4190.408 2015-spring artificial intelligence: introduction · 2016-04-17 · b io i ntelligence...

Bio

Intelligence4190.408 Artificial Intelligence (2016-Spring)

4190.408 2016-Spring

Artificial Intelligence: Introduction

Byoung-Tak Zhang

School of Computer Science and Engineering

Seoul National University

Bio


4190.408 Artificial Intelligence

• Instructor: Prof. Byoung-Tak Zhang ([email protected])

• TA: Seong-Ho Son ([email protected]) & Hyo-Sun Chun ([email protected])

• Classroom: 302-107

• Time: Tue & Thu 11:00-12:15

• Objectives:– To understand the theory and applications of artificial intelligence and cognitive science

– To acquire the technical tools for building intelligent agents, such as Bayesian networks, deep neural networks, and reinforcement learning.

– To understand the history and future prospects of artificial intelligence

• Textbook– Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig, 2010.

• References– A Tutorial on Learning with Bayesian Networks, David Heckerman

– Cognitive Neuroscience: The Biology of the Mind, Third Edition, M.S. Gazzaniga, R.B. Ivry, and G.R. Mangun, Norton & Company, 2008.

– Hypernetworks: A molecular evolutionary architecture for cognitive learning and memory, IEEE Computational Intelligence Magazine, 3(3):49-63, 2008.

http://bi.snu.ac.kr/Courses/4ai16s/4ai16s.html

mailto:[email protected]



./Courses/4ai16s/4ai16s.html

Bio


4190.408 Artificial Intelligence

• Evaluation:

– two exams (50%)

– two miniprojects (30%)

– project presentation (10%)

– participation in discussion (10%)

• Projects:

– Project 1: Bayesian networks

– Project 2: Deep neural networks

• Practice

– Bayesian Network (3/15 & 3/17)

– Deep Neural Network (T.B.A.)

• Topics– Brain, Mind & AI

– Bayesian Networks

– Problem Solving and Heuristic Search

– Knowledge Representation and Reasoning

– Natural Language Processing

– Logic, Symbolic AI, and Cognitive Science

– Deep Neural Networks

– Intelligent Agents

– Cognitive Robots

– Wearable AI

– Human-level AI

http://bi.snu.ac.kr/Courses/4ai16s/4ai16s.html

./Courses/4ai16s/4ai16s.html

Bio


4190.408 Artificial Intelligence 2016-Spring

AI History and Highlights

Byoung-Tak Zhang

School of Computer Science and Engineering

Seoul National University

Bio


Brief History of AI

• Early enthusiasm (1950’s & 1960’s)

– Turing test (1950)

– 1956 Dartmouth conference

– Emphasize on intelligent general problem solving

• Emphasis on knowledge (1970’s)

– Domain specific knowledge

– DENDRAL, MYCIN

• AI became an industry (late 1970’s & 1980’s)

– Knowledge-based systems or expert systems

– Wide applications in various domains

• Searching for alternative paradigms (late 1980’s - early 1990’s)

– AI’s Winter: limitations of symbolic/logical approaches

– New paradigms: neural networks, fuzzy logic, genetic algorithms, artificial life

• Resurge of AI (mid 1990’s – present)

– Internet, Information retrieval, data mining, bioinformatics

– Intelligent agents, autonomous robots

• Recent trends:

– Probabilistic computation

– Biological basis of intelligence

– Brain research, cognitive science

Bio


Turing’s Dream of Thinking Machines (1950)

• Can machine think?

• Alan Turing proposes the Turing test to decide if a computer is exhibiting intelligent behavior

– Turing, Alan M. "Computing machinery and intelligence." Mind (1950): 433-460.

• http://youtu.be/1uDa7jkIztw

Alan Turing (1912-1954)

http://youtu.be/1uDa7jkIztw

Bio


Birth of AI (1956)

• Dartmouth Conference 1956: "Artificial Intelligence“ gained its name– organized by Marvin Minsky, John McCarthy and two senior scientists: Claude Shannon

and Nathan Rochester of IBM

– proposal included this assertion: "every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it"

– Proposal: http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html

Five of the attendees of the 1956 Dartmouth Summer Research Project on Artificial Intelligence reunited at the July AI@50 conference. From left: Trenchard More, John McCarthy, Marvin Minsky, Oliver Selfridge, and Ray Solomonoff.http://www.dartmouth.edu/~vox/0607/0724/ai50.html

http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html

Bio


Deep Blue (1997)

• IBM’s Deep Blue computer beats Garry Kasparov, the world chess champion.

• Deep Blue can evaluate 200 million chess positions per second

• http://youtu.be/y9UMt-8gfW8

http://youtu.be/y9UMt-8gfW8

Bio


DARPA Grand Challenge (2005)

• A Stanford vehicle wins the DARPA Grand Challenge

• Driving autonomously across the desert for 131 miles

• Racing Video: http://youtu.be/M2AcMnfzpNg

• Stanford Racing Team: http://cs.stanford.edu/group/roadrunner//old/index.html

http://youtu.be/M2AcMnfzpNg

http://cs.stanford.edu/group/roadrunner/old/index.html

Bio


DARPA Urban Challenge (2007)

• Tartan Racing (CMU+GM) claimed the $2 million prize

• 96 km urban area course, to be completed < 6 hours

• Challenge involves mission planning, motion planning, behavior generation, perception, world modeling

• http://youtu.be/P0NTV2mbJhA

http://youtu.be/P0NTV2mbJhA

Bio


Google’s Driverless Car (2009)

• Uses artificial technology intelligence and makes decisions on its own (if mistake is made it will alert driver)

– Artificial Intelligence / Computer Vision / GPS / Google Maps / Various Sensors

• Test Driving: http://youtu.be/X0I5DHOETFE

• Ted by Sebastian Thrun: http://youtu.be/r_T-X4N7hVQ

http://youtu.be/X0I5DHOETFE

http://youtu.be/r_T-X4N7hVQ

Bio


IBM Watson wons “Jeopardy!” (2011)

• Watson, a supercomputer built by IBM, defeated the two greatest-ever Jeopardy champions

• Involves natural language processing, information retrieval, knowledge representation and reasoning, and machine learning

• Jeopardy!: http://youtu.be/WFR3lOm_xhE

• CogniToy’s dinosaur connected to Watson: http://youtu.be/1Q2v2rIpjTg

http://youtu.be/WFR3lOm_xhE

http://youtu.be/1Q2v2rIpjTg

Bio


Apple Siri: Personal Assistant (2011)

• an intelligent personal assistant and knowledge navigator which works as an application for Apple's iOS

• adapts to the user's individual preferences over time and personalizes results, and performing tasks such as finding recommendations for nearby restaurants, or getting directions

• http://youtu.be/8ciagGASro0

http://youtu.be/8ciagGASro0

Bio


The Next 50 Years: Human-Level AI

• To achieve a true human-level intelligence, brain-like information processing is required

Creative

Adaptive

Sociable

Versatile

Uncertain

Inattentive

Emotional

Illogical

1 + 2 = 5 !

100 < 10 ?

Bio


AI in Movies

• 2001 a Space Odyssey (1968)– HAL-9000, human-level artificial assistant

• Bicentennial Man (1999)– Android robot Andrew, household robot

– Emphasize humanity of AI robot

• I, Robot (2004)– Humanoid robots serve humanity by

obeying “Three Laws of Robotics”

– Inspired by Issac Asimov’s short-story collection in 1942

• A.I. (2006)– AI robot with emotion

• Iron Man 3 (2008)– JARVIS, an AI agent communicating and

interacting with humans

• Her (2013)– A haman falls in love with an AI computer

• Transcendence (2014)– A supercomputer into which human

consciousness is uploaded

Bio


What is Artificial Intelligence(AI)?

• Branch of computer science that is concerned with the automation of intelligent behavior

• Design and study of computer programs that behave intelligently• Study of how to make computers do things at which, at the

moment, people are better• Designing computer programs to make computers smarter• Develop programs that respond flexibly in situation that were not

specifically– e.g.) House-cleaning robots

• Perceive its surroundings• Navigate on the floor• Respond to events• Decide what to do next• Space exploration

• Synonyms of AI: machine intelligence

Bio


What is Artificial Intelligence(AI)?

• AI is a collection of hard problems which can be solved by humans and other living things, but for which we don’t have good algorithms for solving. – e. g., understanding spoken natural language, medical diagnosis, circuit design, learning, self-

adaptation, reasoning, chess playing, proving math theories, etc.

• Definition from R & N book: a program that– Acts like human (Turing test)– Thinks like human (human-like patterns of thinking steps)– Acts or thinks rationally (logically, correctly)

• Some problems used to be thought of as AI but are now considered not– e. g., compiling Fortran in 1955, symbolic mathematics in 1965, pattern recognition in 1970

Bio


Research Areas and Approaches

ArtificialIntelligence

Research

Rationalism (Logical)Empiricism (Statistical)Connectionism (Neural)Evolutionary (Genetic)Biological (Molecular)

Paradigm

Application

Intelligent AgentsInformation RetrievalElectronic CommerceData MiningBioinformaticsNatural Language Proc.Expert Systems

Learning AlgorithmsInference MechanismsKnowledge RepresentationIntelligent System Architecture

Bio


Paradigms for Artificial Intelligence

Symbolic AI Rule-Based Systems

Connectionist AI Neural Networks

Evolutionary AI Genetic Algorithms

Molecular AI: DNA Computing

Bio


Paradigms for Computational Intelligence

Symbolism Connectionism DynamicismHyper-

interactionism

Metaphor Symbol system

Neural system

Dynamical system

Biomolecularsystem

Mechanism Logical Electrical Mechanical Chemical

Description Syntactic functional Behavioral Relational

Representation Localist Distributed Continuous Collective

Organization Structural Connectionist Differential Combinatorial

Adaptation Substitution Tuning Rate change Self-assembly

Processing Sequential Parallel Dynamical Massively parallel

Structure Procedure Network Equation Hypergraph

Mathematics Logical, formal language

Linear algebra, statistics

Geometry, calculus

Graph theory, probabilistic logic

Space/time Formal Spatial Temporal Spatiotemporal

[Zhang, IEEE CIM, 2008]

Bio


4190.408 Artificial Intelligence 2015-Spring

AI History and Highlights: Appendix

Biointelligence Lab, SNU

Bio


Acting Humanly: Turing test

• Turing (1950) “Computing machinery and intelligence”:– “Can machine think?” “Can machine behave intelligently?”– Operational test for intelligent behavior: the Imitation Game

– Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes

– Anticipated all major arguments against AI in following 50 years– Suggested major components of AI: knowledge, reasoning, language

understanding, learning

• Problem: Turing test is not reproducible, constructive, or amenable to mathematical analysis

[Stuart Russell's (Berkeley) course slides]

Bio


Thinking Humanly: Cognitive Science

• 1960s “Cognitive Revolution”: information-processing psychology replaced prevailing orthodoxy of behaviorism

• Requires scientific theories of internal activities of the brain

– What level of abstraction? “Knowledge” or “Circuits”?

– How to validate? Requires

• Predicting and testing behavior of human subjects (top-down)

• Direct identification from neurological data (bottom-up)

• Both approaches (roughly, Cognitive Science and Cognitive Neuroscience) are now distinct from AI

• Both share with AI the following characteristic:

– The available theories do not explain (or engender) anything resembling human-level general intelligence

• Hence, all three fields share one principal direction!


Bio


Thinking Rationally: Laws of Thought

• Normative (or prescriptive) rather than descriptive

• Aristotle (~ 450 B.C.): What are correct arguments/thought processes?

• Several Greek schools developed various forms of logic:

– notation plus rules of derivation for thoughts;

– May or may not have proceeded to the idea of mechanization

• Direct line through mathematics and philosophy to modern AI

• Problems:

– Not all intelligent behavior is mediated by logical deliberation

– What is the purpose of thinking? What thoughts should I have out of all the thoughts (logical or otherwise) that I could have?


Bio


Acting Rationally: The Rational Agent

• Rational behavior: doing the right thing

• The right thing: that which is expected to maximize goal achievement, given the available information

• Doesn’t necessarily involve thinking – e.g., blinking reflex – but thinking should be in the service of rational action

• Aristotle (Nicomachean Ethics):– Every art and every inquiry, and similarly every action and pursuit, is thought

to aim at some good


Bio


Brief history of AI - Golden years 1956-74

• Research:– Reasoning as search: Newell and Simon developed a program

called the "General Problem Solver".

– Natural language Processing: Ross Quillian proposed the semantic networks and Margaret Masterman & colleagues at Cambridge design semantic networks for machine translation

– Lisp: John McCarthy (MIT) invented the Lisp language.

• Funding for AI research:– Significant funding from both USA and UK governments

• The optimism:– 1965, Simon: "machines will be capable, within twenty years, of

doing any work a man can do

– 1970, Minsky: "In from three to eight years we will have a machine with the general intelligence of an average human being."

[Xiao-Jun Zeng’s (Univ. of Manchester) course slides]

Bio


Brief history of AI - The first AI winter

• The first AI winter 1974−1980:– Problems

• Limited computer power: There was not enough memory or processing speed to accomplish anything truly useful

• Intractability and the combinatorial explosion. In 1972 Richard Karp showed there are many problems that can probably only be solved in exponential time (in the size of the inputs).

• Commonsense knowledge and reasoning. Many important applications like vision or natural language require simply enormous amounts of information about the world and handling uncertainty.

– Critiques from across campus

• Several philosophers had strong objections to the claims being made by AI researchers and the promised results failed to materialize

– The end of funding

• The agencies which funded AI research became frustrated with the lack of progress and eventually cut off most funding for AI research.


Bio


Brief history of AI - Boom 1980–1987

• Boom 1980–1987:

– In the 1980s a form of AI program called "expert systems" was

adopted by corporations around the world and knowledge

representation became the focus of mainstream AI research

• The power of expert systems came from the expert knowledge using

rules that are derived from the domain experts

• In 1980, an expert system called XCON was completed for the Digital

Equipment Corporation. It was an enormous success: it was saving

the company 40 million dollars annually by 1986

• By 1985 the market for AI had reached over a billion dollars

– The money returns: the fifth generation project

• Japan aggressively funded AI within its fifth generation computer

project (but based on another AI programming language - Prolog

created by Colmerauer in 1972)

• This inspired the U.S and UK governments to restore funding for AI

research


Bio


Brief history of AI - the second AI winter

• the second AI winter 1987−1993

– In 1987, the Lisp Machine market was collapsed, as desktop

computers from Apple and IBM had been steadily gaining speed

and power and in 1987 they became more powerful than the more

expensive Lisp machines made by Symbolics and others

– Eventually the earliest successful expert systems, such as XCON,

proved too expensive to maintain, due to difficult to update and

unable to learn.

– In the late 80s and early 90s, funding for AI has been deeply cut

due to the limitations of the expert systems and the expectations

for Japan's Fifth Generation Project not being met

– Nouvelle AI: But in the late 80s, a completely new approach to AI,

based on robotics, has bee proposed by Brooks in his paper

"Elephants Don't Play Chess”, based on the belief that, to show

real intelligence, a machine needs to have a body — it needs to

perceive, move, survive and deal with the world.


Bio


Brief history of AI - AI 1993−present

• AI achieved its greatest successes, albeit somewhat

behind the scenes, due to:

– the incredible power of computers today

– a greater emphasis on solving specific subproblems

– the creation of new ties between AI and other fields working on

similar problems

– a new commitment by researchers to solid mathematical methods

and rigorous scientific standards, in particular, based probability

and statistical theories

– Significant progress has been achieved in neural networks,

probabilistic methods for uncertain reasoning and statistical

machine learning, machine perception (computer vision and

Speech), optimisation and evolutionary computation, fuzzy

systems, Intelligent agents.


Bio


AI in Movies: 2001 a Space Odyssey

• 2001 a Space Odyssey (1968, Stanley Kubrick)

• HAL-9000, 공상과학영화속의인간수준인공지능비서

– 우주선 Discovery의관제와승무원보호를담당

– 현재상황을인식하고추론, 미래를예측하여행동을수행

– 미래를예측하고이를바탕으로행동하는능력은인간수준인공지능의핵심적인자질

[Movie clip] [HAL 9000: AI system]

[Byoung-Tak Zhang’s Doosan seminar slides]

1-space odyssey.avi

1-space odyssey.avi

Bio


AI in Movies: Star Trek

• Star Trek (1973 ~ 2013)• Lieutenant Commander Data

– One of main characters of Star Trek

– Artificial intelligence android with self-consciousness

Cold-minded Android

Human-like Android

Continuously learns how human acts

[Movie clip]

[Data: AI android]


2_star trek.avi

2_star trek.avi

Bio


AI in Movies: Bicentennial Man

• Bicentennial Man (1999)

• Android robot Andrew who is purchased as a household robot

• Emphasize “humanity” of AI robot– If a robot spends enough

time around humans, can he learn to become one of them?

– Emotion, Creativity, Curiosity, Achievement Need, Love …


Bio


AI in Movies: I, Robot

• I, Robot (2004)

• What is intelligence?– Information processing

– Creativity, dreaming, free will, spirit

– An exceptional result just error?

– A.I. & robot: indispensability for each other

• Three laws of robotics (Isaac Asimov, 1942)

– These rules might occur unexpected problems.

– A.I. with exceptional results need to be studied.

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

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

3. A robot must protect its own existence as long as such protectiondoes not conflict with the First or Second Law.

[Movie clip]


3_I Robot.avi

3_I Robot.avi

Bio


AI in Movies: A.I.

• A.I. (2006)• AI robot with emotion

– “David”– Perception, cognition,

and action like humans– Influence of the

emotion on thinking– Active goal setting and

planed behavior– Learning and

self-improving from the experiences


Bio


AI in Movies: Iron Man 3

Iron Man 3 (2008) J.A.R.V.I.S.

AI agent communicating andinteracting with humans

Information gathering fromsensors and internet

Priority Speech recognition Context-aware Object recognition Gesture recognition Active learning Future prediction based

from the data


Bio


AI in Movies: Surrogate (2009)

• Surrogate (2009)• Artificial lifeforms

that can link up with humans– Mankind stays at

home and operates surrogates

– Go out into the world without having to deal with dangers

• Surrogate does not have AI

• kind of another body like avatar


Bio


AI in Movies: Her

• Her (2013)

• A human falls in love with an AI computer

• Human-like intelligence– Personal assistant, companion, lover,

composer, coach

– Interact with us, learn with us and ultimately express sentiments and creativity

Learn Concept

Understanding

Reasoning

Creative Artistic

Musical

Interact Cognition

Recognition

Consciousness

Express Perception

Self-aware

Communication

Human

AI System


4_Her.avi

4_Her.avi

4190.408 2015-spring artificial intelligence: introduction · 2016-04-17 · b io i ntelligence...

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