Artificial Intelligence

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Artificial Intelligence

• CS 165A
• Fall 2004
• Lecture 1
• Prof. Terry Smith

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Goals of this course

• To teach you the main ideas of AI
• To introduce you to a set of key techniques and
  algorithms from AI
• To help you understand whats hard in AI and why
• To see how AI relates to the rest of computer
  science
• To get you thinking about how AI can be applied
  to a variety of real problems
• To have fun

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Course administrivia

• Web sites
• http//www.cs.ucsb.edu/cs165a
• http//groups.yahoo.com/group/UCSB-CS165A
• Syllabus
• Discussion sessions
• Schedule
• Assignments
• Assignment 0 due on Tuesday!
• Expectations
• Come to class, and come prepared
• Participate Ask questions, offer insight, tell
  me Im wrong...
• Think!

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What is Artificial Intelligence?

• AI in the media
• Popular movies
• 2001 A Space Odyssey
• Star Trek
• The Terminator
• AI The Movie
• Popular press, novels
• Often portrayed as
• A property of evil computers
• Computers doing impossible things
• Public view
• Books and movies have inspired many AI
  researchers
• Books and movies have raised the publics
  expectations

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What is Artificial Intelligence? (cont.)

• The science and engineering of making
  intelligent machines, especially intelligent
  computer programs.
• The business of getting computers to do things
  they cannot already do, or things they can only
  do in movies and science fiction stories.
• The study of how to make computers do things at
  which, at the moment, people are better.
• The design of flexible programs that respond
  productively in situations that were not
  specifically anticipated by the designer.
• The construction of computations that perceive,
  reason, and act effectively in uncertain
  environments.
• The branch of CS concerned with enabling
  computers to simulate such aspects of human
  intelligence as speech recognition, deduction,
  inference, creative response, the ability to
  learn from experience, and the ability to make
  inferences given incomplete information.
• Modeling aspects of human cognition on
  computers
• What AI people do

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Goals of AI

• Scientific
• To understand the principles and mechanisms that
  account for intelligent action
• Engineering
• To design intelligent systems that can survive
  and operate in the real world and solve problems
  of considerable scientific difficulty at high
  levels of competence

To create models and mechanisms of intelligent
action
To understand and build intelligent systems
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Intelligent systems

• An intelligent system is characterized as one
  that can
• Exhibit adaptive, goal-oriented behavior
• Learn from experience
• Use vast amounts of knowledge
• Exhibit self-awareness
• Interact with humans using language and speech
• Tolerate error and ambiguity in communication
• Respond in real-time

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What AI people study

• Logic
• Knowledge representation
• Search
• Reasoning/inference
• Non-monotonic reasoning
• Planning
• Probabilistic reasoning
• Naïve physics
• Machine learning

• Speech recognition
• Natural language processing
• Computer vision
• Pattern recognition
• Intelligent agents
• Robotics
• Neural networks
• Data mining
• Expert systems

and more
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What AI people (and programs) do

• Prove theorems
• Emulate/model human cognitive abilities
• (Attempt to) solve exponentially hard problems
• Build expert systems for diagnostic tasks (e.g,
  medical diagnosis, error analysis)
• Build robots
• Build machine vision systems for industrial
  tasks, surveillance, consumer apps, etc.

• Create speech recognition and understanding
  systems for various domains
• Process text to understand, summarize, correct,
  respond, etc.
• Create data mining systems to process very large
  amounts of information (e.g., bioinformatics)
• Build intelligent agents to look and act in
  socially useful ways
• Develop computer games

and more
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Some notable AI systems

• IBMs Deep Blue
• Beat world chess champion Gary Kasparov in 1997
• Kasparov vs. (Israeli-built) Deep Junior, January
  2003 (ended in a draw)
• Kasparov vs. X3D Fritz, November 2003
• Expert systems
• Medical diagnosis
• A computerized Leukemia diagnosis system did a
  better job checking for blood disorders than
  human experts
• Speech recognition
• Commercial systems by Dragon, IBM, and others
• Phone-based systems (e.g., airline reservations)
• Automatic scheduling for manufacturing operations
• User interface
• Grammar and spelling checkers, automated help

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Some notable AI systems (cont.)

• Data mining
• Fraud detection, credit scoring, customer
  profiles and preferences, genome analysis
• Cyc
• Doug Lenats 18-year old project to give computer
  common sense
• Computer vision
• E.g., Hands Across America 1995
• Face recognition systems for biometrics
• Robotics
• Mars Rover, robots for hazard environments,
  factory automation
• Sony, Honda, others robot pets
• CMU Navlab drove across country (2797/2849 miles)
• 1980s DARPA ALV Program
• DARPA Grand Challenge 2004
• Failed in 2004repeat in October 2005

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DARPA Grand Challenge
http//www.darpa.mil/grandchallenge

• DARPA intends to conduct a challenge of
  autonomous ground vehicles between Los Angeles
  and Las Vegas in March of 2004. A cash award of
  1 million will be granted to the team that
  fields the first vehicle to complete the
  designated route within a specified time limit.

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Terrain between LA and Las Vegas
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Perspectives on AI / Disciplines involved

• AI functions as a channel of ideas between
  computing and other fields, ideas that have
  profoundly changed those fields
• Logic
• Mathematics
• Statistics
• Philosophy
• Psychology
• Linguistics
• Neuroscience
• Computer science
• Cognitive science

AI
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Foundations of AI

• Philosophy
• Framed the ideas of AI
• Dualism/materialism, logical/rational/empirical,
  causality, consciousness, mind/body
• Mathematics
• Formalized computation, logic, probability
• Possibilities and limitations of computation
• Psychology
• Experimental the brain as an information
  processing device (Cognitive Science)
• Computer Engineering
• Built real machines, Moores Law progress

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AI and Computer Science

• AI is mostly about software (usually large and
  complex)
• Important Algorithms, tools, complexity, etc.
• Early advanced in CS due to AI researchers
  include
• Search algorithms
• List structures, pointers
• Virtual memory
• Dynamic memory allocation
• Garbage collection
• Logical programming
• CS 165A will be taught primarily from a CS
  perspective
• Not the only perspective, though

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UCSB CS AI Sequence 165A and 165B

• 165A. Artificial Intelligence (Fall)
• (4) TURK
• Prerequisites Computer Science 130A open to
  computer science majors only
• An introduction to the field of Artificial
  Intelligence, which attempts to understand and
  build intelligent systems. Topics include AI
  programming languages, search, logic, knowledge
  representation and reasoning, game playing,
  planning, uncertainty, perception, and
  intelligent agents. 
• 165B. Machine Learning (Winter)
• (4) SMITH / SU
• Prerequisites Computer Science 165A
• The course covers the most important techniques
  of machine learning (ML) and includes discussions
  of well-posed learning problems artificial
  neural networks concept learning and general to
  specific ordering decision tree learning
  genetic algorithms learning sets of rules
  Bayesian learning analytical learning and
  combining inductive and analytical learning. The
  course integrates these approaches to learning
  with fundamental aspects of machine intelligence
  (MI), including search, knowledge representation
  and reasoning, and applications. 

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Proper background

• Blind search (depth-first, breadth-first)
• CS 130A
• Trees (programming)
• CS 20, 50, 130A
• Boolean logic, Propositional logic, First-order
  logic
• CS 40
• Probability, Bayes rule
• PSTAT 120A
• Parsing
• CS 20, 160 (some)
• C / Java
• several

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AI A I

• Artificial
• As in artificial flowers or artificial light?
• Intelligence
• What is intelligence?
• The capacity to acquire and apply knowledge
• The faculty of thought and reason
• Secret information, especially about an actual or
  potential enemy
• Symbol manipulation, grounded in perception of
  the world
• The computational part of the ability to achieve
  goals in the world
• What makes someone more/less intelligent than
  another?
• Are monkeys, ants, trees, babies, chess
  programs intelligent?
• How can we know if a machine is intelligent?

Turing Test (Alan Turing, 1950), a.k.a. The
Imitation Game
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Replicating human intelligence?

• AI doesnt necessarily seek to replicate human
  intelligence
• Sometimes more, sometimes less
• Essence of X vs. X
• Examples
• Physical vs. electronic newspaper
• Physical vs. virtual shopping
• Birds vs. planes

• Saying Deep Blue doesnt really think about
  chess is like saying an airplane doesnt really
  fly because is doesnt flap its wings.
• Drew McDermott

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Strong AI vs. Weak AI

• Strong AI
• Makes the bold claim that computers can be made
  to think on a level (at least) equal to humans
• One version The Physical Symbol System
  Hypothesis
• A physical symbol system has the necessary and
  sufficient means for general intelligent action
• Intelligence symbol manipulation (perhaps
  grounded in perception and action)
• Weak AI
• Some thinking-like features can be added to
  computers to make them more useful tools
• Examples expert systems, speech recognition,
  natural language understanding.

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Strong AI vs. Weak AI (cont.)

• Principles of Strong AI
• Intelligent behavior is explicable in scientific
  terms a rigorous understanding of intelligence
  is possible
• Intelligence can take place outside the human
  skull
• The computer is the best laboratory instrument
  for exploring these propositions
• Maybe
• Strong AI is science?
• Weak AI is engineering?

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Philosophical and ethical implications

• Is Strong AI possible?
• If so (or even if not)
• Should we be worried? Is this technology a
  threat? (Bill Joy)
• Is it okay to kill an intelligent machine?
• When will it happen? (Will we know?)
• Will they keep us around? (Kurzweil, Moravec)
• Might we become too dependent on technology?
• Terrorism, privacy
• Main categories of objections to AI
• Nonsensical (Searle)
• Impossible (Penrose)
• Unethical, immoral, dangerous (Weizenbaum)
• Failed (Wall Street)

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Another way of looking at AI
Human
Ideal
Systems that think like humans
Systems that think rationally
Thought processes and reasoning
Systems that act like humans
Systems that act rationally
Behavior
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Human/Biological Intelligence

• Thinking humanly (Cognitive modeling)
• Cognitive science
• 1960s Information processing replaced
  behaviorism as the dominant view in psychology
• Cognitive neuroscience
• Neurophysiological basis of intelligence and
  behavior?
• Acting humanly (Operational intelligence)
• The Turing Test operational test for
  intelligent behavior
• What does it require?
• Required knowledge, reasoning, language
  understanding, learning
• Problem It is not reproducible or amenable to
  mathematical analysis rather subjective

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Ideal/Abstract Intelligence

• Thinking rationally (Laws of Thought)
• Rational thought governed by Laws of Thought
• Logic approach mathematics and philosophy
• Acting rationally (Rational agents)
• Rational behavior doing the right thing
• Maximize goal achievement, given the available
  information (knowledge perception)
• Can/should include reflexive behavior, not just
  thinking
• General rationality vs. limited rationality
• Basic definition of agent something that
  perceives and acts

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How can you tell its AI?

• It does something that is clearly human-like
• or
• Separation of
• data/knowledge
• operations/rules
• control
• Has
• a knowledge representation framework
• problem-solving and inference methods

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Why study AI?

• Its fascinating
• Deep questions about intelligence, consciousness,
  the nature of being human
• Grand challenges creating intelligent machines
• Multidisciplinary endeavor
• It leads to a different perspective on computer
  science issues
• Levels of explanation
• Search, problem solving, etc. higher level
  approach
• Exponential, NP-hard problems
• Its good background for related areas
• Computer vision, speech recognition, natural
  language understanding, probabilistic reasoning
  systems, machine learning, etc.

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A quote

• The hardest applications and most challenging
  problems, throughout many years of computer
  history, have been in artificial intelligence
  AI has been the most fruitful source of
  techniques in computer science. It led to many
  important advances, like data structures and list
  processing... artificial intelligence has been a
  great stimulation. Many of the best paradigms for
  debugging and for getting software going, all of
  the symbolic algebra systems that were built,
  early studies of computer graphics and computer
  vision, etc., all had very strong roots in
  artificial intelligence.
• Donald Knuth

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Will it get me a job?

• Well.
• Not as many AI jobs as Java programming jobs.
• But
• See web site (Announcements) for relevant
  articles
• AI is a component of many advanced technologies
• A thorough understanding of the concepts covered
  in the course will make you a better computer
  scientist
• You will have a broader array of tools with which
  to approach problems
• You will better be able to evaluate technologies
  with AI components
• AI related research usually requires a graduate
  degree

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A note on AI programming

• Lisp
• List processing
• Interpreter great for fast prototyping
• Features garbage collection, dynamic typing, .
• Prolog
• Logic programming
• Program set of logical statements general
  theorem prover
• Other high-level languages (Java, C, etc.)

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Top AI Schools and Companies

• Top AI Schools
• Stanford University
• MIT
• Carnegie Mellon University (CMU)
• Berkeley
• Also Toronto, Washington, Illinois, Texas,
  Maryland, Edinburgh, UCLA, Karlsruhe, and many
  others.
• Top research labs
• Microsoft Research (MSR)
• IBM Research
• ATT Labs
• Xerox PARC, SRI, ATR (Japan),

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Reminders

• Peruse the course web site
• Join the Yahoo group
• Keep up with assigned reading
• Assignment 0 due Tuesday
• First discussion session next Wed., 3pm
• Review of relevant prerequisites data
  structures, probability and statistics, logic
• Info on using CSIL (if necessary)

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Tuesday Quiz

• Name a discipline that has significantly
  contributed to the historical foundations of AI
• Briefly, how did it contribute to AI?
• Alexander Kronrod, a Russian AI researcher, said
  Chess is the Drosophila of AI.
• Briefly explain this statement.