Artificial Intelligence

1. Artificial Intelligence What is AI? Issues in AI

2. An Overview - AI is a science of making intelligent machines - Intelligence is a type of computation: What is a computation?  Turing Machines - How do we know if a machine is intelligent or not?  Turing Test

3. 1. What is AI? • Artificial intelligence is the science and engineering of making computer programs that exhibit characteristics of human intelligence. • Scientific aim: To understand the requirements for and mechanisms of human, animal, machine, robotic intelligence • Engineering aim: To apply such knowledge in building useful artifacts (machines & robots) capable to do things done by humans or animals

4. What is intelligence? - Intelligence is the computational part of the ability to solve problems and achieve goals in the world in an efficientmanner (McCarthy) - ‘Computational part .. to do … efficiently’  Algorithm (e.g.) Tower of Hanoi Problem: Tic-tac-toe: Chess: No. of all possible board states: 10120!! - Combinatorial explosion problem - Blind search – intractable

5. Tower of Hanoi

6. Branches of AI Knowledge representation • Processing information about and representing facts about the world in some abstract way Pattern recognition • Extracting knowledge from images (e.g., letters, face, X-ray data, satellite photos) Reasoning and inference • Deriving conclusions from premises or incomplete observations (e.g., logical deduction, math theorem proving, medical diagnosis, stock market/weather forecasting) Machine learning • Improving performance from experience (e.g., rule induction & adaptive modification) Planning • Planning a complex sequence of actions (e.g., playing chess) Natural language processing • Production and interpretation of spoken and written language

7. Pattern recognition • Knowledge representation • Reasoning & inference • Machine learning

8. Applications of AI Computer vision - IRIS (biometric identification device), detection of forgeries, chip inspection Expert systems • MYCIN (medical diagnosis), HYPO (legal reasoning), auto pilot, intelligent tutoring system Game playing • IBMS’ Deep Blue (search 2m positions per sec) Speech recognition - Dragon Naturally Speaking Robotics - robot moles in Mars exploration

9. Dartmouth Workshop (1956) - Summer workshop that officially launched the field known as ‘Artificial Intelligence’ (named by McCarthy) - Participants included: McCarthy (Stanford), Minsky (MIT), Shannon (Lucent), Newell (CMU), Simon (CMU) General Problem Solver (GPS) (Newell & Simon, 1960’s) - Landmark computer program that solves simple problems/puzzles (e.g., Tower of Hanoi) and even comes up with proofs for mathematical theorems - Based on a general problem solving strategy called the ‘mean-ends analysis’ (work backward from the goal to decide on what action(s) will help you achieve in which goals are decomposed into subgoals in a recursive fashion)

10. Weak AI vs Strong AI in the Study of Mind (Searl 1980) Weak AI: - “The principal value of the computer in the study of mind is that it gives us a very powerful tool.” Strong AI: - “An appropriately programmed computer literally has cognitive states and therefore explains how the human mind works.”

12. 2. Issues in AI Issue #1: What is a computation?

13. Turing Machines (Turing, 1937) - A Turing Machine, an idealized, mathematical abstraction of a digital computer, consists of (1) 1-dim tape of cells of unlimited length (written on each cell is a symbol from finite alphabet) (2) read/write head (3) control (action) table or program

14. Control program: - State of head: {S1, S2, S3} - Binary alphabet on tape: {0,1} - Movement of head: {Left, Right}

15. A Turing machine that computes: “2 x 4 = 8”

16. (Turing’s) Definition of computation - “A function is said to be computable if it can be implemented on a Turing Machine.” - Such functions are called Turing computable functions (e.g., f(x) = 0; natural log e; +/x; if-then) • Roughly speaking, a function or task is computable if its solution can be found in “finite” time (or polynomial time). • A problem in which the time required to solve grows exponentially as the problem size grows said to be uncomputable (i.e., unsolvable), thereby requiring “infinite” time to solve  NP-hard problem (e.g., Traveling Salesman Problem)

17. Traveling Salesman Problem 16-city problem A candidate solution

18. Universal (Turing) Machine - Turing also showed that it is possible to design a single Turing machine that can simulate any Turing machine. Such a machine is called a Universal Turing Machine Church-Turing Thesis: In essence, “A Universal Turing Machine can compute any non-NP-hard problem.” (e.g.) - Programmable computers (PC, MaC) von Neumann Machine - program – control/action table unit - CPU – read/write head unit - RAM - tape - DNA (biological computation device)

19. Issue #2: How do we know if a machine is intelligent or not?

20. Turing Test (Turing, 1951) - First attempt to define an operational definition of intelligence - Turing defined intelligent behavior as the ability to exhibit human-likeperformance, sufficient to fool an interrogator in an “imitation game”

21. Can the Turing test be a definition of intelligence?!% 1. A computer may pass the test but without ‘real’ understanding of the conversation that took place (e.g., Searl’s Chinese Room) 2. Many ‘real’ human beings might fail the test. 3. A computer often exhibits intelligence without being a conversational partner (e.g., autopilot)

22. Chinese Room (Searle, 1980) - Thought experiment developed as an attack on the Turing Test (againt Strong AI) - Showed that in theory, it is possible to create a system that exhibits intelligent output without understanding (i.e., in the absence of mind), thus passing the Turing test - Would it be practically possible to build such a system? Why or why not?