the Beyond topics
- Gelernter, D. (2016). The tides of mind: Uncovering the spectrum of consciousness. WW Norton & Company.
- Marquis, P., Papini, O., & Prade, H. (2014). Some Elements for a Prehistory of Artificial Intelligence in the Last Four Centuries. ECAI.
- Scheutz, M. (Ed.). (2002). Computationalism: new directions. MIT Press.
- Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach.
This is an updated edition of the 2010 version containing extensive current references. [note the book is getting hard to find sometimes due to demand, and its being the definitive AI textbook. Check the edition you are using/getting]
- Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT Press. This is an updated (2nd) edition of the 1998 version
- Nilsson, N. J., & Nilsson, N. J. (1998). Artificial intelligence: a new synthesis. Morgan Kaufmann.
- Poole, D. L., Mackworth, A. K., & Goebel, R. (1998). Computational intelligence: a logical approach (Vol. 1). New York: Oxford University Press.
see also Artificial Intelligence: Foundations of Computational Agents 2nd Edition by the same authors.
- Pratt, V. (1987). Thinking Machines—The Evolution of Artificial Intelligence. Oxford: Basil Blackwell. – this is a general history of earlier machines … great reference to get historical insights not easily obtained elsewhere.
- Turing, A. M. (1948). Intelligent machinery. NPL. Mathematics Division. See also, Turing, A. (2004). Intelligent machinery (1948). The Essential Turing: Seminal Writings in Computing, Logic, Philosophy, Artificial Intelligence, and Artificial Life plus The Secrets of Enigma B. Jack Copeland, 395 which provides context and pointers to additional Turing resources.
- B. Jack Copeland (2004), Computability: Turing, Gödel, Church, and Beyond, The MIT Press.
Hard(er) Core Science Fiction and Speculative Fiction works
- John C. Wright’s Count to the Eschaton series is worth reading … provides interesting glimpse into a possible (far) future. It’s also fun to read … so good ideas and an interesting, universe spanning plot.
added a section for Artificial / computational / machine intelligence recommended readings on the main site — here.
noted also that “We’re also paying attention to the applicability of AI/MI concepts to the space of What Sloman calls ‘possible minds’. “The idea is that the space of possible minds encompasses not only the biological minds that have arisen on this earth, but also extraterrestrial intelligence, and whatever forms of biological or evolved intelligence are possible but have never occurred, and artificial intelligence in the whole range of possible ways we might build AI”
The initial list of principal textbooks include:
1. Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach.
This is an updated edition of the 2010 version containing extensive current references.
[note the book is getting hard to find sometimes due to demand, and its being the definitive AI textbook, check the edition you are using/getting]
2. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT Press. This is an updated (2nd) edition of the 1998 version.
In addition to his University of Alberta academic appointment, Richard Sutton is now the head of Alphabet/Google DeepMind Alberta operations.
3. Nilsson, N. J., & Nilsson, N. J. (1998). Artificial intelligence: a new synthesis. Morgan Kaufmann.
4. Poole, D. L., Mackworth, A. K., & Goebel, R. (1998). Computational intelligence: a logical approach (Vol. 1). New York: Oxford University Press.
5. Artificial Intelligence: Foundations of Computational Agents 2nd Edition by the same authors.
Fascinating insights by Computer Science / Artificial Intelligence profs …
some have summarized their insights by writing: “only by doing activities that fulfill our curiosity without any pre-defined objectives, true creativity can be unleashed. They call this the ‘Myth of the Objective’: Objectives are well and good when they are sufficiently modest … In fact, objectives actually become obstacles towards more exciting achievements, like those involving discovery, creativity, invention, or innovation—or even achieving true happiness… the truest path to “blue sky” discovery or to fulfill boundless ambition, is to have no objective at all.”
some of Stanley’s and Lehmans insights:
- “The flash of insight is seeing the bridge to the next stepping stone by building from the old ones. ”
- “[Picbreeder] is just one example of a fascinating class of phenomena that we might call non-objective search processes, or perhaps stepping stone collectors. The prolific creativity of these kinds of processes is difficult to overstate”
- “ measuring success against the objective is likely to lead you on the wrong path in all sorts of situations”
- “You can’t evolve intelligence in a Petri dish based on measuring intelligence. You can’t build a computer simply through determination and intellect—you need the stepping stones. ”
- “ambitious objectives are the interesting ones, and the idea that the best way to achieve them is by ignoring them flies in the face of common intuition and conventional wisdom. More deeply it suggests that something is wrong at the heart of search. ”
I find their books inspiring and insightful. Reframing questions and providing different lines of attack on AI and Search Optimization to Ambitious Goals …
One of the main categories of discussion in this book is that of worthwhile tasks for AI. I will devote some time to stating some of the recognized questions, problems, and tasks. I will also mention some notable AI accomplishments and highlight a few of the recognized scholarly achievements. Another topic for discussion is the classification of Intelligences. What is Natural Intelligence? What is Artificial General Intelligence? What is Superintelligence? What about human measures such as IQ? G? What does the AlphaZero algorithm beating the best human players in Chess, Go and Shogi mean? Can the Paperclip Apocalypse really happen?
All these and more … coming soon …