Artificial general intelligence
This book constitutes the refereed proceedings of the 5th International Conference on Artificial General Intelligence, AGI 2012, held in Oxford, UK, in December 2012. The 34 revised full papers presented together with 4 invited keynote lectures were carefully reviewed and selected from 80 submissions. The papers are written by leading scientists involved in research and development of AI systems possessing general intelligence at the human level and beyond; with a special focus on humanoid robotics and AGI, cognitive robotics, creativity and AGI, the future evolution of advanced AGIs, and the dynamics of AGI goal systems.
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- I love the idea of cognitive AI. I've dabbled with OpenCog https://en.wikipedia.org/wiki/OpenCog , and in my youth I read "Artificial General Intelligence (2005)" by Ben Goertzel and I was really convinced.
But it seems like... cognitive AI hasn't paid off? Big & deep Neural Nets are the type of ML/AI that are achieving milestones in learning, gameplay and tasks.
If someone has a strong case to make for cognitive programming, I'd love to hear it. But right now it seems like it's a heuristics-based system that's destined to lose to NNs.
(And before the nitpickers arrive, I totally grant that using heuristics for toy robots makes sense, and is a good way to expose people to programming.)
- > what AGI has always meant.
Reading wikipedia about history of the term, it sounds like term was popularized by this book: https://www.amazon.com/Artificial-General-Intelligence-Cogni... which says that general means "ability so solve variety of tasks in variety of domains", not "all tasks in all domains". So "always meant" is easily challengeable here.
- OK, here ya go. This list is, for now, just a raw dump of titles and authors. When I have some more time, I'll turn this into a proper blog post with more commentary, sorting / grouping of the titles, links to complementary resources etc.
One bit of warning... the first half of this list includes everything "AI" related I've read (or am currently reading) for about the past two years. I say "AI" and not "AGI" because I haven't always bothered to make the distinction. So some of this stuff may not be relevant to you.
FYI, this list (especially the first half) is a heterogeneous lot that varies considerably along the "pop sci" to "academic" axis, as well as the "applied" to "theoretical" axis.
I'm also leaving off (for now anyway) most the "related, but not specifically about AI" stuff, like books on Probability, Bayesian Statistics, Graph Theory, etc., and books that are more about contemporary "Machine Learning" and "Deep Learning" topics. I do believe that there is a connection though, and that DL may play a role in building a truly intelligent system, so when I write the expanded blog post, I'll include more of that stuff.
OK, With no further ado, the "AI stuff I've read recently" list.
0. Blackboard Systems - Robert Engelmore & Tony Morgan
1. The Age of Spiritual Machines - Ray Kurzweil
2. The Emotion Machine - Marvin Minsky
3. The Society of Mind - Marvin Minsky
4. Possible Minds: 25 Ways of Looking at AI - John Brockman (ed)
5. AI Super-Powers: China, Silicon Valley and the New World Order - Kai-Fu Lee (more meta than actually about AI per-se)
6. Artificial Intelligence In Practice - Bernard Marr
7. Architects of Intelligence - Martin Ford
8. Gödel, Escher, Bach - Douglas Hofstadter
9. Superintelligence - Nick Bostrom
10. The Master Algorithm - Pedro Domingos
11. On Intelligence - Jeff Hawkins
12. Unified Theories of Cognition - Alan Newell
13. The AGI Revolution - Ben Goertzel
14. How To Create A Mind - Ray Kurzweil
15. Case-Based Reasoning: A Textbook - Michael M. Richter & Rosina O. Weber
16. The Age of Intelligent Machines - Ray Kurzweil
17. The Singularity is Near - Ray Kurzweil
18. An Introduction to MultiAgent Systems - Michael J. Wooldridge
19. Engineering General Intelligence - Volume 1 - Ben Goertzel
20. Artificial Intelligence: A Modern Approach - Peter Norvig & Stuart Russell
----
Below here is the stuff that is in the "to read" queue. This part tends to run more to textbooks and more academic material and less of the "pop sci" stuff. The choices on this list were inspired by other reading and research I've done, which led me to believe this group of titles includes material that will be useful. YMMV.
21. Cause Effect Pairs in Machine Learning - Isabelle Guyon, Alexander Statnikov, & Berna Bakir Batu (eds)
22. Propositional, Probabilistic, and Evidential Reasoning: Integrating Numerical and Symbolic Approaches - Weiru Liu
23. Human Compatible - Stuart Russell
23. Rebooting AI - Gary Marcus & Ernest Davis
24. Artificial Intelligence - Melanie Mitchell
25. Protocol Analysis - K. Anders Ericsson & Herbert A. Simon
26. The Robot's Dilemma: The Frame Problem in Artificial Intelligence - Zenon W. Pylyshyn
27. Image and Mind - Stephen Michael Kosslyn
28. Elements of Episodic Memory - Endel Tulving
29. Things and Places: How The Mind Connects With The World - Zenon W. Pylyshyn
30. Information Algebras: Generic Structures for Inference - Jurg Kohlas
31. Probabilistic Logics and Probabilistic Networks - Rolf Haenni, Jan-Willem Romeijn, Gregory Wheeler & Jon Williamson
32. Knowledge-Based Systems in Artificial Intelligence - Randall Davis & Douglas B. Lenat
33. Building Large Knowledge-Based Systems - Douglas B. Lenat & R.V. Guha
34. Elements of Information Theory - Thomas M. Cover & Joy A. Thomas
35. The Bayesian Choice - Christian P. Robert
35. Machine Learning for Commonsense Reasoning Processes - Xenia Naidenova
36. Reinforcement Learning and Optimal Control - Dimitri P. Bertsekas
37. An Introduction to Kolmogorov Complexity and Its Applications - Ming Li & Paul Vitanyi
38. The Mechanical Mind: A Philosophical Introduction to Minds, Machines, and Mental Representation - Tim Crane
39. Constraining Cognitive Theories - Zenon W. Pylyshyn
40. Neuro-Dynamic Programming - Dimitri P. Bertsekas & John Tsitsiklis
41. The Language Instinct - Steven Pinker
42. How The Mind Works - Steven Pinker
43. Images and Words - Steven Pinker
44. Words and Rules - Steven Pinker
45. The Blank Slate - Steven Pinker
46. Consciousness Explained - Daniel Dennett
47. How to Build a Brain: A Neural Architecture for Biological Cognition - Chris Eliasmith
48. Introduction to Artificial Intelligence - Eugene Charniak & Drew McDermott
49. Metamagical Themas - Douglas Hofstadter
50. I Am A Strange Loop - Douglas Hofstadter
51. Fluid Concepts And Creative Analogies: Computer Models Of The Fundamental Mechanisms Of Thought - Douglas Hofstadter
52. Principles of Synthetic Intelligence - Joshua Bach
53. Anatomy of the Mind - Ron Sun
54. Generic Inference: A Unifying Theory for Automated Reasoning - Marc Pouly & Jurg Kohlas
55. Knowledge Representation, Reasoning, and the Design of Intelligent Agents: The Answer-Set Programming Approach - Michael Gelfond & Yulia Kahl
56. Semantic Networks: An Evidential Formalization and its Connectionist Realization - Lockendra Shastri
57. Automated Planning and Acting - Malik Ghallab, Dana Nau, & Paolo Traverso
58. How Can The Human Mind Occur in the Physical Universe? - John Anderson
59. Spiking Neuron Models - Wulfram Gerstner & Werner Kistler
60. Neural Engineering - Chris Eliasmith & Charles H. Anderson
61. Principles of Neural Design - Peter Sterling & Simon Laughlin
62. Semantic Information Processing - Marvin Minsky
63. The SOAR Cognitive Architecture - John Laird
64. The Architecture of Cognition - John Anderson
65. Human Associative Memory - John Anderson & Gordon E. Bower
66. Knowledge Representation & Reasoning - Ronald J. Brachman & Hector J. Levesque
67. Cognitive Architectures - Maria Isabel Aldinhas Ferreira, Joao Silva Sequeira, & Rodrigo Ventura (eds)
68. Scripts, Goals, and Planning - Roger C. Schank & Robert P. Abelson
69. Reasoning About Plans - James F. Allen, Henry A. Kautz, Richard N. Pelavin, & Josh D. Tenenberg
70. Generating Abstraction Hierarchies: An Automated Approach to Reducing Search in Planning - Craig A. Knoblock
71. Representations of Commonsense Knowledge - Ernest Davis
72. Approaches to Knowledge Representation - G.A. Ringland & D.A. Duce
73. Knowledge Representation - John F. Sowa
74. Conceptual Structures: Information Processing in Mind and Machine - John F. Sowa
75. Inside Computer Understanding - Roger C. Schank & Christopher K. Riesbeck
76. Integrating Rules and Connectionism for Robust Commonsense Reasoning - Ron Sun
77. Probabilistic Logic Networks - Ben Goertzel, et al.
78. The Subtlety of Sameness: A Theory and Computer Model of Analogy Making - Robert M. French
79. Analogy Making as Perception - Melanie Mitchell
80. Real World Reasoning - Ben Goertzel, et al.
81. Artificial General Intelligence - Ben Goertzel & Cassio Pennachin
82. The Hidden Pattern: A Patternist Philosophy of Mind - Ben Goertzel
83. Engineering General Intelligence - Volume 2 - Ben Goertzel, Cassio Pennachin, & Nil Geisweiller
84. Computation and Cognition - Zenon W. Pylyshyn
85. Universal Artificial Intelligence - Marcus Hutter
86. The Cambridge Handbook of Computational Psychology - Ron Sun
87. Handbook of Neuroevolution Through Erlang - Gene I. Sher
88. Readings in Cognitive Science: A Perspective from Psychology and Artificial Intelligence - Allan Collins & Edward E. Smith -- this one contains a LOT of seminal papers in the field, including one of the earliest papers on Semantic Networks by Ross Quillian. I actually put this on the wrong part of the list, as I'm about halfway through reading this one, but just remembered it and didn't want to renumber everything. Heh.
Eh, that's a pretty good chunk of the stuff I've got lying around here, waiting to be read. There's a handful of others, but my fingers are getting numb.
- I'm curious how close the research community is to general AI
Nobody knows, because we don't know how to do it yet. There could be a "big breakthrough" tomorrow that more or less finishes it out, or it could take 100 years, or - worst case - Penrose turns out to be right and it's not possible at all.
Also, are there useful books, courses or papers that go into general AI research?
Of course there are. See:
https://www.amazon.com/Engineering-General-Intelligence-Part...
https://www.amazon.com/Engineering-General-Intelligence-Part...
https://www.amazon.com/Artificial-General-Intelligence-Cogni...
https://www.amazon.com/Universal-Artificial-Intelligence-Alg...
https://www.amazon.com/How-Create-Mind-Thought-Revealed/dp/0...
https://www.amazon.com/Intelligence-Understanding-Creation-I...
https://www.amazon.com/Society-Mind-Marvin-Minsky/dp/0671657...
https://www.amazon.com/Unified-Theories-Cognition-William-Le...
https://www.amazon.com/Master-Algorithm-Ultimate-Learning-Ma...
https://www.amazon.com/Singularity-Near-Humans-Transcend-Bio...
https://www.amazon.com/Emotion-Machine-Commonsense-Artificia...
https://www.amazon.com/Physical-Universe-Oxford-Cognitive-Ar...
See also, the work on various "Cognitive Architectures", including SOAR, ACT-R, CLARION, etc,
https://en.wikipedia.org/wiki/Cognitive_architecture
"Neuvoevolution"
https://en.wikipedia.org/wiki/Neuroevolution
and "Biologically Inspired Computing"
https://en.wikipedia.org/wiki/Biologically_inspired_computin...
- I think this should be re-stated that a group of respected researchers have revived Artificial Intelligence's original ambitions.
In fact, there have been a handful of folks thinking and working on Strong AI/Artificial General Intelligence steadily for years. Ben Goertzel even started the conference [1] and journal [2] in 2008.
One of the biggest things I see in every one of these conversations and threads is everyone assuming humans are actually good at things, as opposed to just marginally better than most other machines or humans. So people's standards for machine intelligence are way above what they would expect from human standards.
Google translate is a perfect example. People were complaining about it here on HN the other day because it wasn't perfect, however in comparison to any group of average professional linguists (Some of whom I work with daily) Google Translate and some of the other machine translation services' accuracy for it's speed is light years ahead.
People also forget that "Strong AI" and "AI complete" is always a moving target for benchmarking - and there is no benchmarking standard (No, a turing test isn't a robust enough test for an AGI). I posit that humans will never truly accept an AI as smarter than us until it dominates us. It will always be - well yes it can X, but is it really thinking? Does it have consciousness? Great questions philosophically, but practically it really doesn't matter.
This is good though, I think if there is anything the human race should be working on it's this. Everything, and I mean EVERYTHING pales in comparison in my opinion.
[1] http://www.agi-conference.org/2013/ [2] http://www.degruyter.com/view/j/jagi
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