Artificial Intelligence

21 Nov 2022 23:16

Yet Another Inadequate Placeholder.

I am not best-pleased to see this phrase come back in to vogue over the last few years, riding on a combination of absurd, apocalyptic myth-making and real but limited advances in the art of curve-fitting, a.k.a. "deep learning". (Said differently, I remember the last time Geoff Hinton's students were going to take over the world with multi-layer connectionist models.)

Valentino Braitenberg, Vehicles: Experiments in Synthetic Psychology [Review: Hume on Wheels, or, One Must Imagine Frankenstein Happy]
Marvin Minsky, The Society of Mind
Stuart Russell and Peter Norvig, Artificial Intelligence, a Modern Approach [The standard textbook for about two decades now]
Herbert Simon, The Sciences of the Artificial
Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction

Margaret Boden, The Creative Mind: Myths and Mechanisms
Daniel Dennett
- Brainstorms
- Brainchildren: Essays on Designing Minds [Review: An Attempt to Introduce the Experimental Mode of Reasoning into Moral Subjects]
Marco Dorigo and Marco Colombetti, Robot Shaping: An Experiment in Behavior Engineering
The Genealogy of ELIZA {I am not joking when I say that no one should be allowed to comment on AI who hasn't stepped the source code for Eliza]
Alison Gopnik, "What AI Still Doesn't Know How to Do", Wall Street Jounral 15 July 2022 [What is more interesting here than the headline stuff is the suggestion that the right way to think about large language models is as a information-retrieval technology, a way of interacting with the corpus of texts fed into it --- for better or worse...]
John Holland, Adaptation in Natural and Artificial Systems
Gary Marcus
- "Deep Learning: A Critical Appraisal", arxiv:1801.00631
- "Innateness, AlphaZero, and Artificial Intelligence", arxiv:1801.05667
Drew McDermott, "Artificial Intelligence Meets Natural Stupidity", ACM SIGART Bulletin 57 (April, 1976): 4--9
Melanie Mitchell, "Why AI is Harder Than We Think", arxiv:2104.12871
Adam Sobieszek and Tadeusz Price, "Playing Games with AIs: The Limits of GPT-3 and Similar Large Language Models", Minds and Machines 32 341--364 [I have a bunch of quibbles and comments. (0) Gosh, there are a lot of citations to the journal editors. (1) They don't actually use item response theory! They just suggest that we can get information about whether a question-answerer is a human or a computer if different sources have different probabilities of an answer. Which is absolutely true and maybe worth mentioning in this context, but doesn't need IRT. (I say this as someone who thinks that a Rasch model for Turing tests would be awesome.) There is also the issue of why we should think there would be a probability of a given answer for either human beings or machines, stable over time. (2) I think their information-theory-inspired remarks about compression and generalization are a bit over-simplified. But I realize I have (or once had) expert over-sensitivity in this area, and I guess what they say is close enough to right for present purposes. (3) I think the point that if you are going to learn to predict unlabeled text, you are not going to be able to distinguish truth from falsehood, is quite right. (Even true texts are going to contain refutations, hypotheticals, etc.). (4) Similarly, the idea that statistical properties of symbol strings complicate the syntax/semantics distinction is one I remember being fairly widely understood in the late 1990s. (I'd argue that you can find a version of it in Zellig Harris's Language and Information.) Certainly in my own (then) area of research, if in a particular stochastic process the string "01" is followed by "1" 99% of the time, it's very hard to avoid saying things like "'01' usually implies '1' is coming" (cf. "black clouds approaching mean rain soon"). But the semantic field (if I may put it that way) is limited to more of the same process, not the rest of the world. (5) It is incredibly striking to me that there is absolutely nothing in this paper about the specific architectures of the neural networks involved, other than their ability to maintain some sort of long-range context. If these arguments were right, we should be able to do the same thing with a sufficiently powered-up implementation of any probabilistic text predictor --- maybe even anything which does universal source coding. (I don't quite see how to make Lempel-Ziv do this, but I haven't thought about it for more than literally one minute.) If someone is looking for a nice (but expensive) project, then, implementing one of Paul Algoet's old universal prediction schemes at read-the-whole-Web scale suggests itself.]
Judea Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference [Graphical models, before Uncle Judea got seized by the importance of causality]

Claude E. Shannon and John McCarthy (eds.), Automata Studies (1956)
Herbert A. Simon
- Models of My Life
- The Shape of Automation for Men and Management = The New Science of Management Decisions

CRS, Revised and Extended Remarks at "The Rise of Intelligent Economies and the Work of the IMF" (2018)

J. McCarthy, M. L. Minsky, N. Rochester and C. E. Shannon, "Proposal for the Dartmouth Summer Research Project on Artificial Intelligence" [1955; PDF scan via Ray Solomonoff (!)]

Margaret Boden, Mind as Machine: A History of Cognitive Science
Hamid R. Ekbia, Artificial Dreams: The Quest for Non-Biological Intelligence
Stefano Franchi and Guven Guzeldere (eds.), Mechanical Bodies, Computational Minds: Artificial Intelligence from Automata to Cyborgs
Phil Husbands, Owen Holland and Michael Wheeler (eds.), The Mechanical Mind in History
Pamela McCorduck, Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence
Nils Nilsson, The Quest for Artificial Intelligence [Participant's history.]
Alex Roland and Philip Shiman, Strategic Computing: DARPA and the Quest for Machine Intelligence, 1983--1993
Pierre Steiner, "C. S. Peirce and Artificial Intelligence: Historical Heritage and (New) Theoretical Stakes", pp. 265--276 in Philosophy and Theory of Artificial Intelligence

Meredith Broussard, Artificial Unintelligence: How Computers Misunderstand the World
Melanie Mitchell, Artificial Intelligence; A Guide for Thinking Humans
Janelle Shane, "You Look Like a Thing and I Love You": How AI Works and Why It's Making the World a Weirder Place

Harry M. Collins, Artificial Experts: Social Knowledge and Intelligent Machines
Kenneth M. Ford, Clark Glymour & Patrick J. Hayes (eds.), Android Epistemology

Philip Agre, Computation and Human Experience
Philip Agre and Ian Horswill, "Lifeworld Analysis," Journal of Artificial Intelligence Research 6 (1997): 111--145
James S. Albus and Alexander M. Meystel
- Engineering of Mind
- Intelligent Systems
Léon Bottou, "From machine learning to machine reasoning", Machine Learning (2014): 133--149
Justine Cassell, Joseph Sullivan, Scott Prevost, and Elizabeth Churchill (eds.), Embodied Computational Agents
John C. Collins, "On the Compatibility Between Physics and Intelligent Organisms," physics/0102024 [Claims to have a truly elegant refutation of Penrose]
Craig DeLancey, Passionate Engines: What Emotions Reveal about the Mind and Artificial Intelligence
Keith L. Downing, Intelligence Emerging: Adaptivity and Search in Evolving Neural Systems
Dario Floreano and Claudio Mattiussi, Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies
Vineet Gupta, Radha Jagadeesan and Prakash Panangaden, "Approximate reasoning for real-time probabilistic processes", cs.LO/0505063
Joseph Y. Halpern and Riccardo Pucella, "Probabilistic Algorithmic Knowledge", cs.AI/0503018
Marcus Hutter, "Towards a Universal Theory of Artificial Intelligence based on Algorithmic Probability and Sequential Decision Theory," cs.AI/0012011
Ken Kansky, Tom Silver, David A. Mély, Mohamed Eldawy, Miguel Lázaro-Gredilla, Xinghua Lou, Nimrod Dorfman, Szymon Sidor, Scott Phoenix, Dileep George, "Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics", arxiv:1706.04317
Eliza Kosoy, David M. Chan, Adrian Liu, Jasmine Collins, Bryanna Kaufmann, Sandy Han Huang, Jessica B. Hamrick, John Canny, Nan Rosemary Ke, Alison Gopnik, "Towards Understanding How Machines Can Learn Causal Overhypotheses", arxiv:2206.08353
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, Samuel J. Gershman, "Building Machines That Learn and Think Like People", arxiv:1604.00289
C. E. Larson and N. Van Cleemput, "Automated conjecturing III: Property-relations conjectures", Annals of Mathematics and Artificial Intelligence 81 (2017): 315--327
Hector J. Levesque and Gerhard Lakemeyer, The Logic of Knowledge Bases
R. Levinson, "A General Programming Language for Unified Planning and Control," Artificial Intelligence 76 (1995)
James Robert Lloyd, David Duvenaud, Roger Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani, "Automatic Construction and Natural-Language Description of Nonparametric Regression Models", arxiv:1402.4304
Luo Zhaohui, Computation and Reason: A Type Theory for Computer Science
Arthur B. Markman, Knowledge Representation
N. Muscettola, G. A. Dorais, C. Fry, R. Levinson and C. Plaunt, "A Unified Approach to Model-Based Planning and Execution," in Proceedings of the 6th International Conference on Intelligent Autonomous Systems
Victor Vikram Odouard, Melanie Mitchell, "Evaluating Understanding on Conceptual Abstraction Benchmarks", arxiv:2206.14187
John Pollock
- How to Build a Person: A Prolegomenon
- Cognitive Carpentry: A Blueprint for How to Build a Person
David L. Poole and Alan K. Mackworth, Artificial Intelligence: Foundations of Computational Agents [2nd edition free online]
Stuart Russell and Eric H. Wefald, Do the Right Thing: Studies in Limited Rationality
Tony Veale, Pablo Gervás and Rafael Pérez y Pérez, eds., Special Issue on Computational Creativity", Minds and Machines, Volume 20, Number 4 (2010)
Michael P. Wellman, "Putting the agent in agent-based modeling", Autonomous Agents and Mutli-Agent Systems 30 (2016): 1175--1189