1 / 34

CS B351 : Intro to Artificial Intelligence and Computer Simulation

CS B351 : Intro to Artificial Intelligence and Computer Simulation. Instructor: Kris Hauser http://cs.indiana.edu/~hauserk. Basics. Class web site http:// cs.indiana.edu/classes/b351 Textbook S. Russell and P. Norvig Artificial Intelligence: a Modern Approach 3 rd edition. Basics.

emil
Download Presentation

CS B351 : Intro to Artificial Intelligence and Computer Simulation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. CS B351: Intro to Artificial Intelligence and Computer Simulation Instructor: Kris Hauser http://cs.indiana.edu/~hauserk

  2. Basics • Class web site • http://cs.indiana.edu/classes/b351 • Textbook • S. Russell and P. Norvig • Artificial Intelligence: a Modern Approach • 3rdedition

  3. Basics • Instructor • Kris Hauser (hauserk@indiana.edu) • AIs • Dan Coroian(dcoroian@indiana.edu)

  4. Office Hours • Kris Hauser • M 2-3,Th 2-3 in Info E 257 (connector building) • Dan Coroian • TBA

  5. Agenda • Intro to AI • Overview of class policies

  6. What is AI? • AI is the reproduction of human reasoning and intelligent behavior by computational methods

  7. What is AI? • AI is an attempt of reproduction of human reasoning and intelligent behavior by computational methods

  8. What is AI? • Discipline that systematizes and automates reasoning processes to create machines that:

  9. The goal of AI is: to build machines that operate in the same way that humans think • How do humans think? • Build machines according to theory, test how behavior matches mind’s behavior • Cognitive Science • Manipulation of symbolic knowledge • How does hardware affect reasoning? Discrete machines, analog minds

  10. The goal of AI is: to build machines that perform tasks that seem to require intelligence when performed by humans • Take a task at which people are better, e.g.: • Prove a theorem • Play chess • Plan a surgical operation • Diagnose a disease • Navigate in a building • and build a computer system that does it automatically • But do we want to duplicate human imperfections?

  11. The goal of AI is: to build machines that make the “best” decisions given current knowledge and resources • “Best” depending on some utility function • Influences from economics, control theory • How do self-consciousness, hopes, fears, compulsions, etc. impact intelligence? • Where do utilities come from?

  12. What is Intelligence? “If there were machines which bore a resemblance to our bodies and imitated our actions as closely as possible for all practical purposes, we should still have two very certain means of recognizing that they were not real men. The first is that they could never use words, or put together signs, as we do in order to declare our thoughts to others… Secondly, even though some machines might do some things as well as we do them, or perhaps even better, they would inevitably fail in others, which would reveal that they are acting not from understanding, …” Discourse on the Method, by Descartes (1598-1650)

  13. What is Intelligence? • Turing Test (c. 1950)

  14. What is intelligence?

  15. An Application of the Turing Test • CAPTCHA: Completely Automatic Public Turing tests to tell Computers and Humans Apart

  16. Chinese Room (John Searle)

  17. Can Machines Act/Think Intelligently? • Yes, if intelligence is narrowly defined as information processingAI has made impressive achievements showing that tasks initially assumed to require intelligence can be automated Each success of AI seems to push further the limits of what we consider “intelligence”

  18. Some Achievements • Computers have won over world champions in several games, including Checkers, Othello, and Chess, but still do not do well in Go • AI techniques are used in many systems: formal calculus, video games, route planning, logistics planning, pharmaceutical drug design, medical diagnosis, hardware and software trouble-shooting, speech recognition, traffic monitoring, facial recognition, medical image analysis, part inspection, etc... • DARPA Grand Challenge: robotic car autonomously traversed 132 miles of desert • IBM’s Watson competes with Jeopardy champs • Some industries (automobile, electronics) are highly robotized, while other robots perform brain and heart surgery, are rolling on Mars, fly autonomously, …, but home robots still remain a thing of the future 18

  19. Can Machines Act/Think Intelligently? • Yes, if intelligence is narrowly defined as information processingAI has made impressive achievements showing that tasks initially assumed to require intelligence can be automated • Maybe yes, maybe not, if intelligence cannot be separated from consciousness • Is the machine experiencing thought? • Strong vs. Weak AI

  20. Big Open Questions • Is intelligent behavior just information processing?(Physical symbol system hypothesis) • If so, can the human brain solve problems that are inherently intractable for computers? Will a general theory of intelligence emerge from neuroscience? • In a human being, where is the interface between “intelligence” and the rest of “human nature” • Self-consciousness, emotions, compulsions • What is the role of the body?(Mind-body problem)

  21. AI contributes to building an information processing model of human beings, just as Biochemistry contributes to building a model of human beings based on bio-molecular interactions Both try to explain how a human being operates Both also explore ways to avoid human imperfections (in Biochemistry, by engineering new proteins and drug molecules; in AI, by designing rational reasoning methods) Both try to produce new useful technologies Neither explains (yet?) the true meaning of being human

  22. Main Areas of AI • Knowledge representation (including formal logic) • Search, especially heuristic search (puzzles, games) • Planning • Reasoning under uncertainty, including probabilistic reasoning • Learning • Robotics and perception • Natural language processing Agent Perception Robotics Reasoning Search Learning Knowledgerep. Constraintsatisfaction Planning Naturallanguage ... Expert Systems

  23. Bits of History • 1956: The name “Artificial Intelligence” is coined • 60’s: Search and games, formal logic and theorem proving • 70’s: Robotics, perception, knowledge representation, expert systems • 80’s: More expert systems, AI becomes an industry • 90’s: Rational agents, probabilistic reasoning, machine learning • 00’s: Systems integrating many AI methods, machine learning, natural language processing, reasoning under uncertainty, robotics again

  24. AI References • Conferences • IJCAI, ECAI, AAAI, NIPS • Journals • AI, Comp. I, IEEE Trans. Pattern Anal. Mach. Intel., IEEE Int. Sys., JAIR • Societies • AAAI, SIGART, AISB • AI Magazine (Editor: IU’s David Leake)

  25. Careers in AI • ‘Pure’ AI • Academia, industry labs • Applied AI • Almost any area of CS! • NLP, vision, robotics • Economics • Cognitive Science

  26. Syllabus • Introduction to AI • Philosophy, history, agent frameworks • Search • Uninformed search, heuristic search, heuristics, game playing • Reasoning under uncertainty • Probability, planning under uncertainty, Bayesian networks, probabilistic inference, temporal sequences • Machine learning • Neural nets, decision tree learning, support vector machines, etc. • Applications • Constraint satisfaction, motion planning, computer vision

  27. Class Policies

  28. Prerequisites • C211 • I recommend: • Two semesters programming • Basic knowledge of data structures • Basic knowledge of algorithmic complexity

  29. Programming Assignments • Projects will be written in Python • Easy to learn • 2 weeks for each assignment • See Resources tab on class webpage for helpful links

  30. Grading • 50% Homework • 6 assignments, lowest score will be dropped • 30% Final • 15% Midterm • 5% Participation

  31. Homework Policy • Due at end of class on due date • Typically Thursdays • No “slip days” • Extensions only granted in rare cases • Require advance notice except emergencies

  32. Final Project • Encouraged if you are intending to do research or coursework in AI, pursue higher degree • Individual or small groups (up to 3) • Counts as three homework assignments • Content • Software, new research, or technical report • Mid-semester project proposal • End-of-year report and in-class presentation

  33. Takeaways • AI has many interpretations • Act vs. think, human-like vs. rational • Concept has evolved • “Intelligence” has many interpretations • Turing test • Chinese room • AI success stories from each perspective

  34. Homework • Register • Textbook • http://cs.indiana.edu/classes/b351 • Readings: • R&N Ch. 1, 26 (introduction and historical perspectives) • R&N 3.1-3

More Related