Lecture 1CS250: Intro to AI/Lisp Computers & Thought Lecture 1 January 5th, 1999 CS250

Lecture 1CS250: Intro to AI/Lisp What is cognition? Cognition is widely studied: philosophy, psychology, and other fields Can we implement computer programs that think? –Model the process –Create the result –Example: airplane flight

Lecture 1CS250: Intro to AI/Lisp Is cognition computation? Computation is what you can do with a Turing machine (Church-Turing) –What's a Turing machine? –Model for a Turing machine? Need states and operations –Brain states –Operations that move among states Alan Turing Copyright (c) Maxfield & Montrose Interactive Inc.

Lecture 1CS250: Intro to AI/Lisp The Turing Test How to tell if a computer is intelligent? Use the Turing test –Boston Computer Museum 1991 test Is this a good definition of intelligence?

Lecture 1CS250: Intro to AI/Lisp Is Computation Enough for Cognition? Chinese Room Argument –Does the man in the room understand Chinese? –Does it matter? What's the difference between a native Chinese speaker, and the "room in a man"?

Lecture 1CS250: Intro to AI/Lisp Refutations of the Chinese Room Argument Systems reply Another reply: –Searle argues 1) Some elements don't understand Chinese (human, paper and the book) 2) Elements that don't understand cannot be pieced together to create a whole that does understand

Lecture 1CS250: Intro to AI/Lisp What Do You Need to Pass a Turing Test? Conversational skills (known as natural language processing, or simply NLP) Store of knowledge Reasoning Learning

Lecture 1CS250: Intro to AI/Lisp Approaching AI Building a brain in a computer –Cognitive modeling High-school geometry approach –Logic & inference Agent approach –Rationality Experiential approach –Case-based reasoning

Lecture 1CS250: Intro to AI/Lisp Other Fields & AI Philosophy –Questions of brain and mind –Consciousness –Logic

Lecture 1CS250: Intro to AI/Lisp Mathematics Logic Theory of computability Probability

Lecture 1CS250: Intro to AI/Lisp Logic in AI Inference –Possible reasoning process for cognition Representation –Logical statements –Observations about the world –States of the world

Lecture 1CS250: Intro to AI/Lisp How hard is hard? Computability / complexity theory How hard is a problem?

Lecture 1CS250: Intro to AI/Lisp "What are the odds?" World is full of uncertainty Probability helps formalize uncertainty Bayes Theorem is key: From

Lecture 1CS250: Intro to AI/Lisp Psychology "Information processing view" –Response to behaviorism

Lecture 1CS250: Intro to AI/Lisp Computer Engineering Sets AI apart from non-computer disciplines Focus on implementation

Lecture 1CS250: Intro to AI/Lisp Linguistics Link between language and thought –MITECS article on Language of ThoughtLanguage of Thought

Lecture 1CS250: Intro to AI/Lisp Sapir-Whorf hypothesis Linguistic Relativity –Structural differences between languages will generally be paralleled by nonlinguistic cognitive differences Linguistic determinism –Structure of a language strongly influences or fully determines the way its native speakers perceive and reason about the world

Lecture 1CS250: Intro to AI/Lisp What about Sapir-Whorf? Anthropologist John Lucy –Speakers of languages with different basic color vocabularies might sort non-primary colors (e.g., turquoise, chartruese) in slightly different ways Psychologist Alfred Bloom's claim –No distinct counterfactual marker in Chinese --> difficult for Chinese speakers to think counterfactually

Lecture 1CS250: Intro to AI/Lisp Cognition and Language Abilities to learn and use language part of our general intelligence –Language Specific Impairments –Williams syndrome Cognition and language can be decoupled

Lecture 1CS250: Intro to AI/Lisp Natural Language Processing NLP is one of the most difficult tasks in AI (AI-complete) Why? –Ambiguity resolved by context –Computers lack context

Lecture 1CS250: Intro to AI/Lisp Early AI : Samuel's checkers playing program beats Samuel Summer Dartmouth: Summer workshop with John McCarthy Marvin Minsky, Claude Shannon and others 1958 –Lisp –Time sharing –Advice Taker

Lecture 1CS250: Intro to AI/Lisp Knowledge-Based Systems Late 60's, early 70's: Big talk falls flat Knowledge-based systems –Know about the world D ENDRAL –Deduced molecular structure from mass spectrometry –Encoded rules from experts MYCIN

Lecture 1CS250: Intro to AI/Lisp Linguistics in AI The "Yale school" –Roger Schank jumped ship from linguistics to AI –"There's no such thing as syntax" What do you need to understand language? –Heavy on domain knowledge –Scripts, CBR

Lecture 1CS250: Intro to AI/Lisp Representational Systems Driven by big problems –Battlefield communication –Logistics –Campaign planning Scaling up –Prolog for rules –Frames (from MM) for structured representations

Lecture 1CS250: Intro to AI/Lisp Recent History Assist instead of replace Neural networks are back –Perceptrons, by Minsky and Papert –Backpropagation brings NN's back Probabilistic systems –Bayesian networks (e.g., Koller & Horvitz) Information retrieval and analysis

Lecture 1CS250: Intro to AI/Lisp Intelligent Agents Tasks or Problems Action selection and planning Agent communication languages Agents in entertainment applications Believable agents Collaboration between people and agents Communication between people and agents Coordinating perception, thought, or action Expert assistants Information agents Integrated theories of intelligence Knowledge acquisition and accumulation Learning and adaptation Modeling emotion Multi-agent communication, coordination, or collaboration Multi-agent simulation Multi-agent teams Techniques or Algorithms Algorithms for negotiation Artificial market systems Cognitive models Evaluations and implemented systems Game-theoretic modeling of the behavior of other agents Logic-based agent communication languages Meta-modeling of an agent by itself Mobile agents Task-specific agent architectures Intelligent Interfaces Tasks or Problems Auditory scene analysis Computer-aided instruction Conversation Help desks Intelligent buildings or rooms Models of human speech perception Music perception Speech coding Speech recognition Speech synthesis Techniques or Algorithms Evaluations and implemented systems Hidden Markov models Learning interaction models Learning user preferences Student modeling techniques Knowledge Representation and Reasoning Tasks or Problems Causal reasoning Common-sense reasoning Constraint satisfaction tasks Design, modeling, simulation, or diagnosis Game playing Reasoning about embedded systems Reasoning about relevance Representations of belief, intention, time, space, action, or events Spatial and geometric reasoning Temporal reasoning Natural Language Processing Tasks or Problems Dialog Discourse Generation Information extraction from the Web Machine translation Multimedia models Understanding Techniques or Algorithms Evaluations and implemented systems Hidden Markov models Statistical or corpus based methods Planning, Scheduling and Control Tasks or Problems Active perception and sensor-based planning Agent architectures for planning and control Decision-theoretic planning Mixed-initiative planning Multi-agent planning Plan and schedule visualization Plan execution, monitoring or replanning Planning and learning Resource management Scheduling Techniques or Algorithms Comparative analyses Compilation to SAT Constraint management approaches Discrete control theory approaches Empirical evaluations Evaluations and Implemented systems Fuzzy control techniques Graphplan-based algorithms MDP planning Partial-order planning Planning using dynamic belief networks Scheduling algorithms Specialized planning algorithms Robotics Tasks or Problems Behavioral control Dynamical control systems Geometric motion planning Human robot interaction Mapping and exploration Micro-robotics Mobile robotics Multi-robot coordination Robot control architectures Robot learning Techniques or Algorithms Coordination methods for multirobot systems Evaluations and implemented systems Fuzzy logic controllers Pomdp localization methods Subsumption architecture Techniques or Algorithms Analogical reasoning Boolean satisfiability Case-based reasoning Complexity analysis Constraint satisfaction Description logics Design, analysis or evaluation of ontologies Design and evaluation of implemented KR systems Game-playing methods Genetic algorithms Integer and constraint programming Logic programming and theorem proving Modal logics Model-based reasoning Parallel and distributed implementations Qualitative reasoning Search or optimization Significant applications Simulated annealing Temporal logics Machine Learning and Discovery Tasks or Problems Abstraction learning Active learning Computational learning theory Constructive induction Data mining Learning and planning Learning dynamics Learning in computational biology Learning in embedded systems Learning in information retrieval Learning on the Internet Online learning Reinforcement learning Scientific discovery Speedup learning Supervised learning Theory refinement Unsupervised learning Techniques or Algorithms Case-based learning Comparative analyses Decision-tree learning Empirical evaluation of learning algorithms Evaluations and implemented systems Evolutionary computation Genetic programming Inductive logic programming Learning algorithms with provable properties Learning belief networks Learning mixture models Multi-strategy learning Neural nets PAC learning and beyond Reinforcement learning algorithms Specialized learning algorithms Theory of model selection and evaluation Uncertainty in AI Tasks or Problems Computation and action under bounded resources Control of computational processes Decision making under uncertainty Decision-theoretic planning and reasoning Diagnosis: medical, mechanical, or software Enhancing the human-computer interface Integration of logical and probabilistic inference Learning and data mining Stochastic modeling Temporal reasoning Uncertain reasoning in embedded systems Techniques or Algorithms Abstraction in representation and inference Algorithms for learning and data mining Automated construction of decision models Automated explanation of results Beyond Markov models Comparative analyses of algorithms and systems Design and performance of architectures for real-time reasoning Discovery of causal relationships Economic models of problem solving Empirical validation of methods Evaluations and implemented systems Experience with knowledge-acquisition methods Formal languages Game-theoretic modeling Hybrid techniques MDPs and Bayesian Networks Qualitative methods and models Representing causality Specialized reasoning techniques Specialized representations Statistical methods Time-dependent utility functions Vision Tasks or Problems Active perception Analysis of medical images Face recognition Hand-eye coordination Image and video compression Image processing Object recognition Perception and learning Psychophysical modeling Visual recognition and tracking Visual scene analysis Techniques or Algorithms Evaluations and implemented systems Markov Random fields Neural net algorithms Optical flow techniques

Lecture 1CS250: Intro to AI/Lisp Conclusions AI is a young field with a tumultuous past Interdisciplinary Humans are really, really smart