Introduction to Artificial Intelligence ITK 340, Spring 2012
For Thursday • Read Russell and Norvig, chapter 1 • Do chapter 1, exs 1 and 9 – There’s no single right answer for these. I’m looking for thoughtful multiple sentence responses.
Due Tuesday • Send email to mecaliff@ilstu.edu from your preferred email address • Student information sheet
Course Info • Instructor • Textbook • Syllabus • Students
What is AI, anyway? • Artificial Intelligence • The artificial part is easy-- we’re building machines and computer programs • Intelligence, however, is not well-defined • Some things that require great intelligence in human being are easy for computers • Other things that are easy for most (all?) humans are very difficult for computers
Categorizing the Definitions • Acting or thinking – Some definitions focus on thinking and reasoning, on the “mind” of the machine – Others focus on acting, on the behavior of the machine (whether there’s real thought behind it may not matter?) • Human or rational – Some definitions measure the computer against humans – Others focus on rationality--an ideal concept of intelligence
Thinking Humanly • “The exciting new effort to make computers think … machines with minds , in the full and literal sense” (Haugeland, 1985) • “[The automation of] activities that we associate with human thinking, activities such as decision-making, problem solving, learning …” (Bellman, 1978)
Thinking Humanly • The cognitive modeling approach • Interested not only in solving the problem, but also in mimicking human thought processes • This is where AI is most closely related to cognitive science
Acting Humanly • “The art of creating machines that perform functions that require intelligence when performed by people” (Kurzweil, 1990) • “The study of how to make computers do things at which, at the moment, people are better” (Rich and Knight, 1991)
Acting Humanly • The “Turing Test” Approach • Focus is on how the system behaves, not how it works inside • Performance is measured against human performance • Biggest problem is the question of the value of the test-- but we can’t pass it yet • Development of practical systems
Thinking Rationally • “The study of mental faculties through the use of computational models” (Charniak and McDermott, 1985) • “The study of the computations that make it possible to perceive, reason, and act” (Winston, 1992)
Thinking Rationally • The laws of thought approach • Focus on logic--making correct inferences • Problems – Difficulty of formulating some types of knowledge logically – Solving in principal vs. solving in practice • Strong contributions in reasoning and knowledge representation
Acting Rationally • “A field of study that seeks to explain and emulate intelligent behavior in terms of computational processes” (Schalkoff, 1990) • “The branch of computer science that is concerned with the automation of intelligent behavior” (Luger and Stubblefield, 1993)
Acting Rationally • The rational agent approach • Instead of thinking the right way, focuses on doing the right thing • More general than laws of thought • More testable than comparing to human behavior • Approach taken by your text
What Do You Know? • Examples of artificial intelligence in your life? • Can you name any of the areas of AI?
Foundations of AI
Foundations of AI • Philosophy • Mathematics • Economics • Neuroscience • Psychology • Computer engineering • Control theory and cybernetics • Linguistics
The Birth of AI • McCulloch and Pitts(1943) theory of neurons as competing circuits followed up by Hebb’s work on learning • Work in early 1950’s on game playing by Turing and Shannon and Minsky’s work on neural networks • Dartmouth Conference – Organizer: John McCarthy – Attendees: Minsky, Allen Newell, Herb Simon – Coined term artificial intelligence
Early Years • What was the mood of the early years?
Early Years • Development of the General Problem Solver by Newell and Simon in 1960s. • Arthur Samuel’s work on checkers in 1950s. • Frank Rosenblatt’s Perceptron (1962) for training simple networks
At MIT • Marvin Minsky and John McCarthy • Development of LISP • SAINT: solved freshman calculus problems • ANALOGY: solved IQ test analogy problems • SIR: answered simple questions in English • STUDENT: solved algebra story problems • SHRDLU: obeyed simple English commands in the blocks world
Early Limitations • Solved toy problems in ways that did not scale to realistic problems – Knowledge representation issues – Combinatorial explosion • Limitations of the perceptron were demonstrated by Minsky and Papert (1969)
Knowledge Is Power: The Rise of Expert Systems • Discovery that detailed knowledge of the specific domain can help control search and lead to expert level performance for restricted tasks • First expert system was DENDRAL. It interpreted mass spectogram data to determine molecular structure. Developed by Buchanan, Feigenbaum and Lederberg (1969).
Other Early Expert Systems • MYCIN: Diagnosis of bacterial infection (1975) • PROSPECTOR: Found molybdenum deposit based on geological data (1979) • R1: Configured computers for DEC (1982)
AI Becomes an Industry • Numerous expert systems developed in 80s • Estimated $2 billion by 1988 • Japanese Fifth Generation project started in 1981. • MCC founded in 1984 to counter Japanese. • Limitations become apparent: prediction of AI Winter – Brittleness and domain specificity – Knowledge acquisition bottleneck
Rebirth of Neural Networks • New algorithms (re)discovered for training more complex networks (1986) • Cognitive modeling • Industrial applications: – Character and hand-writing recognition – Speech recognition – Processing credit card applications – Financial prediction – Chemical process control
AI Becomes a Science • Empirical experiments the norm • Theoretical underpinnings are important • The “See what I can do” approach is no longer an acceptable method for doing research • Some movement toward learning/statistical methods.
Rise of Intelligent Agents • Why?
Popular Tasks of Today • Data mining • Intelligent agents and internet applications – softbots – believable agents – intelligent information access • Scheduling applications • Configuration applications
State of the Art • Deep Blue beats Kasparov • Sojourner, Spirit and Opportunity explore Mars • NASA Remote Agent in Deep Space I explores solar system • DARPA grand challenge: Autonomous vehicle navigates across desert and then urban environment. • Usable machine translation thru Google.
State of the Art • iRobot Roomba automated vacuum cleaner, and PackBot used in Afghanistan and Iraq wars • Automated speech/language systems on telephone. • Fairly accurate speech recognition • Spam filters using machine learning. • Question answering systems automatically answer factoid questions.
Views of AI • Weak vs. strong • Scruffy vs. neat • Engineering vs. cognitive
Recommend
More recommend
