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CMSC 471. History of AI. Chronology of AI, R&N Chapter 26. Adapted from slides by Tim Finin and Marie desJardins. Today’s class. History of AI Key people Significant events Future of AI Where are we going Philosophy of AI Can we build intelligent machines?

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History of ai l.jpg

CMSC 471

History of AI

Chronology of AI,

R&N Chapter 26

Adapted from slides by

Tim Finin and

Marie desJardins.

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Today’s class

  • History of AI

    • Key people

    • Significant events

  • Future of AI

    • Where are we going

  • Philosophy of AI

    • Can we build intelligent machines?

      • If we do, how will we know they’re intelligent?

    • Should we build intelligent machines?

      • If we do, how should we treat them…

      • …and how will they treat us?

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Key people (AI prehistory)

  • George Boole invented propositional logic (1847)

  • Karel Capek coined the term “robot” (1921)

  • Isaac Asimov wrote many sf books and essays (I, Robot (1950) introduced the Laws of Robotics – if you haven’t read it, you should!)

  • John von Neumann: minimax (1928), computer architecture (1945)

  • Alan Turing: universal machine (1937), Turing test (1950)

  • Norbert Wiener founded the field of cybernetics (1940s)

  • Marvin Minsky: neural nets (1951), AI founder, blocks world, Society of Mind

  • John McCarthy invented Lisp (1958) and coined the term AI (1957)

  • Allen Newell, Herbert Simon: GPS (1957), AI founders

  • Noam Chomsky: analytical approach to language (1950s)

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Key people (early AI history)

  • Hubert and Stuart Dreyfus: anti-AI specialists

  • Ed Feigenbaum: DENDRAL (first expert system, 1960s)

  • Terry Winograd: SHRDLU (blocks world, 1960s)

  • Roger Schank: conceptual dependency graphs, scripts (1970s)

  • Shakey: mobile robot (SRI, 1969)

  • Doug Lenat: AM, EURISKO (math discovery, 1970s)

  • Ed Shortliffe, Bruce Buchanan: MYCIN (uncertainty factors, 1970s)

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Key events: Genesis of AI

  • Turing test, proposed in 1950 and debated ever since

  • Neural networks, 1940s and 1950s, among the earliest theories of how we might reproduce intelligence

  • Logic Theorist and GPS, 1950s, early symbolic AI

  • Dartmouth University summer conference, 1956, established AI as a discipline

  • Early years: focus on search, learning, knowledge representation

  • Development of Lisp, late 1950s

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Key events: Adolescence of AI

  • The movie 2001: A Space Odyssey (1968) brought AI to the public’s attention

  • Early expert systems: DENDRAL, Meta-DENDRAL, MYCIN

  • Arthur Samuels’s checkers player, Doug Lenat’s AM and EURISKO systems, and Werbos’s and Rumelhart’s backpropagation algorithm held out hope for the ability of AI systems to learn

  • Hype surrounding expert systems led to an inevitable decline in interest in the mid to late 1980s, when it was realized they couldn’t do everything

  • Hype surrounding neural networks in the late 1980s led to similar disappointment in the 1990s

  • Roger Schank’s conceptual dependency theory and Doug Lenat’s Cyc started to address problems of common-sense reasoning and representation

  • Hans Berliner’s heuristic search player defeated the world backgammon champion in 1979

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Key events: AI adulthood (barely)

  • Many commercial expert systems introduced, especially in the 1970s and 1980s

  • Fuzzy logic and neural networks used in controllers, especially in Japan and Europe

  • Recent developments and areas of great interest include:

    • Bayesian reasoning and Bayes nets

    • Ontologies, knowledge reuse, and knowledge acquisition

    • Mixed-initiative systems that combine the best of human and computer reasoning

    • Multi-agent systems, Internet economies, intelligent agents

    • Autonomous systems for space exploration, search and rescue, hazardous environments

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What do AI people do?

  • Subject headings from IJCAI-01 conference proceedings:

    • Knowledge Representation and Reasoning

    • Search, Satisfiability, and Constraint Satisfaction

    • Cognitive Modeling

    • Planning

    • Games

    • Diagnosis

    • Logic Programming and Theorem Proving

    • Uncertainty and Probabilistic Reasoning

    • Neural Networks and Genetic Algorithms

    • Machine Learning and Data Mining

    • Case-Based Reasoning

    • Multi-Agent Systems

    • Natural Language Processing and Information Retrieval

    • Robotics and Perception

    • Web Applications

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Are we there yet?

  • Great strides have been made in knowledge representation and decision making

  • Many successful applications have been deployed to (help) solve specific problems

  • Key open areas remain:

    • Incorporating uncertain reasoning

    • Real-time deliberation and action

    • Perception (including language) and action (including speech)

    • Lifelong learning / knowledge acquisition

    • Common-sense knowledge

    • Methodologies for evaluating intelligent systems

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Philosophy of AI

Alan M. Turing, “Computing Machinery and Intelligence”

John R. Searle, “Minds, Brains, and Programs”

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Philosophical debates

  • What is AI, really?

    • What does an intelligent system look like?

    • Does an AI need—and can it have—emotions, consciousness, empathy, love?

  • Can we ever achieve AI, even in principle?

  • How will we know if we’ve done it?

  • If we can do it, should we?

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Turing test

  • Basic test:

    • Interrogator in one room, human in another, system in a third

    • Interrogator asks questions; human and system answer

    • Interrogator tries to guess which is which

    • If the system wins, it’s passed the Turing Test

  • The system doesn’t have to tell the truth (obviously…)

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Turing test objections

  • Objections are basically of two forms:

    • “No computer will ever be able to pass this test”

    • “Even if a computer passed this test, it wouldn’t be intelligent”

  • Chinese Room argument (Searle, 1980), responses, and counterresponses

    • Robot reply

    • Systems reply

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“Machines can’t think”

  • Theological objections

  • “It’s simply not possible, that’s all”

  • Arguments from incompleteness theorems

    • But people aren’t complete, are they?

  • Machines can’t be conscious or feel emotions

    • Reductionism doesn’t really answer the question: why can’t machines be conscious or feel emotions??

  • Machines don’t have Human Quality X

  • Machines just do what we tell them to do

    • Maybe people just do what their neurons tell them to do…

  • Machines are digital; people are analog

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“The Turing test isn’t meaningful”

  • Maybe so, but…

    If we don’t use the Turing test, what measure should we use?

  • Very much an open question…

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Ethical concerns: Robot behavior

  • How do we want our intelligent systems to behave?

  • How can we ensure they do so?

  • Asimov’s Three Laws of Robotics:

    • A robot may not injure a human being or, through inaction, allow a human being to come to harm.

    • A robot must obey orders given it by human beings except where such orders would conflict with the First Law.

    • A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

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Ethical concerns: Human behavior

  • Is it morally justified to create intelligent systems with these constraints?

    • As a secondary question, would it be possible to do so?

  • Should intelligent systems have free will? Can we prevent them from having free will??

  • Will intelligent systems have consciousness? (Strong AI)

    • If they do, will it drive them insane to be constrained by artificial ethics placed on them by humans?

  • If intelligent systems develop their own ethics and morality, will we like what they come up with?

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