Introduction To Artificial Intelligence

1. Introduction To Artificial Intelligence John Woodward John.woodward@nottingham.edu.cn http://www.cs.nott.ac.uk/~jrw/

2. Physics and AI • In physics we have the foundations (Newton’s three laws) and some revolutions (quantum mechanics and Einstein's special and general theories of relativity). Over about 300 years. • Mathematics has an even longer history. • AI is relatively new (started around 1940’s) so we do not have any icons/heros….yet. • AI is relevant to any intellectual discipline. • Do we need a new physics?

3. Definition of Artificial Intelligence • In math – definitions are centrally important. • Can we define intelligence? (thought processes/reasoning). Emotional? Social? • Make a list of things computers cannot do. • Intelligence is knowing where to break rules 

4. Explicit vs. Implicit Programming • If we have a problem e.g. sorting a list of numbers, we can explicitly write an efficient algorithm to solve the problem (i.e. quick sort). Or finding max in an array • There are problems which take too long to solve so we must accept approximate methods.(e.g. travelling salesman problem). • There are problems we don’t even know how to solve e.g. speech recognition and vision. • In these cases we can write a program which writes a program or other AI methods.

5. What is intelligence? • What are the consequences of the actions in each of these pictures? • The second is even a common expression in English “to paint yourself into a corner”. • Early robots actually unplugged themselves, or got suck like this.

6. Turing Test Turing (1950) “Computing machinery and intelligence": Can machines think? Can machines behave intelligently? Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes Suggested major components of AI: knowledge, reasoning, language understanding, learning Problems: Turing test is not reproducible, constructive, or amenable to mathematical analysis

7. AI is not trying to copy humans • “artificial flight” was successful because the Wright brothers stopped mimicking birds. • We don’t want to copy pigeons. • Where else is the idea of a “gliding wing” and a propeller used in nature?

8. Example of Artificial Flight • First flight was hot air balloon - seen in nature? • Flying squirrel glide. • Sycamore seeds do use the idea of propeller. • Flagella in bacteria.

9. Cognitive Science How can we approach how humans think. 1. introspection (catch our own thoughts e.g. remembering someone's face, do we think in “words” – the rotation test) 2. psychological experiments (experiment on peoples behavior). What people say they do, and what they do are two different things e.g. recognizing caricatures. 3. brain imaging. Scans of brain show which parts use more oxygen.

10. Laws of Thought “Socrates is a man; all men are mortal; therefore Socrates is mortal.” LOGIC In 1965 computer programs existed that could in principle solve any solvable problem described in logical notation (however if no solution exists, the program would not terminate). How to we formally state real-world problems. Some problems take too long to solve exactly.

11. Foundations of AI • Philosophy • Mathematics • Economics • Neuroscience • Psychology • Control Theory • Linguistics

12. Philosophy • How can formal rules be used to draw valid conclusions? • How can the mind arise from physical matter. • What is knowledge, how does it originate, and lead to action • Consciousness and freewill (todo)

13. Mathematics • Logic. What are the formal rules? Gödel's incompleteness theorem. • Computation: What can and cannot be computed? Church’s thesis and halting problem, NP completeness. • Probability: How do we reason with uncertain information?

14. Economics • How do we make decisions to maximize payoff (utility, money, happiness). • How do we do this when others cooperate or do not cooperate (criminals). • What about if the reward is not immediate, but maybe delayed far into the future. • Decision theory/game theory/operations research.

15. Neuroscience • How does the brain process information? • What can brain damaged patients tell us about the working of the human brain (see books by Oliver Sacks)? • Lesions on rats brains and mazes. • How to neurons give rise to consciousness?

16. Cognitive Psychology • How to humans act and think? • 3 steps of a knowledge-based agent 1. stimulus is translated into an internal representation. 2. The representation is manipulated by cognitive processes. 3. The internal representations are converted into an action. Think about a game of chess. AGENT ENVIROMENT

17. Control Theory • How can object operate autonomously? • For example A water clock regulates is own flow rate Steam engine governor A guided missile, or a space probe, or a robot that can operate independently of a human controller.

18. Linguistics • How do language and thought relate? • How can a child understand sentence he or she had never hear before? Dog understands "SIT”. • Language is central to humans (which are by far the most intelligent species). No other animal has a full language like humans. • Understanding language require syntax (grammar) but also context • (e.g. you are pulling my leg – translate). • Do we think using words (e.g. English).

19. History of AI McCulloch and Pitts (1943) on/off perceptron. Hebb (1949) Hebbian learning rule. Turing (1950) “Computing Machinery and Intelligence” Newell and Simon (1976) physical symbol system hypothesis Samuel (1952) checkers player; the program leaned to play better than its creator

20. Perceptron 1 • A set of inputs are presented. • The inputs represent a problem. • A node sums up the weighted inputs and calculated an output. • A action is performed according to the value on the output (e.g. a robot controller <-1 turn left, >1 turn right, else move straight). • The Hebb rule tells us how to learn the weights

21. Perceptron 2 • Convergence theorem (1962) says that the learning algorithm can adjust its connection weights of a perceptron to match any input, provided such a match exists. • Minsky and Papert (1969) a two input perceptron cannot be trained to recognize when its two inputs are different (linearly separable or the XOR problem)

22. Computing Machinery and Intelligence • Alan Turing proposed • Machine learning • Genetic algorithms • Reinforcement learning • He proposed CHILD PROGRAM- instead of producing a program with adult abilities, produce a program with ability to learn like a child. Goal = -100 points Save = +10 points

23. Physical Symbol System Hypothesis • “A physical symbol system has the necessary and sufficient means for a general intelligent action”. • Symbols represent objects in the real world (e.g. chess pieces). • We have “data” which is manipulated (chess rules).

24. Samuel (1952) checkers player • Computer “do what they are told”. • I can play a game (e.g. OXO) • However I make mistakes • I can write a program which avoids this mistakes. • If I add learning – it can play better than me! • What were the inputs/outputs – how can we use a perceptron to learn OXO?

25. Machine Translation 1 • During the cold war, America used machines to translate Russian scientific text. • “the spirit is willing but the flesh is weak” • Was translate as • “the vodka is good but the meat is rotten” • A similar example; how do you pronounce ghoti • GOOGLE TRANSLATE…

26. Machine Translation 2 • Ghoti is a constructed word used to illustrate irregularities in English spelling. It is a respelling of the word fish, i.e., it is supposed to be pronounced /fɪʃ/. Its components include: • gh, pronounced /f/ as in tough /tʌf/; • o, pronounced /ɪ/ as in women /ˈwɪmɪn/; and • ti, pronounced /ʃ/ as in nation /ˈne͡ɪʃən/.

27. Expert Systems 1 • MYCIN is a medical expert system. • Rules were obtained by interviewing experts. • With about 450 rules, it could perform as well as some experts and considerable better than junior doctors. • Rules also incorporated “uncertainly” reflecting the confidence in the diagnosis (like a real doctor).

28. Expert Systems 2 • You are an expert in the following – but you try explaining to someone how you do it • Riding a bike • Walking • Driving a car • Touch typing • Recognizing handwriting.

29. AI and Industry • Digital Equipment Corporation 1986 • Expert system • Saved estimated $40 million US$ • Japan started 5th generation project. • Many projects never met their goals  • But many companies are using these techniques today – probably most obvious is the computer game industry (worth more than the movie industry)

30. State of the Art • Robotic Vehicles • Speech recognition • Game playing • Spam filtering • Robotics • Machine Translation

31. Robotic Vehicles • A driverless robotic volkeswagen car • Fitted with cameras, radar, laser range finders and onboard software. • Control commands for steering, breaking and acceleration. • 22mph, 132 mile course. DARPA Grand Challenge. • Some of the early cars drove straight into trees – why. • Why was it held in the desert?

32. Speech recognition • In use in your mobile phone (voice dialing) • When you call the train company in the UK – you have a simple conversation • Where to? (Nottingham) • Where from? (London Heathrow) • When would you like to travel? (4:40pm) • What is your credit card number. But how many different ways can you say “Hello” – the tone and intonation of your voice carry a lot of information.

33. Game playing • IBM’s Deep Blue defeated the world champion Garry Kasparov. • “a new kind of intelligence” • IBM’s stock increased by $18 billion USD. • By studying this, chess players could draw!!! • Recently the computer is much better. • But what about “GO”, or other games?

34. Spam filtering • Many emails are spam (credit card, sexy girls waiting to meet you….) • We can scan for keywords e.g. viagra, but spammers are clever and slightly misspell the word viiagra. • Just looking at keywords is not enough (you might ask IT services to reset your password). • Why is spam called spam?

35. Logistic Planning • During the 1991 Persian Gulf crisis, US armed forces moved 50,000 people, needing origins, routes and destinations. • AI planning techniques generated a plan in hours, which would normally take weeks. • Timetable scheduling at UNNC. • Nurse Roistering at National Heath Service in UK.

36. Robotics • The iRobot Corporation sold 2 million Roomba robotic vacuum cleaners for home use. • The can navigate in an intelligent way.

37. Machine Translation • Arabic to English • The program builds a statistical model from two trillion examples. • None of the programmers speak Arabic, but do understand statistics and machine learning.

38. Are computers = electric brains? • The Chinese call a computer • 电脑 “electric brain” • Maybe a better translation • 计算机 “Meter Operators Machine ” • Is there and algorithm which is functionally equivalent to the human brain?

39. Testimony • Some of what we learn is not through experience, but through what people tell us. • “the great wall of china can be seen from outer space”. • If you thought the capital of Canada was Toronto, but I told you it was Ottawa, you might believe me. • What if I told you the capital was Paris? • You are learning by testimony now.