Maybe someone else can comment as to whether or not these books work well without computer access but: Seven Languages in Seven Weeks: A Pragmatic Guide to Learning Programming Languages (Pragmatic Programmers) https://www.amazon.com/dp/193435659X/ref=cm_sw_r_awd_6rZSvb6X2M657
There's also a second book that has 7 more languages. These would give you a good overview of what's out there and looking at how different languages do things may be more pragmatic given your situation.
I'd read Seven Languages in Seven Weeks. It'll give you some insight into different languages. It'll make it easier for you to pick up new languages. Use the sites others have practiced to reinforce the book.
http://www.amazon.com/Seven-Languages-Weeks-Programming-Programmers/dp/193435659X
The author makes a point about cultivating the skill of learning new programming languages. I think 7 Languages in 7 Weeks is a great book for that.
I'm at work right now ( library is at home), but there's a few books I can recommend. there's no one great path though, it'll be "find a hole in knowledge, fill it, move forward until you find another hole in your knowledge".
First, learn a programming language, and master it. Then learn to abstract that. Use this book as a good way to abstract. You'll come to recognize two basic abstraction: first, every language does all the same things, in just a little different way. The book acts sort of as a Rosetta stone for comp sci. Second, some languages are better than others for some things, and for AI, none of that matters, just pure processing power. I started the project in c# ( very simple for prototyping and testing, not super efficient) , and then swapped to C++ when I started working the outcomes into a game world to see evolutionary behavior better ( using the unreal engine) .
Next,Hofstadter has two books worth reading, "I am a strange loop" and "Gödel, Escher, Bach: An Eternal Golden Braid" - this ( combined with Kurzweil's "How to Create a Mind") will give you some of the more theoretical on neurology/computer cross over. read them both, even though it's 60% the same book. there's enough differences to make both worth while.
On the biology side, This book has some info on what it means to be "alive" which is a prerequisite for what it means to have "artificial intelligent life" this will likely lead you into genetic research and how DNA works - it's important, but not until you get there.
On the sociology / economic side, I can't think of one book that would be a great starting point. I will say that specifically understanding risk/reward principals and game theory are critical to development of any AI that goes beyond a linear regression model. Also look into OODA loops, it's a military term for "Observe, orient, decide, act"
For machine learning, there's no one better to teach it than Andrew Ng, the chief scientist at Baidu. His course at Stanford is recorded and free to take on Coursera. You can find it here
Saw your PM, but figured I'd post it here since you weren't the only person who pinged me on it :). Happy to answer any more questions, but i'm not online all that much so please be patient!
Take care and good luck!