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True or false? 1. Machine intelligence will never equal human intelligence. True or false? 1. Machine intelligence will never equal human intelligence. 2. Humans are the most intelligent things that God could possibly create. True or false? 1. Machine intelligence will never equal human intelligence. 2. Humans are the most intelligent things that God could possibly create. 3. Humans are really just a very sophisticated machine. True or false? 1. Machine intelligence will never equal human intelligence. 2. Humans are the most intelligent things that God could possibly create. 3. Humans are really just a very sophisticated machine. 4. Humans have never created anything that can outperform humans. True or false? 1. Machine intelligence will never equal human intelligence. 2. Humans are the most intelligent things that God could possibly create. 3. Humans are really just a very sophisticated machine. 4. Humans have never created anything that can outperform humans. 5. Some machines are already more intelligent than some humans. What is Intelligence? What (who) is intelligent? Worlds First “Automated” Chess Player The Turk (von Kempelen) Sample Conversation with Eliza Eliza: Me: Eliza: Me: Eliza: Me: Eliza: Me: Eliza: Me: Eliza: Me: Eliza: Me: Eliza: Me: Eliza: Please tell me your problem. I'm overwhelmed Did you come to me because you are overwhelmed? I did Say, do you have any psychological problems? I'm paranoid delusional How long have you been paranoid delusional? About three decades What does that suggest to you? That I'm in big trouble Do you believe it is normal to be in big trouble? Only if one is abnormal Are you saying no just to be negative? I didn't say no You are being a bit negative. What does that suggest to you? We were discussing you --not me. What (who) is intelligent? Where is the intelligence? http://www.primates.com/chimps/chimpanzee-picture.jpg http://upload.wikimedia.org/wikipedia/commons/4/4e/Human_and_chimp_brain.png Some ways intelligence is measured • • • • • • • IQ test Nobel prize SAT, ACT, grades Loquaciousness Silence (“better to remain silent…”) Success Survival Can a machine be intelligent? Area A Area B Area C Schema GM/NASA Robot Watson Intelligence Turing Test Intelligence Turing Test A Brief History of Machine Intelligence Commander Data 1997 Difference engine Babbage 1879 “Automated chess” von Kempelen 1770 Pascaline Pascal 1653 Artificial duck Vaucanson 1739 Eniac Mauchly and Eckert 1946 Machine of the Year Time 1983 R2D2 In a Galaxy, Far, Far Away Consider the robots in various Sci-Fi movies… What technical problems must be solved in order to have robots with the capabilities presented in these movies? Consider the robots in various Sci-Fi movies… What technical problems must be solved in order to have robots with the capabilities presented in these movies? (a) Voice recognition (b) Realistic speech synthesis (with inflection) (c) Natural language understanding (d) 3D vision processing (e) Extensive, readily accessible knowledge base (f) Rapid learning ability (g) Commonsense reasoning (h) Power supply to sustain long-lasting and powerful motion (i) Durable components A few real robots… http://asimo.honda.com/asimotv/ Humor12.com http://i.livescience.com/images/080418-human-brain-02.jpg Humor12.com Intelligence The creative use of acquired knowledge in a variety of environmentally constrained situations. This presumes the following: hierarchical knowledge base, associative memory, learning, symbol processing, concept formation, problem solving, use of rules, creative generalization, autonomy, multi-faceted capabilities “I believe that understanding intelligence involves understanding how knowledge is acquired, represented, and stored; how intelligent behavior is generated and learned; how motives, and emotions, and priorities are developed and used; how sensory signals are transformed into symbols; how symbols are manipulated to perform logic, to reason about the past, and plan for the future; and how the mechanisms of intelligence produce the phenomena of illusion, belief, hope, fear, and dreams—and yes even kindness and love. To understand these functions at a fundamental level, I believe, would be a scientific achievement on the scale of nuclear physics, relativity, and molecular genetics.” (James Albus) Possible Objections Computing Machinery and Intelligence (A. M. Turing, 1950) • (1) The Theological Objection Thinking is a function of man's immortal soul. God has given an immortal soul to every man and woman, but not to any other animal or to machines. Hence no animal or machine can think. • (2) The 'Heads in the Sand' Objection "The consequences of machines thinking would be too dreadful. Let us hope and believe that they cannot do so." • (3) The Mathematical Objection There are a number of results of mathematical logic which can be used to show that there are limitations to the powers of discrete-state machines. Possible Objections (continued) • (4) The Argument from Consciousness This argument is very well expressed in Professor Jefferson's Lister Oration for 1949, from which I quote. "Not until a machine can write a sonnet or compose a concerto because of thoughts and emotions felt, and not by the chance fall of symbols, could we agree that machine equals brain-that is, not only write it but know that it had written it. No mechanism could feel (and not merely {p.446} artificially signal, an easy contrivance) pleasure at its successes, grief when its valves fuse, be warmed by flattery, be made miserable by its mistakes, be charmed by sex, be angry or depressed when it cannot get what it wants." Possible Objections (continued) (5) Arguments from Various Disabilities These arguments take the form, "I grant you that you can make machines do all the things you have mentioned but you will never be able to make one to do X". Numerous features X are suggested in this connection. I offer a selection: Be kind, resourceful, beautiful, friendly, have initiative, have a sense of humor, tell right from wrong, make mistakes, fall in love, enjoy strawberries and cream, make some one fall in love with it, learn from experience, use words properly, be the subject of its own thought, have as much diversity of behavour as a man, do something really new. Possible Objections (continued) • (6) Lady Lovelace's Objection Our most detailed information of Babbage's Analytical Engine comes from a memoir by Lady Lovelace. In it she states, "The Analytical Engine has no pretensions to originate anything. It can do whatever we know how to order it to perform" (her italics). • (7) Argument from Continuity in the Nervous System The nervous system is certainly not a discretestate machine. A small error in the information about the size of a nervous impulse impinging on a neuron, may make a large difference to the size of the outgoing impulse. It may be argued that, this being so, one cannot expect to be able to mimic the behavour of the nervous system with a discrete-state system. Possible Objections (continued) • (8) The Argument from Informality of Behavour It is not possible to produce a set of rules purporting to describe what a man should do in every conceivable set of circumstances. • (9) The Argument from Extra-Sensory Perception … These disturbing phenomena seem to deny all our usual scientific ideas. How we should like to discredit them! Unfortunately the statistical evidence, at least for telepathy, is overwhelming. It is very difficult to rearrange one's ideas so as to fit these new facts in... The idea that our bodies move simply according to the known laws of physics, together with some others not yet discovered but somewhat similar, would be one of the first to go. This argument is to my mind quite a strong one. Three AI debates (Franklin) 1. Is AI even possible 2. How should it be done? 3. Representations or not? Is AI Possible? Strong AI: Appropriately programmed computers have cognitive states (i.e., are minds); programs are cognitive theories Weak AI: Computers are only/just tools for the study of the mind A proposal for the Dartmouth summer research project on Artificial Intelligence “We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.” J. McCARTHY, Dartmouth College M.L. MINSKY, Harvard University N. ROCHESTER, I.B.M Corporation C.E.SHANNON, Bell Telephone Laboratories August 31, 1955 Luger: Artificial Intelligence, 5th edition. © Pearson Education Limited, 2005 Main topics for discussion at the AI conference, Dartmouth College 1956: 1. Automatic Computers 2. How Can a Computer be Programmed to Use a Language 3. Neuron Nets 4. Theory of the Size of a Calculation 5. Self-Improvement (Machine Learning) 6. Abstractions 7. Randomness and Creativity Luger: Artificial Intelligence, 5th edition. © Pearson Education Limited, 2005 Some Hard Problems What do you see? What do you see? Interpret… • The spaceship photographed Seattle flying to Mars. • Time flies like an arrow… Interpret… • The spaceship photographed Seattle flying to Mars. • Time flies like an arrow… Four Hard Problems for: Humans Computers 1. 2. 3. 4. 1. Vision 2. Natural Language Processing 3. Commonsense Reasoning 4. Generalization Calculus Chess Perfect recall Constructing precise algorithms for difficult problems Moravec, H. (1998) “When will computer hardware match the human brain?” in Journal of Evolution and Technology, Vol. 1 Moravec, H. (2003). “Robots After All”, Communications of the ACM. October What is the key missing ingredient? • Speed? (Moravec) • Knowledge? – (Cyc/Lenat) • Algorithm? – (e.g., vision, language) Some Questions… • • • • What can machines learn? How can machines have emotions? Can machines ever be conscious? If we can build intelligent machines, should we? • What is the future of mankind in a world of intelligent machines? Ingredients for an intelligent system 1. 2. 3. 4. 5. 6. 7. 8. 9. perception/sensory processing (e.g., vision) action (locomotion/mobility, reaching/grasping) memory (learning, knowledge representation) thought (reasoning, problem solving, planning, prediction, decision making, concept formation, categorization, generalization) attention motivation language creativity consciousness AI and Big Questions A. B. C. D. E. F. G. H. I. J. K. L. M. N. What does it mean to be human? What is intelligence? Is intelligence inherently limited? How does meaning arise from mindless mechanisms? What defines purpose? What is creativity? Do we search the space of possibilities or do we create it? How can mechanistic views of humans be reconciled with perspectives of meaning and value? What is consciousness? What does it mean to understand something? What will advances in artificial intelligence/life mean for humans? What are the essential differences in humans and other species? How do we categorize things? Why do we ask big questions? “Embodiment” http://www.is.umk.pl/~duch/Wyklady/komput/w12/cog_shop_research.html Cog and Rodney: Which is the “person”? Anne Foerst Is AI Possible? (Continued) John Searle attacks the following: 1) that the appropriately programmed computer has cognitive states 2) that the programs explain human cognition 3) ELIZA, SHRDLU, or "any Turing machine simulation of human mental phenomena" as such Weapon: The Chinese Room Experiment Searle, J. (1980). “Minds, Brains, and Programs.” The Behavioral and Brain Sciences, vol. 3. Minds, Brains, and Programs 1. The Systems Reply Person does not understand the Chinese story, but system does... Searle: Person can internalize deciphering symbols and scratchpad, do calculations in his head (sans room), but he still does not understand Chinese. Since the system is now in him, neither does it. 2. The Robot Reply A different kind of program drives a robot which interacts with the world, thus really understanding the Chinese story (with the requisite mental states)... Searle: This approach "tacitly concedes that cognition is not solely a matter of formal symbol manipulation." Furthermore, perceptual and motor skills add nothing to understanding or intentionality. We can extend the original thought experiment to make the human the robot's homunculus, but the human still doesn't understand. 3. The Brain Simulator Reply Our (now parallel) program simulates neural activity in the brain of a person who understands Chinese... Searle: Doesn't this beg the question? Isn't strong AI supposed to be about any program working? (i.e., the idea that we should be able to understand mind without understanding the brain). "If we had to know how the brain worked in order to do AI, we wouldn't bother with AI." 4. The Combination Reply Just combine the three previous replies (i.e., a super-duper Turing test passing neural robot)... Searle: We probably would ascribe intentionality to the device in the absence of information about how it worked, but this doesn't help strong AI, since we are basing our judgment on looks and behavior, not on formal programs alone. "If we knew independently how to account for its behavior without such assumptions, we would not attribute intentionality to it, especially if we knew it had a formal program." 5. The Other Minds Reply People only know that other people understand anything (e.g., Chinese) by their behavior, and since a (hypothetical) computer can pass the requisite behavior tests, we must say it is cognitive... Searle: The issue is not about how one knows that others have cognitive states, but what is involved in attributing cognitive states to them (amen). Must be more than just computational processes and related output. 6. The Many Mansions Reply Arguments against strong AI only applies to current technology... Searle: Redefines strong AI "as whatever artificially produces and explains cognition." One can't be expected to argue against a changing hypothesis... The Computational Complexity Reply Penrose: "there might be some `critical' amount of complication in an algorithm which it is necessary to achieve in order that the algorithm exhibit mental qualities." (The Emperor's New Mind p.20) Searle: not addressed in original article – Elsewhere, uses a team of non-Chinese speaking people The Searle Doesn't Understand What It Means To Understand Reply Ok, so he’s not here to defend himself! – We probably don’t understand this, either… However, what is understanding? (Penrose touches on this lightly, but not adequately p.19) – Is it a label? - a feeling? self-awareness? Compression (Baum)? What is a mental or cognitive state? How Should AI Be Done? Top down: Psychological (behavioral) level Bottom up: Physiological (neural) level Emergence… Representations or Not? Of course… (for any non-trivial level of intelligence) The real issues are 1. to what extent the representations must exist from the outset (nature) versus being learned (nurture). 2. are the representations implicit or explicit Search Search Some Maze-Following Algorithms Pheromone trail Traveler deposits “pheromones” as it traverses the maze Always takes path containing least amt of pheromones (use clockwise or random check when faced with choice of paths having equal amts of pheromones) Stack memory Traveler remembers choice made at each branch by placing them on a stack Processes choices in clockwise fashion Backtracks at dead ends to last choice point, tries any untried path(s) Updates stack as maze is traversed Wall to right (or left) Traveler always makes turns at dead ends or choice points that keep the wall to its right (or left) Clone Traveler clones itself at each branch and the maze is processed in parallel. At least one version of the traveler will find the exit. Depth-first or breadth-first search Treat the branches as nodes (vertices) and the paths as arcs (edges) and perform a depth-first or breadth-first search on the resulting graph. Additional Considerations Paths of varying widths Loops General route detection and marking schemes (for variable width paths) Other obstacles (barriers in path, predators, etc.) Learning maze following behaviors… Learning (remembering) the most efficient path Interesting questions How would a human solve a maze? What aspects of this problem are pertinent to modeling human wayfinding? How is maze following indicative of the more general problem of “search?”