recognition
>
> Multi-class protein fold recognition using support vector machines and neural networks, Chris Ding and Inna Dubchak, Bioinformatics, 17:349-358, 2001
> ps.gz
> http://www.kernel-machines.org/papers/upload_4192_bioinfo.ps
>
> Support Vector Machines for predicting protein structural class Yu-Dong Cai*1 , Xiao-Jun Liu 2 , Xue-biao Xu 3 and Guo-Ping Zhou 4
> BMC Bioinformatics (2001) 2:3
> http://www.biomedcentral.com/content/pdf/1471-2105-2-3.pdf
>
> The spectrum kernel: A string kernel for SVM protein classification Christina Leslie, Eleazar Eskin and William Stafford Noble Proceedings of the Pacific Symposium on Biocomputing, 2002
> http://www.cs.columbia.edu/~bgrundy/papers/spectrum.html
>
> Protein-protein interactions
>
> Predicting protein-protein interactions from primary structure w, Joel R. Bock and David A. Gough, Bioinformatics 2001 17: 455-460
> pdf
> http://bioinformatics.oupjournals.org/cgi/reprint/17/5/455.pdf
>
> Protein secondary structure prediction
>
> A Novel Method of Protein Secondary Structure Prediction with High Segment Overlap Measure: Support Vector Machine Approach, Sujun Hua and Zhirong Sun, Journal of Molecular Biology, vol. 308 n.2, pages 397-407, April 2001.
>
> Protein Localization
>
>
> Sujun Hua and Zhirong Sun Support vector machine approach for protein subcellular localization prediction Bioinformatics 2001 17: 721-728
>
>
> Various
>
> Rapid discrimination among individual DNA hairpin molecules at single-nucleotide resolution using an ion channel
> Wenonah Vercoutere, Stephen Winters-Hilt, Hugh Olsen, David Deamer, David Haussler, Mark Akeson
> Nature Biotechnology 19, 248 - 252 (01 Mar 2001)
>
> Making the most of microarray data
> Terry Gaasterland, Stefan Bekiranov
> Nature Genetics 24, 204 - 206 (01 Mar 2000)

This is the absolute must fucking-awesome time to be alive. The world is accumulating knowledge at an amazingly increasing rate. Right now, the world's amount of aggregate knowledge doubles every 1.5 years. We are really close to having self-driving cars. Things that were computationally intractable 10 years ago are now trivial today. And, the rate of growth there is accelerating as well. Imagine in 10 years, the best supercomputing cluster may be able to simulate a brain as complicated as a dog. 10 years later, designing and simulating brains will probably be a video game that kids play, e.g. design the most powerful organisms and have them battle and evolve in a changing environment.

Go to /r/automate and /r/futurology and see what is coming. Get How to Create a Mind and read that, it is a book by a scientist who is now the chief scientist of Google, and he has an extremely optimistic view of the future.

Congratulations, you have just freed your mind! Now, use it to do something awesome, make a shit load of money, find meaningful relationships, and contribute something to humanity.

>I didn't say it was correct but it makes it more likely that people will dismiss it out of hand.

That's fair, I agree. It's just frustrating: there are so many strawmen arguments related to AI that a huge number of intelligent people dismiss it outright. But if you actually look into it, it's a deeply worrying issue-- and the vast majority of people who actually engage with the good arguments are pretty damn concerned.

I would be very interested if anyone can produce a compelling rebuttal to the main points in Superintelligence, for instance. I recommend this book very highly to anyone, but especially people who wonder "is AI safety just bullshit?"

>Especially when those people get significant amounts of funding

Numerically speaking, this is inaccurate. Cf. this article.

You would love Godel Escher Bach by Douglas R Hofstadter. It won the pullitzer prize and is basically just a really good popular math/computer science/art book. But a really excellent jumping off point. Yes it lacks mathematical rigor (of course) but if you are a bright clever person who likes these things, its a must read just for exposure to the inter-connectivity of all of these topics in a very artistic and philosophical way. But be prepared for computer code, musical staff notation, DNA sequences, paintings, and poetry (all themed around Godel, Escher and Bach).

Hmm alright, considering your background, I'd probably recommend you giving Michael Sipser's Introduction to Theory of Computation a read (I sure there are many electronic copies floating around on the Internet). I think they cover the prerequisite math concepts required in a preliminary chapter before the content which I highly recommend you spend some time on. It works it's way up by walking you through notions of computations in increments, first through finite state automata before adding in more features, working its way up to a Turing machine. You can skip most of the exercises, since those are mostly for graduate students who need practice before undertaking research. If you ever get confused about concepts along the way just drop me a PM or a question in /r/askcomputerscience and I'm sure the community would be happy to help out.

Also if you're interested I could mail you my copy of (meaning a copy that I had bought some time ago, not that I wrote it) the Annotated Turing. It does a great job of explaining the concept of a Turing machine provided a non-mathematical and non-CS background. I'd be more than happy to share my books with people who are interested, plus there's no use in me keeping it around now that I'm done with it.

Just bear in mind that unlike most of science, the concepts here are very abstract, there aren't many direct physical implications, this really is a pure study of notions at play. i.e. how does one go about studying "how to do things" and its implications. A lot of details such as "how can such a machine exist with an infinite tape? what moves it? how does it implement its decision making scheme?" are all unimportant and ultimately inconsequential to the study itself.

Instead, what we care about are things like "I have a problem, is it possible for me to come up with a solution (algorithm) for it? Or is it logically impossible?" or things like "I have come up with a way to make a "computer", can it do things that other computers can? If I had to make it sort an arbitrary set of numbers so that they are ordered numerically, can my computer do it?". Turing machines, are a tool to help us reason about formally around these sort of arguments, and to give insight into what we can qualify as "computation". Further down the line we even ask questions like "are some problems inherently more 'difficult' than others?" and "if I can solve problem B, and I somehow use the solution for problem B to solve some other problem A?"

Perhaps this all sounds perplexing now, but maybe just go through some content and spend time reading a little and these should start to make a little more sense. good luck with your future endeavors on this journey!

Learn some math, yes. Algebra, Discrete Math, Inductive Logic, Set Theory. Calc and Matrix Algebra are good for specific things, and just in general to beef up your math skills. But don't get hung up on it too much. It's a good thing to always have going in the background.

Start to think about how all computation is math - check out The Annotated Turing and really wrap your head around both what Petzold is talking about and what Turing is talking about.

That may require you to take a step back and study Formal Languages, Finite State Machines, and other related concepts (all the stuff that lets you build up to Regular Expressions, etc). Turnings thesis really gets to the heart of a computation, and those concepts build on that.

Then go learn LISP just to bend your brain some more.

Comp Sci is a fascinating subject, and you're off to a good start by thinking about what that Stack Overflow commenter meant - how are all languages similar? How do they differ? What's the underlying problem you're solving, and what are different ways of solving it? How do the tools you choose alter your solution?

Try writing something relatively simple (say, a program that plays Checkers or Tic-Tac-Toe and always wins) in a few different languages (start with ones you know, then learn some new ones to see how - if you've worked with Procedural or OO languages, try Functional ones).

It becomes useful to replace concepts such as equivalence relations (and other relations) with symbols in order to facilitate understanding, actually. I'm sure you've used the =, <, >, the greater-than-or-equal-to, or the less-than-or-equal-to symbols before. These symbols allow those that read equations, definitions, or proofs to quickly and unambiguously understand what is being discussed. If you end up studying higher math for a while, you become familiar and comfortable with this style of notation.

Interestingly, notation like this and the thought process it represents is important in understanding the structure of mathematical logic, forms a large part of the basis of automata theory (aka why you're able to enjoy complex technology, like computers), and may hold key insights into the nature of consciousness and sentience itself.

If you've got the stomach for the notation, wide worlds of fascinating information await!

I too love fun math[s] books! Here are some of my favorites.

The Number Devil: http://www.amazon.com/dp/0805062998

The Mathematical Magpie: http://www.amazon.com/dp/038794950X

I echo the GEB recommendation. http://www.amazon.com/dp/0465026567

The Magic of Math: http://www.amazon.com/dp/0465054722

Great Feuds in Mathematics: http://www.amazon.com/dp/B00DNL19JO

One Equals Zero (Paradoxes, Fallacies, Surprises): http://www.amazon.com/dp/1559533099

Genius at Play - Biography of J.H. Conway: http://www.amazon.com/dp/1620405938

Math Girls (any from this series are fun) http://www.amazon.com/dp/0983951306

Mathematical Amazements and Surprises: http://www.amazon.com/dp/1591027233

A Strange Wilderness: The Lives of the Great Mathematicians: http://www.amazon.com/dp/1402785844

Magnificent Mistakes in Mathematics: http://www.amazon.com/dp/1616147474

Enjoy!

How to Create a Mind: The Secret of Human Thought Revealed By Ray Kurzweil. Ray is world renown for predicting the outcome of upcoming technologies with stunning accuracy. Not through psychic powers or anything, but through normal predictive means. He predicted when the first machine would be capable of beating the best chess player in the world. He is predicting that we will approach what is called the technical singularity by 2040. Its amazing. He is working with Google on a way to stop aging, and possible reverse it one day. Something I recommend for sure.

EDIT: Books are awesome

You know, a fantastic book is Programming Game AI By Example. It's fantastic for learning about AI, but the author also put a lot of effort into the code, so you can learn a lot about general game design from it as well. Well worth the price. http://www.amazon.com/Programming-Game-Example-Mat-Buckland/dp/1556220782 . You can download the code samples from the author's website to see what I mean. It is only 2D, so it won't help you with collision detection and some of the more 3D specific topics, but the core of the engine can be applied to anything.

One thing that is really important is to realize that there's no silver bullet. Every design decision has its benefits and its trade offs. It's easy to fall into the trap of overthinking your design, especially for personal projects - it is more beneficial for you to try to write it and have to rewrite it because of a bad design decision than to spend months trying to come up with the perfect architecture. That's not to say that you should ignore design, but rather that once you think you have a good idea, try it out, experiment, see what works and what doesn't. If you focus on having a modular design to your software, you'll find that rewrites get easier and easier.

I watched the entire course of Data Structures and Algorithms by Richard Buckland (UNSW) and thought it was excellent.
http://www.youtube.com/playlist?list=PLE621E25B3BF8B9D1

There is also an online course by Tim Roughgarden (Stanford) currently going on. It's very good but I don't know if you can still sign up.
https://class.coursera.org/algo

Topcoder.com is a fun place to test your skills in a competitive environment.

That being said, based on the description you are interested in things which don't usually fit into algorithms books or courses. Instead, you might want to look into machine learning and maybe even NLP. For example Pattern Recognition and Machine Learning by Bishop and Foundations of Statistical Natural Language Processing by Manning & Schuetze are great books for that.

In Search Of Schrodinger's Cat by John Gribbin is a very readable physics and quantum physics history sketch. Might be slightly dated now, although I can't think of anything directly contradicted by recent work. Then again, I'm not actually a physicist :)

The Quark and the Jaguar is quite a bit more complicated, but still quite accessible to the layperson and has a lot of interesting stuff.

Slightly less sciency, more maths/logic/computation is Gödel, Escher, Bach: An Eternal Golden Braid

A Guinea Pig's History of Biology is pretty much what the title says, although there's an awful lot about fruit-flies too. Quite a good review of the history of biological experimentation, especially genetics.

H2O: A Biography of Water from a previous editor of Nature, covers water across a variety of fields. The second half of the book is mostly a rant about cold fusion and homoeopathy though, from what I recall, but the first half makes up for it.

Most general-audience things by Richard Feynman are well worth the read. He's got some great physics lectures, and his autobiography (Surely You're Joking, Mr Feynman?) is fun, but more for the anecdotes than the science.

Those are off the top of my head. If its something in a particular field, I might have some other ideas I'm currently forgetting.

I don't know where you've been looking, but Bayesian networks have been around long enough that they are covered quite well in textbooks. The very popular AI: A Modern Approach (Russell & Norvig) has a good overview of the basics and it is very well written with plenty of examples, as far as I recall. If you really want to get in depth, the "bible" on Bayesian Networks is the fairly recent textbook Probabilistic Graphical Models (Koller & Friedman). I'd recommend finding PDF samples or something before you buy them, of course. And don't worry if you feel some Bayesian Network stuff is over your head ... this is mostly graduate-level CS stuff, so you might just need to be patient =P

Asimov's books went even farther than that. Don't read if you don't want to be spoiled on his most famous scifi series.

[Spoiler](#s "Because Law 1 had the robots take care of humans, the first AIs decided to go out and commit genocide on every alien species in the universe, just so they couldn't compete with humans in the far future.")

AI safety is hard. Thankfully, if you care about actually doing good in real life, there are organizations out there working on this kind of thing. Machine Intelligence Research Institute does research on friendly AI problems; the Center for Applied Rationality promotes increasing the sanity waterline in order to increase awareness of the unfriendly AI problem; the Future for Humanity Institute works on several existential risks, including AI safety.

If you want to learn more about this topic in real life, not just in fiction, then I highly recommend Nick Bostrom's Superintelligence, a book that goes into detail on these issues while still remaining readable by laymen.

This is a great book. The other book that is a bit less mathematical in nature, and covers similar topics, is Introduction to Statistical Learning. It is also a good one to have in your collection if you prefer a less mathematical treatment. https://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370

100x though, that's a bit much :) If you read effectively and take notes effectively, you should only have to go through this book with any depth 1 time. And yes, I did spend time learning how read books like this, and it's worth learning!

piggybacking on what /u/T4IR-PR said, the best book to attack the science aspect of AI is Artifical Intelligence: A Modern Approach. It was the standard AI textbook when I took the class and it's honestly written very well - people with a basic undergraduate understanding of cs/math can jump right in and start playing with the ideas it presents, and it gives you a really nice outline of some of the big ideas in AI historically. It's one of the few CS textbooks that I recommend people buy the physical copy of.

Note that a lot of the field of AI has been moving more towards ML, so if you're really interested I would look into books regarding that. I don't know what intro texts you would want to use, but I personally have copies of the following texts that I would recommend

• Machine Learning (Murphy)
  
• Deep Learning Book (Goodfellow , Bengio)
  
  and to go w/ that
  
  
• All of Statistics (Wasserman)
  
• Information Theory (Mackay)
  
  for some more maths background, if you're a stats/info theory junky.
  
  After all that, if you're more interested in a philosophy/theoretical take on AI then I think Superintelligence is good (I've heard?)

Yes, I believe it is very possible.

After reading [Superintelligence] (https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0198739834/ref=sr_1_1?s=books&ie=UTF8&qid=1479779790&sr=1-1&keywords=superintelligence), it is very likely that we may have whole brain emulation as a precursor to true AI. If we are on that path, it makes sense that we would be running tests in order to remove AI as an existential threat to humanity. We would need to run these tests in real, life-like simulations, that can run continuously and without detection by the emulations themselves in order to be sure we will have effective AI controls.

Not only could humans run these emulations in the future (past? present?), but the Superintelligent agent itself may run emulations that would enable it to test scenarios that would help it achieve its goals. By definition, a Superintelligent agent would be smarter than humans and we would not be able to detect or possibly even understand the level of thinking such an agent would have. It would essentially be our God with as much intellectual capacity beyond us as we have above ants. Time itself could run at nanosecond speeds for the AI given enough computational resources while we experience it as billions of years.

So who created the AI?
Idk, but that was not the question here...

> Hey /r/learnprogramming i've browsed this subreddit a couple times and completed, and started a few courses like Codecademy... I'm looking to get some sort of job in this type of field by next year.

You should be aware that that's a pretty aggressive goal. Consider that people spend 4+ years of their life, full time, at a university, and are only then burgeoning on qualified for an entry level web-development position.

> if not, I will still want to hear what you guys recommend on what I should do to progress

At this point in your life, focus on learning stuff that interests you. If that's technology: great. If you are interested in technology, you could do worse than learning the syntax of one (or hell, even a few) different programming languages. You sound like you're already well on your way here.

> I'm also very interested in machine learning, so if you can recommend a path to learning it

Heh.. learn computer science first ;P. You'll also need to learn some limits/infinite-series, linear alegbra, calculus, and probability/statistics. At 15, that will keep you damn busy. If you're really motivated, pick up a copy of Russel and Norvig's very excellent book. That should give you some idea of what you're getting yourself into.

Lastly and most importantly, screw everything I just said along with what I (or anyone else) thinks. If you really want to learn something, do it. At the end of the day, it's on your shoulders, regardless of how insurmountable the task actually is.

At 10 years of age, I learned x86 assembly programming because I really wanted to write computer viruses. Everyone I asked told me that (assembly) would be way too hard for a kid. Screw all those people, I did.

Your post has too little context/content for anyone to give you particularly relevant or specific advice. You should list what you know already and what you’re trying to learn. I find it’s easiest to research a new subject when I have a concrete problem I’m trying to solve.

But anyway, I’m going to assume you studied up through single variable calculus and are reasonably motivated to put some effort in with your reading. Here are some books which you might enjoy, depending on your interests. All should be reasonably accessible (to, say, a sharp and motivated undergraduate), but they’ll all take some work:

(in no particular order)
Gödel, Escher, Bach: An Eternal Golden Braid (wikipedia)
To Mock a Mockingbird (wikipedia)
Structure in Nature is a Strategy for Design
Geometry and the Imagination
Visual Group Theory (website)
The Little Schemer (website)
Visual Complex Analysis (website)
Nonlinear Dynamics and Chaos (website)
Music, a Mathematical Offering (website)
QED
Mathematics and its History
The Nature and Growth of Modern Mathematics
Proofs from THE BOOK (wikipedia)
Concrete Mathematics (website, wikipedia)
The Symmetries of Things
Quantum Computing Since Democritus (website)
Solid Shape
On Numbers and Games (wikipedia)
Street-Fighting Mathematics (website)

But also, you’ll probably get more useful response somewhere else, e.g. /r/learnmath. (On /r/math you’re likely to attract downvotes with a question like this.)

You might enjoy:
https://www.reddit.com/r/math/comments/2mkmk0/a_compilation_of_useful_free_online_math_resources/
https://www.reddit.com/r/mathbooks/top/?sort=top&t=all

Thanks for the heads up -- I'll definitely look more into Hassabis, sounds like an incredibly interesting guy with his plunge into neuroscience.

Thx, I went to a few universities and picked up several graduate coursebooks on AI, and also went through some online and conventional book sources. On Intelligence really opened my eyes to the power of hierarchical learning, and the mechanics of cortical hierarchies. Absolutely fascinating stuff.

Hahaha and yeah, I agree that the point of games is not to just kill the player. Despite the "adversarial" nature of most AI enemies, they're actually teachers, gently guiding the player towards more nuanced strategies and better reactions.

Yeah. If you want to be good at like programming-programming I would read this book and do all of the projects: https://runestone.academy/runestone/books/published/fopp/index.html If you take like algorithms class you will probably get to use python.

If you want to be good at data analytics I would read "R for data science" if you want to use R. If you learn python people like this book for data science learning https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=pd_sbs_14_2/145-5658251-1609721?_encoding=UTF8&pd_rd_i=1491962291&pd_rd_r=4e33435c-cc98-4256-9c50-6050e79b7803&pd_rd_w=ejSx8&pd_rd_wg=Ter1m&pf_rd_p=d66372fe-68a6-48a3-90ec-41d7f64212be&pf_rd_r=3X23DYAJ2ZMCKP9AA1Z4&psc=1&refRID=3X23DYAJ2ZMCKP9AA1Z4 .

These books are kind of different though. The python book is much more focused on theory and will help you less in the workplace if you aren't actually building predictive models (at least I think based on table of contents).

Superintelligence

Good book.

I think of the human mind as a very specific intelligence designed to meet the demands of a natural life. A tailor made intelligence that is ultra specific seems like an incredibly difficult thing to recreate. I wouldn't be surprised if after AGI was created, it proved that our brains are both works of art, and only useful in specific areas.

They say a Philosopher is comparable to a dog standing on it's hind legs and trying to walk. Our brains are not setup to think about big problems and big solutions. Our brains are very specific. So, certainly, we shouldn't be using it as a model to build AGI.

As far as self awareness, I don't think we understand what that is. I think the seed AI's we have are already self-aware. They just have a very basic drive which is entirely reactionary. We input, it outputs.

It's not that if we connect enough dot's it'll suddenly come alive like Pinocchio. More, it will gradually wake up the more complex the overall program becomes.

Some reading I'd recommend.

Don't be scared off by his masters in theology - theology as an academic subject is a very relevant historical study into the psychology of man (and if it helps legitimize the author at all, the South Park guys are fans). The book is basically about psychoanalysis and the problem of identity. I'm a physics lover, engineer by trade, rationalist to the bone, and it gets my stamp of approval for making logical arguments. I've taken up an interest in neuroscience as well, to which I'd recommend this book. For me, those two books are approaching similar ideas from opposite directions.

Good luck broseph.

What you need to learn depends on exactly what you want to do. If you want computer assistance to help you beat your best friend at correspondence chess, then getting AI to work for you may be as simple as buying or downloading a ready-made off-the-shelf program.

AI is a very broad label that applies to a lot of different things. If you want to write your own chess-playing program, then you need to know about binary trees, depth-limited depth-first search, alpha-beta pruning, and heuristics. These things are great for playing chess and other games that resemble it, but are rather useless for other things that people also call "AI", like computer vision or natural language processing.

My advice is to start by learning programming, practicing your programming skills on less challenging problems, and then working up to whatever you want to do. This subreddit's FAQ will help you.

Alternatively, you can buy a good introductory AI textbook, such as Artificial Intelligence: A Modern Approach. However, before you buy an expensive textbook, be warned: If you don't already have a decent grasp of how programming works, the information is likely to be somewhat overwhelming and not very useful.

That's not a bad idea at all - I used EM way back (like 2002) for natural language processing, still remember it a bit, but dang gonna have to brush up :) Thx for the pointer!

Edit: Haha just realized I have that book! Recognized it from the cover shot on amazon :)

I hate to say this but you do not have a very good understanding as to what "Math" actually is... Mathematical systems are based on axioms. Math itself is just the logic that defines a system (i.e. the system must be consistent, etc) and it is the mathematical logic that backs up the system. In other words, you were looking for something of the scientific method in mathematics... It's mathematical logic.

You can even change the axioms all you want but if the result leads you to contradictions, then it is inconsistent. Look at Gauss's work with non-euclidean systems for example. It is not that math is based on certain axioms but it does use axioms in the construction of systems.

To use you ethics example: It is one thing to say that such ethical axioms could exist (there are a lot of things that could exist) but coming up with a consistent set of ethical axioms (of sufficient size, etc, etc) or a mathematical ethical system, is a whole other ball of wax. While I can't prove that a consistent set of ethical axioms doesn't exist (enter problems of proving a negative here), the odds of such a systems existing is (very, very, very, ... ) low by my estimation.

No mathematician worth their salt would say that mathematics describes "the real world", physics (for the most part) does that. Mathematics is just applying a specific process to different systems (different sets of axioms) and working out the result (consistency, figuring out theories in that system, trying to find mappings to other systems, etc).

I'm not sure this explanation has helped... It's hard to explain these ideas without a wall of text. But if you are interested in philosophy of mathematics, GEB is a good book and A Profile of Mathematical Logic is a great book but a little dense.

I'm sort of in the same boat as you, except with an aero and physics background rather than EE. My approach has been pretty similar to yours--I found the textbooks used by my alma mater, compared to texts recommended by MIT OCW and some other universities, looked at a few lists of recommended texts, and looked through similar questions on Reddit. I found most areas have multiple good texts, and also spent some time deciding which ones looked more applicable to me. That said, I'm admittedly someone who rather enjoys and learns well from textbooks compared to lectures, and that's not the case for everyone.

Here's what I gathered. If any more knowledgeable CS guys have suggestions/corrections, please let me know.

• Intro-ish: Structure and Interpretation of Computer Programs by Abelson & Sussman
  
• Math: Mathematics for Computer Science by Lehman et al.
  
• Computer Architecture: Computer Systems A Programmer's Perspective by Bryant & O'Hallaron
  
• Algorithms: Introduction to Algorithms by Cormen et al.
  
• Programming Languages: Programming Language Pragmatics by Scott
  
• Computational Theory: Introduction to the Theory of Computation by Sipser
  
• Operating Systems: Modern Operating Systems by Tanenbaum & Bos
  
• Networks: Computer Networks by Tanenbaum
  
• Compilers: Compilers by Aho et al.
  
• Security: Security Engineering by Anderson
  
• Type Theory: Types and Programming Languages by Pierce
  
• AI: Artificial Intelligence: A Modern Approach by Russell & Norvig
  
  Full disclosure: I haven't actually read more than the preface of any of those books. Software engineering topics are more directly applicable to me than CS topics right now, so here are some that I've actually started reading:
  
  
• The Pragmatic Programmer by Hunt & Thomas
  
• Clean Code by Martin
  
• Code Complete by McConnell
  
• Design Patterns by Gamma et al.

+1 for top-down learning approaches. There's so much work going on to democratize use of ML techniques in general software development, that, depending on where you want to go, there's little need to start with the classic theory.

IMHO, the classic ML literature suffers a bit from decades of theorists who never had the computing resources (or the data) to make big practical advances, and it tends to be overly dense and mathematical because that's what they spent their time on.

But really it depends on your goals. Which category do you fall into?

1. Get a PhD in math, study computer science, get a job as a data scientist at Google (or equivalent) and spend your days reading papers and doing cutting edge Research in the field.
   
   
2. Learn classic and modern ML techniques to apply in your day to day software development work where you have a job title other than "data scientist".
   
   
3. You've heard about Deep Learning and AlphaGo etc. and want to play around with these things and learn more about them without necessarily having a professional goal in mind.
   
   For #1 the Super Harsh Guide is, well, super harsh, but has good links to the bottom up mathematical approach to the whole thing.
   
   For #2 you should probably start looking at the classic ML techniques as well as the trendy Deep Learning stuff. You might enjoy:
   
   https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291
   
   as a place to start and immediately start playing around with stuff.
   
   For #3 any of the TensorFlow getting started tutorials are good, along with all of Martin Görner's machine learning/deep learning/TensorFlow "without a PhD" videos on YouTube. Here's one of the more recent ones:
   
   https://www.youtube.com/watch?v=vaL1I2BD_xY

I don't even think we'll get that far. I honestly believe that once we create proper A.I. it will snowball out of control in a matter of months and it will turn against us. Their communist plans are mere fever dream when it comes to A.I. "Well, if the robots are nice to us, don't destroy the human species, and actually are subservient to us, then our Communist fever dream could work"

Yeah, okay, it's like trying to decide whether you want chicken or fish for the in-flight meal while the plane is going down.



I recommend reading Superintelligence if you want to get more theroies about it.

> Last few weeks I got very interested in AI and can't stop thinking about it. Watched discussions of philosophers about future scenarios with AI, read all recent articles in media about it.

Most likely you heard about the superintelligence control problem. Check out (the sidebar of) /r/ControlProblem and their FAQ. Nick Bostrom's Superintelligence is pretty much the book on this topic, and I would recommend reading it if you're interested in that. This book is about possible impacts of AI, and it won't really teach you anything about how AI works or how to develop it (neither strong nor weak AI).

For some resources to get started on that, I'll just refer you to some of my older posts. This one focuses on mainstream ("narrow"/"weak") AI, and this one mostly covers AGI (artificial general intelligence / strong AI). This comment links to some education plans for AGI, and this one has a list of cognitive architectures.

I read The Annotated Turing by Charles Petzold while I was in high school and it really sparked my love for logic, math and computer science. So, as far as popular science books go, I can't not recommend it.

Right now I'm interested in programming languages, and I think TAPL is a great resource. The (relatively) new blog PL Perspectives is also pretty cool, and so is /r/ProgrammingLanguages.

If you're worried about not doing projects and participating in Kaggle competitions, why not do those things? They're pretty low risk and high reward. If you're feeling shaky on the theory, read papers and reference textbooks, take notes, and implement things that interest you. For deep learning stuff there are some good resources here: https://github.com/ChristosChristofidis/awesome-deep-learning. For more traditional methods you can't go wrong with Chris Bishop's book (try googling it for a cheaper alternative to Amazon ;): https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738.
Side projects can really help here, and the key is to use references, but don't just copy-paste. Think of something you'd like to apply machine learning to with a reasonable scope. Search google scholar/arxiv for papers that do this or something similar, read them, and learn the techniques. For reading research papers in an area where you're not extremely knowledgeable, use the references in the text or google things you don't know and make sure you understand so you could teach someone else. If you're interested in the topic and exhausted the references, go up the tree and use google scholar to find papers that list the one you're reading as a reference - you usually find interesting applications or improvements on the technique. You can also often find open source training data in the appendices of papers. Kaggle also has a ton of datasets, including obviously the ones they provide for competitions.

I agree this is a missing area. I've been working on some materials like recent videos on Transfer Learning https://studio.youtube.com/video/vbhEnEbj3JM/edit and One Shot learning https://www.youtube.com/watch?v=H4MPIWX6ftE which might be interesting to you. I'd be interested in your feedback. I also think books like https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=pd_lpo_sbs_14_t_1?_encoding=UTF8&psc=1&refRID=3829RHN356ZXBEBP0KF3 do a good job of bridging some of this gap. Reading conference papers is a skill that takes practice and a strong math background.

I was actually thinking this a few days ago about my dog. Having read The Singularity Is Near by Ray Kurzweil, there are a few sections devoted to uploading the brain and using technology to augment brain capabilities. What it boils down to is that the truly unique things about our brain are 'past memories', 'emotions', and 'personality'. Every thing else is the brain is just stuff that regulates our bodies and processes information.

If we take the personality, memories, and emotions of my dog, and improve on the other parts of the brain by adding better memory, speech recognition, etc. Then we might just be able to create another biological species that rivals our intelligence.

We already are making the assumption that technology will make humans more advanced, the same thing should eventually apply to all other biological animals as well. (Except Mosquitos, of couse)

Anyone interested in AI should read Nick Bostrom's book, Superintelligence. Fair warning, it is very dense but rewarding.

One take away here is he did a survey of leading AI researchers who were asked to predict when General AI might arrive - the majority (~67%) believe it will take more than 25 years, interestingly 25% believe it might never happen. Source

Also, really great panel discussion about AI with Elon Musk, Bostrom, others.

It's even more interesting if you don't just think him as the standard insane genius trope, but realize that he is probably genuinely disturbed and conflicted about what he's created and what to do with it.

Trying not to be spoiler-y here for people who haven't seen the movie but there are probably a lot of practical and metaphysical questions weighing on him. Is an AI truly a conscious creature? Does it have wants? If so, what would an AI want? Given that its social manipulation, long-game planning, and deception abilities are off the charts how could we ever be sure that what it told us was the truth? Does it have any moral considerations toward humans? How would we ever be able to contain it if we needed to? And if it is a conscious creature worthy of moral consideration then what are the moral ramifications of everything he's done with it so far?

Really interesting stuff. For those inclined I recommend checking out the book Superintelligence by Nick Bostrom as it explores these themes in depth.

> But why should a deterministic "choice" be 100% the good one? Or is it, say, deterministically the good one in only 70% of the cases?

If you'd actually like to learn about the ways deterministic and stochastic decision policies work, I recommend AI: A Modern Approach. If you're just saying that a deterministic decision policy of tractable size won't make the optimal choice in all real-world situations, I agree.

But we're not arguing about effectiveness. I can't really discuss the effectiveness of different decision policies establishing a lot of mathematical-ish background; and they're irrelevant to the question of whether contra-causal free will can logically exist.

> what I know as a fact is that I indeed have a true freedom of choice

Sure... the exact same way you know the top yellow line is longer than the bottom one. If a very simple argument shows that your intuition is logically impossible, you should distrust your intuition.

> The truth is that the singularity could be reached but never realized as long as you don't connect that super-smart AI to anything.

A super-intelligent AI could probably convince a human to let it out of its confinement (Google The AI-Box Experiment for an exploration of this), but even failing that it might think of a way to break free that we can't even conceive of. Even if we literally didn't connect it to anything, that leaves us with no way to interact with it, so what was the point of developing it?

The reason I say human-based AI is less risky is because it would implicitly have human values. It wouldn't kill all humans so that we can't stop it from turning the planet into paperclips. Designing a friendly AI from scratch basically requires us to express human ethics in a way a computer can understand, which is not even close to a solved problem.

Nick Bostrom's Superintelligence is a pretty good exploration of the dangers of AI if you're interested in the subject, but it's a fairly difficult read. Tim Urban's articles on the subject are simpler, if much less in-depth.

You might be interested in a book called On Intelligence, by Jeff Hawkins. He describes something similar to your simulations idea, but he calls it a predictive hierarchical memory system (or something like that). It is a fascinating idea, actually, and makes a lot of sense.

I too suspect that speech is a central unifying aspect to what we call consciousness. A lot of AI guys seem to agree. There is a theory by Noam Chomsky (I think), called Universal Grammar. As I recall, he suspects that may be key to modern intelligence, and he suspects the genetic mutation for it happened about 70,000 years ago, which gave us the ability to communicate, and allowed Homo Sapiens to successfully move out of Africa. I've also read that mutation 70k years ago referred to as the cognitive revolution. But it seems everyone agrees that's when the move out of Africa began, and communication started; it's not just a Chomsky thing.

I hadn't heard of Lie Groups as well (and didn't look it up the first time you mentioned them) - they sound amazing!
Right now I'm mainly reading the Murphy Book after having finished Probabilistic Models of Cognition (which I enjoyed because I also always wanted to check out Scheme and has some great interactivity).

I suppose I'll have to put these books on the list, thanks! ;)

Because in any formal system with sufficient power (like modern mathematics) Gödel showed that it is possible to construct a statement that is true but cannot be proven. IIRC the statement boils down to "This statement is not a theorem". If it is a theorem (meaning it can be proven within the system) then it is true and so leads to a contradiction because it asserts that it is NOT a theorem. Assuming it is not a theorem does not lead to such a contradiction but then means that the statement is in fact true. So since one possibility leads to a contradiction while the other doesn't it must be that this statement is true but not a theorem (and therefore unprovable). If you are interested in this at a pretty informal level check out the fun and interesting book Gödel, Escher, Bach.

Well, meta-think about this: a "strong AI" like ourselves might seem mysterious, but consider the fact that our brains are really just nueral nets, which can be mathematically approximated. The process of learning and thinking, in the neurological sense, is really nothing a blind search of your cells for optimal solutions. Using a fairly simple set of rules (See Conway's Game), complicated, intelligent behavior emerges. So I'm not really sure what you mean, but I'm guessing you're implying that intelligence cannot be designed, it must emerge. And don't forget that a method of discovering algorithms, is itself, still "just an algorithm!" Cornell's Eureqa project can do this, to a degree. I think my most important point is this: Intelligence is different between individuals and even animals. Don't judge machine intelligence by it's resemblance to the human mind. Both are machines but they process data in profoundly different ways.

PS http://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567

edX.org has a few classes for their micromasters in artificial intelligence going right now until April 22nd or so. Though I think one is 3D modeling or something so I've completely ignored that. They are both free and you can access the course materials after the courses have ended, so you can watch the lectures, read the material, and take quizzes, but not receive a passing certificate or what have you. The two books for the Machine Learning course are both available online in pdf form for free.

Pattern Recognition and Machine Learning

The Elements of Statistical Learning

For the Artificial Intelligence course it's recommended to have:

Artificial Intelligence: A Modern Approach 3rd edition

I learned mostly through reading textbooks in University, but not many of the books we were assigned stood out as being particularly great. Here's a few that I did enjoy:

• Artificial Intelligence: A Modern Approach.
  
• Computer Organization and Design: The Hardware/Software Interface
  
• Algorithms (Sedgewick). I liked this much more than CLRS.
  
  To balance things out, I'll also give a list of books I thought were awful:
  
  
• Discrete Mathematics with Graph Theory
  
• Fundamental of Database Systems
  
• Programming 32-bit Microcontrollers in C: Exploring the PIC32
  
• Software Engineering: A Practitioner's Approach
  
• Essentials of Theoretical Computer Science

I personally love Programming Game AI By Example. It gives lots of very usable examples in an entertaining and understandable way. It's pretty friendly for beginners and even offers a game math primer at the start of the book. However the examples still have a lot of meat to them and thoroughly explains some important AI concepts like state machines and pathfinding.

I would highly recommend to read the following book

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=mp_s_a_1_2?keywords=machine+learning&qid=1566810016&s=gateway&sprefix=machi&sr=8-2

I think it is the best one about ml/dl. Not sure that they already updated the tensorflow examples to tf 2.0 and keras.

And as tensorflow includes keras now, and has perfect pipeline for deploying your model, i think it is the perfect choice. :)

I can recommend this (free) course:
https://www.udacity.com/course/intro-to-artificial-intelligence--cs271

You certainly dont need a degree (it helps of course) but most you need is dedication and perserverance.

In regards to math you need a good (more than)-basic understanding of statistics, linear algebra, algorithms and you also need to develop good data analysis skills.

If you want to get serious with AI this book is fantastic (atleast it helped(still does) me alot): https://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597/ref=sr_1_2?ie=UTF8&qid=1506722436&sr=8-2&keywords=artificial+intelligence+a+modern

and by the way check out this thread maybe:
https://www.reddit.com/r/artificial/comments/6cnlr6/monthly_how_to_get_started_with_ai_thread/

Well, there are a lot of potential dangers to creating kittens with greater brainpower than we could imagine. It's essentially a superintelligent AI problem: it's tricky to create conditions that would allow us to create something more powerful than ourselves without running into a large host of problems where the AI wouldn't slip into a mode that isn't value-alligned with us. Maybe with the right types of check-boxes, it could be done, though this runs into a second problem:

​

I'm not at all sure that you can create superintelligent kittens and be at all sure that you can still call them kittens. Any noun is an idea with other ideas attached to them, and if you change any of those defining ideas enough, language, or human convention, tends to call that original noun by a different name.

​

If the superintelligent kittens would rightly be called something other than kittens, I suspect there would be no checkboxes that would point to them, or allow them to be designed or created.

​

Further, there are always ethical dilemmas that surround intelligent species, and the willy-nilly creation of them, especially with the intent of placing them into service, especially if doing so would cause them to suffer.

​

Anyhow, thanks for the submission, I enjoyed playing with it. :D

It really depends on your comfort and familiarity with the topics. If you've seen analysis before you can probably skip Rudin. If you've seen some functional analysis, you can skip the functional analysis book. Convex Optimization can be read in tandem with ESL, and is probably the most important of the three.

Per my other comment, if your goal is to really understand the material, it's important you understand all the math, at least in terms of reading. Unless you want to do research, you don't need to be able to reproduce all the proofs (to help you gauge your depth of understanding). In terms of bang for your buck, ESL and Convex Optimization are probably the two I'd focus on. Another great book Deep Learning this book is extremely approachable with a modest math background, IMO.

As I've said many times in this thread, I do not think that Watson is general AI. Watson would not be able to do any of these things today. Please don't ask me to repeat this again.

The point is that the ability to break questions into relevant sub-questions is intelligence. Watson does this. It does not do it as well as a human but it still does it scarily well. There's nothing "just" about recursing your way to intelligence, the complexity involved is staggering.

>Understanding the equation is not the same as having to ability to ask yourself "Can I use this equation?", and having the ability to ask yourself "Can I use this equation?" is actually quite useless if you also have to ask yourself if you can use every other possible equation in existence (of which there are infinite).

Understanding the usage of an equation is being able to answer the question "What can I use this equation for?" Understanding how to derive an equation is being able to answer the question "How do I derive this equation?" When we say someone understands an equation we really mean understanding the usage of an equation, understanding how to derive the equation, and a whole host of other types of understanding. Understanding a myriad of other foundation concepts is also implied when we say someone understands an equation. It is a huge tangled web you have to traverse to solve the simplest of problems. We do it quickly and without being conscious of it but there isn't some magical "understand" symbol which allows us to skip the whole process.

I think I'm just doing a terrible job at explaining myself in general. I really would recommend the book GEB, he explains it amazingly well.

Classic Russel & Norwig textbook is definitely worth reading. It starts from basics and goes to quite advanced topics:
http://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597/
Udacity has AI class that follows some chapters of that book.

Murphy's textbook builds ML from the ground up, starting from basics of probability theory:
http://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020/
(I see, it was already recommended)

Coursera has the whole machine learning specialization (Python) and a famous ML class by Andrew Ng (Matlab).

I hope it helps. Good luck!

Our co-founder Eliezer Yudkowsky invented the entire approach called "Friendly AI," and you can read our original research on our research page. It's interesting to note that in the leading textbook on AI (Russell & Norvig), a discussion of our work on Friendly AI and intelligence explosion scenarios dominates the section on AI safety (in ch. 26), while the entire "mainstream" field of "machine ethics" isn't mentioned at all.

A little late to the party, but...

Runestone: Arena 2

I spent most of the week working on music and sound, but managed to also work on UI and spells.

• Town music
  
  New screenshots:
  
  
• Equipment view
  
  
• Spells
  
  Next week I'm hoping to rework the battle AI to use Goal-Oriented Action Planning. I'll use a graph search algorithm to plan for actions that result in a desirable world state with the shortest way to get there. Seems like it might be too CPU intensive for Flash, but we'll see when we get there. Also started reading Artificial Intelligence: A Modern Approach - seems very interesting, but I doubt it'll be much help here.

Here's some lighter reading I've liked:

• Analog Circuit Design: Art, Science and Personalities
  
• The Chip : How Two Americans Invented the Microchip and Launched a Revolution
  
• Gödel, Escher, Bach: An Eternal Golden Braid
  
• Feynamn Lectures on Computation
  
  Readable, but more hardcore
  
  Hardware:
  
  
• The Design of CMOS Radio-Frequency Integrated Circuits, Second Edition
  
  
  Mathy:
  
  
• A New Kind of Science. I liked this book a lot, it is worth the price just for the interesting pictures. Make up your mind for yourself, and don't let negative reviewers unduly influence you.
  
• Probability Theory: The Logic of Science
  
• Information Theory, Inference and Learning Algorithms
  
  
  Programmy:
  
  
• Mastering Regular Expressions
  
  Unixy:
  
  
• Unix Power Tools
  
  
  Off the wall:
  
  
• Molecular Biology of the Cell
  
  

Stuart Russell, the man who literally wrote the book on AI, is concerned.

Plenty of prestigious people on the cutting edge of the research in the field are concerned.

Just because you've only heard the household-name-level famous people talk about it, it doesn't mean that the genuine, in-the-thick-of-it experts aren't concerned either.

As for the 10~20 years figure, you're right that it is unlikely that AI will be made in that timeframe. However, the claim was merely that it is possible to create with enough resources in that timeframe, which I think is reasonable. Since you care about what the experts think, here is a summary of the best information we have about when they think this will happen.

>Median estimates for when there will be a 10% chance of human-level AI are all in the 2020s (from seven surveys).

>Median estimates for when there will be a 50% chance of human-level AI range between 2035 and 2050 (from seven surveys)

___
AI: A Modern Approach is the best textbook on AI by far

Here's the inevitable recommendation for Gödel, Escher, Bach (Amazon page, so you can see the reviews).

Synopsis:

>Twenty years after it topped the bestseller charts, Douglas R. Hofstadter's Gödel, Escher, Bach: An Eternal Golden Braid is still something of a marvel. Besides being a profound and entertaining meditation on human thought and creativity, this book looks at the surprising points of contact between the music of Bach, the artwork of Escher, and the mathematics of Gödel. It also looks at the prospects for computers and artificial intelligence (AI) for mimicking human thought. For the general reader and the computer techie alike, this book still sets a standard for thinking about the future of computers and their relation to the way we think.

>Hofstadter's great achievement in Gödel, Escher, Bach was making abstruse mathematical topics (like undecidability, recursion, and 'strange loops') accessible and remarkably entertaining. Borrowing a page from Lewis Carroll (who might well have been a fan of this book), each chapter presents dialogue between the Tortoise and Achilles, as well as other characters who dramatize concepts discussed later in more detail. Allusions to Bach's music (centering on his Musical Offering) and Escher's continually paradoxical artwork are plentiful here.

It may be strange, but during the biology and nature-of-thought-related sections of GEB I decided to read the neurology chapters of Gray's Anatomy (no, not Grey's Anatomy). It's pretty heady and slows you down quite a bit, but it results in a really interesting mix of deep biological knowledge about the structure of neurons and functioning of the nervous system with GEB's higher-level, cognition-focused discussion.

Note that that's the 40th, British edition of Gray's Anatomy. There are cheaper ones if you don't need the most up-to-date version, including leather-bound reprints of the classic 1901 American reprint. I doubt the old versions have much accurate information about neurology, however.

Ah you mean TV & Movie AI :) I'm not sure if we'll ever get there, but superintelligent AI is reckoned to be only a short hop away from General Purpose AI. There are a series of blog posts on waitbutwhy.com which are the most cogent I've ever seen or read on the subject. A long read, but a must read if you're at all interested in the state of the art.

However, in one of the posts you'll find the results of a survey of domain experts, about when AI will happen, probabilistically. From Nick Bostrom an autodidact that wrote Superintelligence Also a leading thinker about AI at Oxford university. The earliest estimate of true AI is 2025, (25%) then 2040, (50%) and 2060, (75%) now those percentages are from memory but the years should be right. Go check the post. But that's allegedly what AI experts thought when asked at an AI conference.

Google's deepmind are essentially running "an Apollo program for AI" Their words, and have about 600 academics on staff full time working on the issues. They already beat the best human player at Go, and until they did that it was an event thought to be 10 years away. This is coming, it's just a matter of when.

I'm a general engineer myself, with a side interest in computer science. Szeliski's book is probably the big one in the computer vision field. Another you might be interested in is Computer Vision by Linda Shapiro.

You may also be interested in machine learning in general, for which I can give you two books:

• Heavily Theoretical: Christopher Bishop - Pattern Recognition and Machine Learning (Very much research oriented. Assumes you're decent at calculus.)
  
• Heavily Practical: Toby Segaran - Programming Collective Intelligence (A shallow look at the most common practical algorithms straightforwardly applied to real life examples.)
  
  I see you're interested in compilers. The Dragon book mainly focuses on parsing algorithms. I found learning about Forth implementations to be very instructive when learning about code generation. jonesforth is a good one if you understand x86 assembly.

That was a phenomenal article. Extremely long (just like every piece of writing associated with Hofstadter), but excellent nonetheless. I'm admittedly sympathetic to Hofstadter's ideas, not the least of which because of my combined math/cognitive science background.

There was a quote by Stuart Russell, who helped write the book on modern AI, that really stood out to me, and I think expresses a lot of my own issue with the current state of AI:

“A lot of the stuff going on is not very ambitious... In machine learning, one of the big steps that happened in the mid-’80s was to say, ‘Look, here’s some real data—can I get my program to predict accurately on parts of the data that I haven’t yet provided to it?’ What you see now in machine learning is that people see that as the only task.”

This is one of the reasons I've started becoming very interested in ontology engineering. The hyperspecialization of today's AI algorithms is what makes them so powerful, but it's also the biggest hindrance to making larger, more generalizable AI systems. What the field is going to need to get past its current "expert systems" phase is a more robust language through which to represent and share the information encoded in our countless disparate AI systems. \end rant

If you really like this stuff, I would highly recommend two textbooks:

For the communications topics, reliability, optimization etc, ditch Leon-Garcia and pick up this book by Trivedi

If you're interesting in Machine Learning like I am, then this book by Bishop is fantastic. You can find both in the Engineering library I believe.

There's definitely a steep learning curve to get to the mindset of being productive with it. I really enjoy Norvig's "Case Studies" book. I feel like you're right in some ways though... LISP is supposed to be able to be extended even in a language sense but it is just not that intuitive to do it. I have heard interesting things about Perl 6 in this regard but I haven't had time to play with that yet... maybe when i finally completely finish school :)

I would mention Bishop's Pattern Recognition and Machine Learning (https://www.amazon.fr/Pattern-Recognition-Machine-Learning-Christopher/dp/1493938436) as well as Hastie's Elements of Statistical Learning (https://www.amazon.fr/Elements-Statistical-Learning-Inference-Prediction/dp/0387848576/).

Sure they're not that easy to delve into, but they'll give you a very strong mathematical point of view,

good luck !

If anyone's interested, this book here is a really good free introductory textbook on machine learning using R. It has really good reviews that you can see here

Also if you need answers to the exercises, they're here

The textbook covers pretty much everything in OP's article

I'm not an ME, but these are some of my (more computery-ish) favorites that might have general engineering appeal:

The Difference Engine - proto-steampunk!

Gödel, Escher, Bach - essays on logicians' wet dreams.

Anathem - mathy and fantastic.

House of Leaves - you'll either love it or it will just piss you off...

Also, anything by Phillip K Dick or Kurt Vonnegut. And Feynman's (first) autobiography is definitely a must.

Google is your friend. Some good links i found by searching for queries like "where to start if i want to study AI" or "good books about AI":

• https://www.reddit.com/r/artificial/comments/1rg206/where_should_i_start_if_i_want_to_get_into_ai/
  
• https://www.quora.com/How-should-one-start-studying-Artificial-Intelligence-if-he-is-a-college-sophomore
  
• https://www.quora.com/What-are-some-good-books-on-Artificial-Intelligence
  
• https://www.quora.com/What-math-do-I-need-to-learn-for-AI-and-ML-Can-you-recommend-some-good-books-courses-for-beginners
  
  The side bar of https://www.reddit.com/r/MachineLearning/ can also be a good starting point (Machine Learning is propably the most important branch of AI).
  The standard intro book is http://www.amazon.com/Artificial-Intelligence-Modern-Approach-Edition/dp/0136042597

The Starcraft Broodwar API has source code for a bunch of bots from the annual competition at AIIDE. You can find them here. They use a variety of techniques that will help you set you in the right direction.

I'd recommend this book too if you're interested in AI. It's the most comprehensive survey of the most common techniques used in the industry today.

Good luck!

Not specific to algorithms or even to CS, but Douglas Hofstadter (Gödel, Escher, Bach, I Am a Strange Loop) touches on many of the logical fundamentals in a relatively layman-digestable manner.

I wouldn't call him easy reading compared to Sagan or Kaku, and don't know a "pop computer science" equivalent to those two, but you definitely don't need a CS or math degree to get through GEB. Whether it's on-topic enough here is definitely questionable.

---

Edit: I haven't read it, but from the description this one by Thomas Cormen looks like it might be close to what you're looking for: Algorithms Unlocked.

"This is a unique book in its attempt to open the field of algorithms to a wider audience. It provides an easy-to-read introduction to an abstract topic, without sacrificing depth."

From the TOC, it looks like it's probably fairly light on math but gets into code or pseudocode relatively quickly. I still wouldn't call it pop-CS, but if that sounds like a fit, maybe give it a shot!

I don't know who is doing PR for this book but they are amazing. It's not a good book.

My review on Amazon:

> The most interesting thing about this book is how Bostrom managed to write so much while saying so little. Seriously, there is very little depth. He presents an idea out of nowhere, says a little about it, and then says [more research needs to be done]. He does this throughout the entire book. I give it two stars because, while extremely diluted, he does present an interesting idea every now and then.

Read this or this or this instead.

Reductionism is important, but pure reductionism denies the existence of emergent phenomena (phenomena that depend on collective behavior of many simpler things). A very enjoyable book that covers this and many other topics at a popularly-accessible level is
Gödel, Escher, Bach: an Eternal Golden Braid. First published in the late 1970s, GEB is still delightfully fresh and exciting although a few minor elements are dated (e.g. computers now can beat humans at chess).

Good point. If you want to read about superior alien intelligence, check out http://www.amazon.com/gp/aw/d/0199678111/ref=mp_s_a_1_1?qid=1420566893&sr=8-1 Our civilization is currently giving birth to an intelligent life form beyond our imagination.

May I recommend a book I used in college:

http://www.amazon.com/Artificial-Intelligence-Modern-Approach-2nd/dp/0137903952/ref=sr_1_2?s=books&ie=UTF8&qid=1396106301&sr=1-2&keywords=Artificial+Intelligence%3A+A+Modern+Approach

There is a newer (and more expensive) edition (3rd), but frankly it isn't necessary.

This book will give you a very broad and thorough introduction to the various techniques and paradigms in AI over the years.

As a side note, look into functional programming languages, Haskell, Prolog, Lisp, etc.

Good luck, my friend!

This is SO important. We should be doing this faster than China.

A branch of artificial intelligence is that of breeding and gene editing. Selectively selecting for genetic intelligence could lead to rapid advances in human intelligence. In 'Superintelligence: Paths, Dangers, Strategies', the most recent book by Oxford professor Nick Bostrum, as well as his paper 'Embryo Selection for Cognitive Enhancement', the case is made for very simple advances in IQ by selecting certain embryos for genetic attributes or even, in this case, breeding for them, and the payoff in terms of raw intelligence could be staggering.

No, you're making the jump from mathematics as a formal system to mathematics as The Universe again. If you're bringing up Godel in a philosophical context but haven't read [GEB] (http://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567/ref=sr_1_1?ie=UTF8&qid=1344628050&sr=8-1&keywords=GEB), you really should. It sounds like you have a genuine interest in this stuff. The jump you're doing is even addressed in the second chapter.

You're doing what Hofstadter describes as an isomorphism -- you recognize a similarity or pattern between a formal system (mathematics) and reality (physics), so you assign an interpretation of the symbols as they relate to reality. The way you manipulate the symbols may tell you something about the (isomorphic) reality concepts it mirrors, but, as Hofstadter put it, "what portion of reality can be imitated in its behavior by a set of meaningless symbols governed by formal rules"? You really don't know. For all you know, "absolute zero" is just a mal-formed, meaningless symbolic string in the context of physical reality (even though it's a well-formed meaningful string in pure mathematics).

At the end of the day, our ability to think in terms of isomorphisms -- we assign symbols or thoughts to represent other symbols or thoughts, even the thoughts themselves self-referentially -- is one of the beautiful capabilities of the human brain. Under this interpretation, your sense of "I" or "ego" is just the symbolic machinery of your brain being self-referentially pointed onto itself. In your case, the symbolic machinery of pure mathematics has certain properties (e.g. Godel's incompleteness theorum), but you need to be careful when you're asserting properties of the symbolic system versus properties of the isomorphism you assign as an interpretation.

The Count of Monte Cristo (unabridged)

• Took me a year of having it on my shelf before I started it. It's as awesome as people say it is. Yes, it's huge and long but the story so far (even after I have seen the movie) is encapsulating.
  
  Super Intelligence by Nick Bostrom
  
  
• Interesting to see the negative affects of Artificial Intelligence, but it reads like a high school term paper...though, I don't read non-fiction much so that could just be me.

Wow, awesome list, I've read way to many of those books.

You need to add G.E.B.; that book is amazing and fits in quite well on that list.

Very interesting. You might be interested in

https://en.m.wikipedia.org/wiki/On_Intelligence

https://www.amazon.de/Intelligence-Jeff-Hawkins/dp/0805078533

It was pretty popular and was read among AI researchers alot. It's easy to understand.

Consciousness prepare to be understood!

Absolutely!

Math is everywhere and it's just about seeing the patterns emerge from simplicity. My knowledge on this topic has mainly been from my own work in Artificial Life and encoding AI genetic knowledge combined with my general interest in biological patterns (which are everywhere in nature) but the first thing that got many things to click for me was playing around with Turtle Logo in high school that is all about using simple constructs to create amazingly complex structures (i.e. one, two - look familiar?).

Sadly I don't work on my AI research anymore due to ethical concerns so I'm a bit out of date but I'd highly recommend the following that weren't mentioned in the original post though:

• "Godel, Escher, Bach: An Eternal Golden Braid" Book
  
• "The Fractal Geometry Of Nature" Book
  
• Procedural Generation
  
• Gene Expression Programming

I'm currently listening to the audio book of Nick Bostrom's Superintelligence: Paths, Dangers, Strategies, so I'm kind of hyped on AI and their possible existential threat, right now. The Halo writers are greatly downplaying what is possible for a powerful superintelligence can do. Once in control of the Domain, and properly bootstrapped to godhood, Cortana wouldn't have need for the Guardians or any of the Promethean's infrastructure. She could just start converting matter into computronium or something even more exotic. Of course, that's way too un-fun and not adventure sci-fi. If the Halo writers wanted to combine Halo-lore with contemporary conjecture on AI doomsdays, Cortana should have started mass producing Composer platforms to convert all sentient life in the known galaxy into info-life and importing them all into the Domain where they can live in a never ending Utopia...on her terms, of course. Using ancient warships to enforce martial law is just too crude. The Guardians are a decisive strategic advantage, but just not nearly what a superintelligence can get away with.

Also, I'd like to note that according to real world AI theory, the Smart AI of Halo are not "true" AI. They are Emulated Minds, ie, their core architecture is based on high resolution scanning of human brains that is emulated via powerful software. I know that this is common knowledge amongst us, but I find it interesting that RL researchers do make a distinction between artificial machine intelligence and theoretical Full Mind Emulation.