lecture 1cs250: intro to ai/lisp computers & thought lecture 1 january 5th, 1999 cs250

Lecture 1 CS250: Intro to AI/Lisp

Computers & Thought

Lecture 1

January 5th, 1999

CS250


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


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 TuringCopyright (c) 1997. Maxfield & Montrose Interactive Inc.


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?


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"?


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


What Do You Need to Pass a Turing Test?

• Conversational skills (known as natural language processing, or simply NLP)

• Store of knowledge

• Reasoning

• Learning


Approaching AI

• Building a brain in a computer– Cognitive modeling

• High-school geometry approach– Logic & inference

• Agent approach– Rationality

• Experiential approach– Case-based reasoning


Other Fields & AI

• Philosophy– Questions of brain and mind– Consciousness– Logic


Mathematics

• Logic

• Theory of computability

• Probability


Logic in AI

• Inference– Possible reasoning process for cognition

• Representation– Logical statements– Observations about the world– States of the world


How hard is hard?

• Computability / complexity theory• How hard is a problem?


"What are the odds?"

• World is full of uncertainty

• Probability helps formalize uncertainty

• Bayes Theorem is key:

From http://members.tripod.com/~Probability/bayes01.htm


Psychology

• "Information processing view"– Response to behaviorism


Computer Engineering

• Sets AI apart from non-computer disciplines

• Focus on implementation


Linguistics

• Link between language and thought– MITECS article on Language of Thought

http://mitpress.mit.edu/MITECS/work/sterelny2_r.html


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


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


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


Natural Language Processing

• NLP is one of the most difficult tasks in AI (AI-complete)

• Why?– Ambiguity resolved by context– Computers lack context


Early AI

• 1952-1956: Samuel's checkers playing program beats Samuel

• Summer 1956 @ Dartmouth: Summer workshop with John McCarthy Marvin Minsky, Claude Shannon and others

• 1958– Lisp– Time sharing– Advice Taker


Knowledge-Based Systems

• Late 60's, early 70's: Big talk falls flat

• Knowledge-based systems– Know about the world

• DENDRAL

– Deduced molecular structure from mass spectrometry

– Encoded rules from experts

• MYCIN


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


Representational Systems

• Driven by big problems– Battlefield communication– Logistics– Campaign planning

• Scaling up– Prolog for rules– Frames (from MM) for structured

representations


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


Intelligent AgentsTasks 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 AlgorithmsAlgorithms 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 InterfacesTasks 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 AlgorithmsEvaluations and implemented systems Hidden Markov models Learning interaction models Learning user preferences Student modeling techniquesKnowledge Representation and Reasoning

Tasks or ProblemsCausal 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 ProcessingTasks or Problems

Dialog Discourse Generation Information extraction from the Web Machine translation Multimedia models Understanding

Techniques or AlgorithmsEvaluations and implemented systems Hidden Markov models Statistical or corpus based methodsPlanning, Scheduling and Control

Tasks or ProblemsActive 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 AlgorithmsComparative 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

RoboticsTasks 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 AlgorithmsCoordination 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 DiscoveryTasks 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 AlgorithmsCase-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 AITasks 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 AlgorithmsAbstraction 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

VisionTasks 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 AlgorithmsEvaluations and implemented systems Markov Random fields Neural net algorithms Optical flow techniques


Conclusions

• AI is a young field with a tumultuous past

• Interdisciplinary

• Humans are really, really smart

lecture 1cs250: intro to ai/lisp computers & thought lecture 1 january 5th, 1999 cs250

Documents

chinese speakers

language of thoughtsapir

chinese human

turing testhow

natural language processingnlp

native chinese speaker

statesalan turing copyright

computer programs