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An Introduction to Artificial Intelligence and Cognition

An Introduction to Artificial Intelligence and Cognition
Intelligent Control and Cognitive Systems An Introduction to Artificial Intelligence and Cognition Joanna J. Bryson University of Bath, United KingdomOutline Last Time: • Defined intelligence in terms of • behaviour. Talked about sensing for action. This time: • Why focus on action What about • cognition Intro to Cognitive Architectures. •Resources Lots of books in the library (many on • LEGO robots) Russel Norvig is the ultimate AI textbook • (for the last decade), though comes from a CMU Stanford prespective. Norvig Thrun’s Stanford AI lectures are • available on line.Quick History of AI Early 20c Turing invents CS to solve AI. • Dartmouth Conference (1956) John • McCarthy, Marvin Minsky, Nathaniel Rochester Claude Shannon proposed, Alan Newell, Herbert Simon Oliver Selfridge (among others) attended. Proposal used the phrase “artificial • intelligence”, apparently for the first time.The Dartmouth Proposal We propose that a 2 month, 10 man study of artificial intelligence be carried out... 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. McCarthy Minsky et al 1956The Dartmouth Proposal We propose that a 2 month, 10 man study of artificial intelligence be carried out... 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. McCarthy Minsky et al 1956The Dartmouth Proposal We propose that a 2 month, 10 man study of artificial intelligence be carried out... 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. McCarthy Minsky et al 1956The Dartmouth Proposal We propose that a 2 month, 10 man study of artificial intelligence be carried out... 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. McCarthy Minsky et al 1956The Summer Vision Project The summer vision project is an attempt to use our summer workers 11 UGs effectively in the construction of a significant part of a visual system. The particular task was chosen partly because it can be segmented into sub problems which allow individuals to work independently and yet participate in the construction of a system complex enough to be real landmark in the development of "pattern recognition"... The primary goal of the project is to construct a system of programs which will divide a vidisector picture into regions such as likely objects, likely background areas and chaos. We shall call this part of its operation FIGUREGROUND analysis. It will be impossible to do this without considerable analysis of shape and surface properties, so FIGUREGROUND analysis is really inseparable in practice from the second goal which is REGION DESCRIPTION. The final goal is OBJECT IDENTIFICATION which will actually name objects by matching them with a vocabulary of known objects. Papert Minsky (w/ Sussman) MIT 1966Quick History of AI Early 20c Turing invents CS to solve AI. • Dartmouth Conference (1956) John • McCarthy, Marvin Minsky, Nathaniel Rochester Claude Shannon proposed, Alan Newell, Herbert Simon Oliver Selfridge (among others) attended. Proposal used the phrase “artificial • intelligence”.The CMU Perspective Physical Symbol System Hypothesis (Newell • Simon 1963) “A physical symbol system has the necessary and sufficient means for general intelligent action.” Implies: Human thinking is a kind of symbol • manipulation (because a symbol system is necessary for intelligence). Machines can be intelligent (because a • symbol system is sufficient for intelligence).Symbols Symbols (and sometimes • Language) have been thought to define intelligence for a long time. Brooks (1991) was a • significant challenge to this (more next week).Mind uploading / whole brain scanning AI Heaven If intelligence is just • symbol systems, and we’re all Turing compatible... Cartesian dualism • should hold. We can upload our • brains “live” forever. cf. Minsky, Vinge.“New AI” (1986) AI “wasn’t working” (more on this later in • the course). Refocus attention on behaviour, robots. • Produced first robots that could operate at • animallike speeds. Genghis, MIT 1986New AI Functionalist Assumption: All we care about is producing intelligent behaviour. Physical Symbol System Hypothesis (Newell • Simon 1963); Qualia, Chalmers “hard problem” (1995) Build thinking first. Consciousness as epiphenomena • (Churchland 1988, Brooks 1991). We’ll build it if we need it. SciencNewer New AI Functionalist Assumption: All we care about is producing intelligent behaviour. Physical Symbol System Hypothesis (Newell • Simon 1963); Qualia, Chalmers “hard problem” (1995). Build thinking first. Consciousness as epiphenomena • (Churchland 1988, Brooks Stein 1993). We’ll build it to see if we need it. (Bias alert: Stein was my PhD supervisor)Syst em sof t ware ( 0 t h) Syst em sof t ware ( commercial processor) Periperhal Mot ion Vergence Ullmanesque Physical schema Saccades based st ereo visual rout ines based obj. recog. “Building VOR Smoot h pursuit Face popout s Face remembering Face recognit ion Brains Head/ body/ eye/ coord Head/ eye coord Gest ure recognit ion Facial gest ure recog. Body mot ion recog. for Own hand t racking Specif ic obj. recog. Generic object recog. Bodies”, Bring hands Hand Grasping, Bodybased met aphors midline linking t ransfer Brooks DOF reduct ion DOF reduct ion Bat t ing st at ic Stein (specific coords) (generic coords) object s Body st abilit y, Body+arm reaching Body mimicry (1993), leaning, rest ing Manipulat ion t urn t aking MIT AI Sound localizat ion Soundbased manip. Voice/ face assoc lab tech Sound/ mot ion correl Human voice ext ract ion Prot o language report Tone ident ificat ionVoice t urn t aking Visual imagery Symbolizat ion 1439. Ment al rehearsal Imaginat ion Mult ipledraf t s emergence Sept 1 Sept 1 Sept 1 Sept 1 Sept 1 1993 1994 1995 1996 1997Text Intelligence Cognition A European / Brooksian PerspectiveIntelligence What matters is expressing the right • behavior at the right time: action selection. Finding the right action requires search. • Search is intractable. • Corollary 1: This is why we all act stupid. • Corollary 2: Culture / concurrency is what • makes humans so smart.Why is it hard to be smart Pretend someone handed you a robot brain brick, and it came with 100 things it knew how to do without being told. For example, eat, sleep, turn right, turn left, step forward, step backward, pick things up, drop them... Now pick a goal for your robot. For example, flying to Osaka.Sanyo robot watchdog The hardness of smartness (2) Suppose you can’t be bothered to tell your robot exactly how to get to Osaka, so you have it guess. If getting to Osaka is a builtin primitive, the robot may have to try 100 different things. If it requires two steps, the robot may have to try each thing after each thing: 2 100 =10,000The hardness of smartness (3) If the robot doesn’t know how many steps it takes to go to Osaka, it might get caught in an infinite loop. For example, it might eat, sleep, work, eat, sleep, work, eat, sleep, work... and never buy a passport. When computer scientists say “hard” they mean “pretty much intractable.” Sony SDR4Xs. Pictures from BBCIntelligence Design Combinatorics is the problem, search is the only • solution. The task of intelligence is to focus search. • priors Called bias (learning) or constraint (planning). • Most `intelligent’ behavior has no or little real • time search (noncognitive) (c.f. Brooks IJCAI91). For artificial intelligence, most focus from design. •Intelligence What matters is expressing the right • behavior at the right time: action selection. Conventional AI planning searches for an • action sequence, requires set of primitives. That set of primitives came from search • by the system’s designers. ∴ Building AI requires tradeoffs between • search by designers computers.What About Learning A learning system consists of a • representation (state) and an algorithm for changing the values in that representation. Learning searches for the right parameter • values, requires primitives and parameters. No learning algorithm automatically • generates AI through invocation. Evolution and development are just • special kinds of learning.What About Cognition Definition: Cognition is online (realtime) search. Consequence: Cognition is bad.Cognition Why is cognition / individual search bad • Slow • Uncertain • Unpopular in most species. • e.g. Plants •Cognition When is cognition useful • Dynamic environments – change faster • than learning or evolution can adapt. Note this depends on lifehistory. • Baldwin Effect – fast noisy search • facilitates (speeds up) slower more reliable learning processes (Baldwin 1896, Hinton Nowlan 1987).Why History Matters A lineage is a type of state – a set of data • forming a preexisting solution, that then gets improved upon, e.g. through innovation – Generate • selection – Test •Cognition When is cognition useful • Dynamic environments – change faster • than learning or evolution can adapt. Note this depends on lifehistory. • Baldwin Effect – fast noisy search • facilitates (speeds up) slower more reliable learning processes (Baldwin 1896, Hinton Nowlan 1987).(interactive) Why Cognitive Systems What artefacts need to be cognitive • What artefacts need to adjust in real time • (possible answers…) traits: • Proactive, interactive, sensing, mapping... • examples: • Smart homes, personal digital assistants / • phones, drones.Summary The history of AI through about 1995. • More contemporary stuff later in the • course Introduction to “New AI” and Systems AI. • Introduction to discussion of academic / • scientific lineages, including mine.