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Where has Artificial Intelligence been and where is it going? a highly personal view Outline of talk 1. A lighthearted look at what has A.I. been doing these past 45 years in order to survive. 2. A more serious look at what I believe we have learned from work in A.I. and Cognitive Science. 3. Some speculations on whether current trends provide any grounds for optimism for the future of A.I. Where has A.I. been these past 45* years? It has been battling the forces of darkness and has survived In the meantime it has split into many parts and has assumed many different disguises. * It has been 45 years since the 1957 Dartmouth conference. A.I. Has Survived Many Threats It has survived: • • • • • • The illusions of innocence The foolishness of futurism The perils of popularity The dazzle of demos The shameless pursuit of short-term goals The corruptions of capitalism It has survived the illusions of innocence • The age of innocence: Cybernetics, Weiner, Turing, Newell & Simon and the promise of mechanized intelligence (That’s what drew me and many others to it) Photographs courtesy of “Lord of the Rings” It has survived foolhardy futurism “It’s hard to predict, especially the future” (Japanese saying) Examples of foolhardy futurism… • “A computer will be chess champion and will prove a significant mathematical theorem within ten years” (Simon, 1957) • “Within a generation the problem of creating ‘artificial intelligence’ will be substantially solved” (Minsky, 1967). • “I believe that robots with human intelligence will be common within 50 years” (Hans Moravec, 1988) • “…by the year 2020, [neural net computers] will have doubled about 23 times ... resulting in a speed of about 20 million billion neural connection calculations per second, which is equal to the human brain. …The emergence of machines that exceed human intelligence in all its broad diversity is inevitable.” (Ray Kurzweil, 1999) "Guys like Kurzweil and Moravec ... somehow think that they can take Moore's law and project things into the future and say that, by 2020, you'll have human-level intelligence. I don't believe that at all. I think new ideas are required." (John McCarthy, 2001). It has survived the perils of popularity For many years (in the late 1970’s and 1980’s) A.I. was the sexiest topic in computer science and psychology (which may be why many of us are here!) And that leads to certain distractions “the perils of popularity” It has survived the dazzle of demos The ‘Gee Whiz’ School of Science: • Toy systems (GPS, Eliza, Shrdlu, Shaky, Planner/Strips) • Performance systems (Dendral, XCON, Prospector, DART) Lothlorien But how do you know when you should be impressed by a demo? • It’s not easy to tell whether you should be impressed by a demo. • How impressive the demo is depends on what else you assume the system can do. • How impressive the demo is may depend on what tools are available. It has survived the shameless pursuit of short term goals • “Artificial intelligence used to mean robots that think like people; now it means software for rejecting junk e-mail. Low expectations could yield better applications, sooner." Doug Lenat. MIT Tech Review, 2001 So, what’s wrong with that? • Short-term solutions rarely scale up. • It is possible to attain spectacular short-term achievements without any fundamental new understanding of A.I. problems (e.g., Deep Blue). • Short term criteria don’t tell you when your assumptions are hopelessly wrong and you should start over. • Short term problemsolving detracts from the bigger AI goal. A.I. has even survived the corruption of capitalism Teknowledge, Tekmoney, CAIP, Cognicom, Gomi AI, .. What we tell venture capitalists vs. what we really believe Some of the commercial claims make one wonder who our colleagues are working for! “Sometimes I wonder if we have’t carried ecumenism a bit too far” AI’s dilemma • “In the end, we in AI get high marks on the applications side, to which we have been pushed by sponsor pressure and seduced the lure of billions. And certainly, it is hard to quarrel with the good done. But from the perspective of answering big questions and realizing big dreams, the disappearance of AI into computer science retards progress by shifting the way AI people are judged and rewarded.” Patrick Winston, keynote address to the American Association for Artificial Intelligence, July 20, 1999 Is it time to return to the original goal? Can we keep our “eye on the prize”* Is it time for looking back on what we’ve learned and for refocusing on the scientific puzzles *From Nils Nilsson’s 1995 AAAI paper title What have we learned in the past 45 years? Some current criticisms of AI – not my own views: Lessons I have learned from Cognitive Science 1. Representing and manipulating knowledge is not everything; KR has been overemphasized in AI; 2. Logical formalisms are hopeless for representing knowledge; 3. Human cognition has a lot in common with that of other organisms, so AI should start by simulating the simple ones first (e.g., insects), and letting complex ones evolve; 4. Intelligence is distributed among minds, bodies, and environments, and A.I. has not recognized these enough in its pursuit of KR. 1. There is more to intelligence than processing knowledge. No matter how little of the system deals with knowledge, it will always remain the core of intelligence because Intelligent behavior is essentially knowledge-dependent or cognitively penetrable. This follows from the following important facts about (human) cognition: The equivalence classes of events under which intelligent agents’ behavior is predictable are semantically defined: inputs with the same meaning have the same effect. It’s how the world is represented, rather than how it actually is, that determines behaviour. There is no rule or principal of intelligent behavior that cannot be overturned by changing a person’s goals and beliefs. In other words, some part of every intelligent behaviour is “cognitive penetrable”. For details, see my Computation and Cognition, MIT Press, 1984 (2) Logical formalisms are hopelessly inadequate for representing knowledge As Winston Churchill said about democracy: “It is the worst form of government, except for all the rest”. So also logic is the worst form of knowledge representation, except for all the rest. Much has been said about why “logic” is inadequate: e.g., it has trouble representing knowledge that is procedural, quantitative, pictorial (or sensory), higherorder (“meta knowledge”) and indexical; although excellent work is being done on all of these problems. A reasonable assumption is that there may have to be more than one form of representation. Unfortunately, none of the non-symbolic ones proposed so far (e.g., neural nets, images) are adequate for reasoning. Logic or Logical Calculus is just another name for “symbol system” except that logic usually provides a formal semantics (i.e., a theory of how the meaning of expressions is composed from the meanings of its parts) and a system of inference (at least for deductive inference, if not for inductive, abductive and practical reasoning). Nothing like that is available for any of the other forms of representation. So logic is a special form of symbol system; But why do we need symbol systems at all? • What makes it possible to represent and process knowledge in a physical system, and to connect this knowledge with both perception and action, is what Newell & Simon called a Physical Symbol System. This is perhaps the single most important idea of the 20th century because it made possible computing. But in order to be adequate for encoding humanlevel knowledge, physical symbol systems must meet some stringent conditions on format. Conditions on the format of representations 1. The format must have the capacity to distinguish an unlimited number of distinct representations. This is called the criterion of productivity: which von Humboldt characterized as the “Infinite use of finite means”. This means it must be combinatorial. 2. The capacity for representation and inference must be systematic: In intelligent agents the capacity to represent or infer a certain situation is always accompanied by the capacity to represent or infer other related situations. This means it must be compositional. Conditions on the format of an adequate system of representation • The conditions of productivity and systematicity entail the compositionality of representations: complex representations are built out of simpler representations by rules of composition. • Any system of representation meeting these constraints is a logical calculus or language of thought (lingua mentis). • The invention of systems of logic that can encode wide range of beliefs are among the greatest achievements of the 20th century. • For a more detailed argument see: Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28, 3-71. (3) It has been suggested that since human intelligence is continuous with animal intelligence, we should study lower organisms (e.g., insects) in which cognition is simpler, and then move up to human intelligence later. • Comparative psychology does indeed show that many aspects of vision, visual-motor coordination, as well as ontological categories and conceptual systems (e.g., concepts such as physical object, animate, cause, conspecific) and even parts of arithmetic are shared by most organisms, including human infants. • But the research strategy of working up from lower organisms will not work in general. Even though we may share a lot with lower organisms, what we do not share is critical; it is constitutive of “intelligence.” Although many human cognitive capacities are found in animals, many other capacities appear suddenly as we go up the philogenetic scale (evolution and “punctate equilibrium” – Steven Jay Gould). All humans, in contrast with members of other species, possess the capacity for language, the instinctive attribution of beliefs and desires to others, the capacity for counterfactual reasoning, and the need to provide a theoretical explanation for events around them (i.e., to practice inductive and abductive reasoning). Human vision, language, and action are at the service of goals and beliefs in a way that makes human behavior, unlike other animal behavior, largely stimulus-free; Because all intelligent behaviour is “cognitive penetrable” by goals and beliefs. (4) AI has traditionally downplayed the role of the environment in intelligent action: But intelligence must be situated and embodied. • The first recognition of the importance of the environment-agent relation in shaping behavior may be in Simon’s ant example, but it now makes an appearance in many areas of contemporary AI. • There is considerable truth in the observation that intelligence is situated and embodied, but the deep intellectual puzzles still concern how the organism represents the world, because (as discussed earlier): It’s how the world is represented, rather than how it actually is, that determines intelligent behavior. But also recent A.I. trends have been in the direction of taking the environment into account. Recent examples that recognize the importance of taking the agent’s environment into account include: Emphases on reactive planning, where plans allow for unexpected sensory input, Emphasis on active vision, in which vision interrogates the environment for relevant clues, Allowing for nonsymbolic aspects of reasoning through closer links with perception (e.g., “visual inference” – Jon Barwise), The use of indexicals in representations. (See Lespérance & Levesque, 1995; Pylyshyn, 2000, 2001) Forms of representation for a robot: using indexicals From: Pylyshyn, Z. W. (2000). Situating vision in the world. Trends in Cognitive Sciences, 4(5), 197-207. Finally, after all this, are there grounds for optimism about the future of Artificial Intelligence? The simple answer is “Yes, of course, otherwise we would not be here!” But what are some promising trends that offset the enormous problems still to be solved? Some grounds for optimism 1. There are a very many accomplishments for which AI can take credit, despite the “AI winter” we have come through. The military, the airlines, traditional Operations Research domains, robotics and the games world have all seen major AI achievements (see Nilsson, 1995). There are also many small accomplishments, like Google, seen daily on the web and in nearly every walk of life, that have widespread effects. Many have not used the term “artificial intelligence” but they nonetheless owe their technology to AI which has infiltrated much of conventional CS. 2. Unlike the early days of AI, many more approaches are being entertained. While most of the grand polemical claims made by their advocates are almost certainly wrong, these approaches are leading to different lines of inquiry, which is healthy. The diversity is also producing powerful niche results in areas within computational vision, robotics, computational linguistics, and speech understanding. There is strength in diversity (also distractions) Neats Scruffies Brooks Minsky Newell Logic Schank McCarthy Analogues Fuzzy sets Neural nets Expert $ystems Planning Robots Some grounds for optimism 3. One of the consequences of de-emphasizing pure reasoning has been more progress in areas where human cognitive skill is modular (and may not involve any reasoning). Vision (especially “early vision”). Computational Linguistics (a large part of grammatical analysis is modular), Speech recognition (much of phonetic analysis is modular), Visual-motor coordination (much of that is modular in humans and animals). Lends itself to Rod Brooks’ approach. Hybrid systems, in which the developments of modular “smart” subsystems (especially vision and control) are combined with knowledge-based systems (e.g., UBC’s hybrid controllers and U of T’s cognitive robotics). Some grounds for optimism (4) Finally, there has been a large (and unexpected) resurgence of interest in the very broad questions of the nature of intelligence, and it relation to consciousness, to biology, to evolution and to technology. Books and articles by Kurzweil, Moravec, Joy, Wolfram, Dennett and the critical writings of Searle, Fodor, Lanier and others have once again returned the bigger questions of human and machine intelligence to centre stage. While these may get people thinking about where we stand in history, they probably only move AI ahead by focusing public debate and perhaps awakening funding agencies. But some problems are just not ready for scientific scrutiny: they are the mysteries as opposed to the puzzles. The trouble with mysteries, such as the question What is consciousness? and What are the limits of AI? is that they are inherently ill-posed: they are not stated in a way that could connect to a recognizable answer. Yet despite the possibility that this new interest will let in misguided views and give us bad press, a multitude of approaches have to be tolerated because nobody knows where the key discoveries will come from. So we shouldn’t put them down, even when they are wrong (and even when they have larger grants than we do)! ….because then we could all lose ….. References cited Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence, 47, 139-159. Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28, 3-71. Lespérance, Y., & Levesque, H. J. (1995). Indexical knowledge and robot action - a logical account. Artificial Intelligence, 73, 69-115. Levesque, H. J., & Lakemeyer, G. (2001). The Logic of Knowledge Bases. Cambridge, MA: MIT Press. Nilsson, N. J. (1995). Eye on the prize. AI Magazine (summer), 9-17. Pylyshyn, Z. W. (2000). Situating vision in the world. Trends in Cognitive Sciences, 4(5), 197-207. Pylyshyn, Z. W. (2001). Visual indexes, preconceptual objects, and situated vision. Cognition, 80(1/2), 127-158. Pylyshyn, Z. W. (1999). Is vision continuous with cognition? The case for cognitive impenetrability of visual perception. Behavioral and Brain Sciences, 22(3), 341-423. Reiter, R. (2001). Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems. Cambridge, MA: MIT Press. Winston, P. H. (1999, July, 1999). Why I am optimistic. Paper presented at the AAAI Annual Conference.