Artificial Intelligence

1. Artificial Intelligence

2. AI • You are a caveman (or woman) • I travel back in time and bring you a LapTop and show you some of the things it is capable of doing. • Question : Would you, as a caveman, consider the computer to be intelligent?

3. Intelligence • Are the things shown below, Intelligent?

4. Different mice might follow different paths based to their intelligence In other words: The problem can be solved in many ways Ability to solve problems demonstrates Intelligence Searching a path …

5. Big questions • Can machines think? • If so, how? • If not, why not? • What does this say about humans? • What does this say about the mind?

6. Key: Adding the next EVEN number … 1+2 = 3; 3+4 = 7; 7+6 = 13; 13+8 =21; 21+10 = 31 1,3,7,13,21,31 Ability to solve problems demonstrates Intelligence Next number in the sequence … • Consider the following sequence … 1,3,7,13,21,__ • What is the next number ?

7. AI Long Term Goals Produce intelligent behaviour in machines • Why use computers at all? • They can do things better than us • Big calculations quickly and reliably • We do intelligent things • So get computers to do intelligent things

8. So, Let’s Summarize… • Ability to solve problems • Ability to plan and schedule • Ability to memorize and process information • Ability to answer fuzzy questions • Ability to learn • Ability to recognize • Ability to understand • Ability to perceive • And many more … Food for thought: Can only humans beings and animals possess these qualities?

9. What if? • A machine searches through a mesh and finds a path? • A machine solves problems like the next number in the sequence? • A machine develops plans? • A machine diagnoses and prescribes? • A machine answers ambiguous questions? • A machine recognizes fingerprints? • A machine understands? • A machine perceives? • A machine does MANY MORE SUCH THINGS … • A machine behaves as HUMANS do? HUMANOID!!!

10. Some Advantages of Artificial Intelligence • more powerful and more useful computers • new and improved interfaces • solving new problems • better handling of information • relieves information overload • conversion of information into knowledge

11. The Disadvantages • increased costs • difficulty with software development - slow and expensive • few experienced programmers • few practical products have reached the market as yet.

12. Some AI Systems that are Better Than Humans • Backgammon • TD gammon was the first program to beat the worlds best players (Gerald Tesauro) • http://researchweb.watson.ibm.com/massive/tdl.html

13. Why AI? • Engineering: To get machines to do a wider variety of useful things • e.g., understand spoken natural language, recognize individual people in visual scenes, find the best travel plan for your vacation, etc. • CognitiveScience: As a way to understand how natural minds and mental phenomena work • e.g., visual perception, memory, learning, language, etc. • Philosophy: As a way to explore some basic and interesting (and important) philosophical questions • e.g., the mind body problem, what is consciousness, etc.

14. What is Artificial Intelligence ? • making computers that think? • the automation of activities we associate with human thinking, like decision making, learning ... ? • the art of creating machines that perform functions that require intelligence when performed by people ?

15. What’s easy and what’s hard for AI? • It’s been easier to mechanize many of the high-level tasks we usually associate with “intelligence” in people • e.g., symbolic integration, proving theorems, playing chess, medical diagnosis • It’s been very hard to mechanize tasks that lots of animals can do • walking around without running into things • catching prey and avoiding predators • interpreting complex sensory information (e.g., visual, aural, …) • modeling the internal states of other animals from their behavior • working as a team (e.g., with pack animals) • Is there a fundamental difference between the two categories?

16. What can AI systems do? Here are some example applications • Computer vision: face recognition from a large set • Robotics: autonomous (mostly) automobile • Natural language processing: simple machine translation • Expert systems: medical diagnosis in a narrow domain • Spoken language systems: ~1000 word continuous speech • Planning and scheduling: Hubble Telescope experiments • Learning: text categorization into ~1000 topics • User modeling: Bayesian reasoning in Windows help (the infamous paper clip…) • Games: Grand Master level in chess (world champion), checkers, etc.

17. IBM’s Deep Blue versus Kasparov • On May 11, 1997, Deep Blue was the first computer program to beat reigning chess champion Kasparov in a 6 game match (2 : 1 wins, with 3 draws) • Massively parallel computation (259th most powerful supercomputer in 1997) • Evaluation function criteria learned by analyzing thousands of master games • Searched the game tree from 6-12 ply usually, up to 40 ply in some situations. • One ply corresponds to one turn of play.

18. Robotics Shakey (1966-1972) Cog (90s) Robocup Soccer (2000s) Kismet (late 90s, 2000s) Boss (2007)

19. Robotics • Mars rovers • Autonomous vehicles • DARPA Grand Challenge • Google self-driving cars • Autonomous helicopters • Robot soccer • RoboCup • Personal robotics • Humanoid robots • Robotic pets • Personal assistants?

20. How is it Currently Done? Crusher and, more recently, PerceptTOR

21. Vision • OCR, handwriting recognition • Face detection/recognition: many consumer cameras, Apple iPhoto • Visual search: Google Goggles • Vehicle safety systems: Mobileye

22. DARPA grand challenge

23. Stanley Robot Stanford Racing Team www.stanfordracing.org Next few slides courtesy of Prof. Sebastian Thrun, Stanford University

24. What About the DARPA Grand Challenge? • Autonomous Navigation in the Desert over a 132 mile course. • 5 Teams succeeded! • http://www.darpa.mil/grandchallenge05/gcorg/index.html • This was a monumental achievement in autonomous robotics • HOWEVER: This was not an unstructured environment! • GPS waypoints were carefully chosen, sometimes less than a meter apart.

25. Laser Terrain Mapping Sebastian Stanley Stanley’s Technology Path Planning Learning from Human Drivers Adaptive Vision Images and movies taken from Sebastian Thrun’s multimedia website.

26. SENSOR INTERFACE PERCEPTION PLANNING&CONTROL USER INTERFACE RDDF database Top level control Touch screen UI corridor pause/disable command Wireless E-Stop Laser 1 interface RDDF corridor (smoothed and original) driving mode Laser 2 interface Laser 3 interface road center Road finder Path planner Laser 4 interface laser map trajectory map VEHICLE INTERFACE Laser 5 interface Laser mapper vision map Camera interface Vision mapper Steering control obstacle list Radar interface Radar mapper Touareg interface vehicle state (pose, velocity) vehicle state Throttle/brake control GPS position UKF Pose estimation Power server interface vehicle state (pose, velocity) GPS compass IMU interface velocity limit Surface assessment Wheel velocity Brake/steering emergency stop heart beats Linux processes start/stop health status Process controller Health monitor power on/off data GLOBAL SERVICES Data logger File system Communication requests Communication channels clocks Inter-process communication (IPC) server Time server

28. Google self-driving cars

29. Europa Hydrobot • http://www.resa.net/nasa/images/gem/HYDROBOT.JPG

30. AI Applications Games:

31. AI Applications • Games:

32. AI Applications • Robotic toys:

33. AI Applications • Transportation: • Pedestrian detection:

34. AI Applications • Medicine: • Image guided surgery

35. AI Applications • Autonomous Planning & Scheduling: • Telescope scheduling

36. Natural Language • Speech technologies • Automatic speech recognition • Google voice search • Text-to-speech synthesis • Dialog systems • Machine translation

37. Why is AI hard? Two usual ingredients (for standard AI) • Representation • need to represent our knowledge in computer readable form • Reasoning • need to be able to manipulate knowledge and derive new knowledge • many possible ways to do this, but most give rubbish • finding the successful way usually involves search Both of these are hard.

38. A C D B E The Travelling Salesman Problem (TSP) • A salesperson has to visit a number of cities • (S)He can start at any city and must finish at that same city • The salesperson must visit each city only once • For example, with 5 cities a possible tour is:

39. Combinatorial Explosion A 50 City TSP has 1.52 * 1064 possible solutions Age of the universe is 15 billion (1.5 * 1010) years There are 30 million seconds in a year Age of universe is about 45 * 1016 seconds A 10GHz computer might do 109 tours per second Running since start of universe, it would still only have done 1026 tours Not even close to evaluating all tours! Need to be clever about how to solve such search problems!

40. AI Connections • Philosophy logic, methods of reasoning, mind vs. matter, foundations of learning and knowledge • Mathematics logic, probability, optimization • Economics utility, decision theory • Neuroscience biological basis of intelligence • Cognitive science computational models of human intelligence • Linguistics rules of language, language acquisition • Machine learning design of systems that use experience to improve performance • Control theory design of dynamical systems that use a controller to achieve desired behavior • Computer engineering, mechanical engineering, robotics, …

41. AI Generic Techniques • Automated Reasoning • Resolution, proof planning, Davis-Putnam, CSPs • Machine Learning • Neural nets, ILP, decision tree learning • Natural language processing • N-grams, parsing, grammar learning • Robotics • Planning, edge detection, cell decomposition • Evolutionary approaches • Crossover, mutation, selection

42. History of AI

43. Harder than originally thought • 1966: Weizenbaum’sEliza • “ … mother …” → “Tell me more about your family” • “I wanted to adopt a puppy, but it’s too young to be separated from its mother.” • 1950s: during the Cold War, automatic Russian-English translation attempted • 1954: Georgetown-IBM experiment • Completely automatic translation of more than sixty Russian sentences into English • Only six grammar rules, 250 vocabulary words, restricted to organic chemistry • 1966: ALPAC (Automatic Language Processing Advisory Committee) report: machine translation has failed to live up to its promise • “The spirit is willing but the flesh is weak.” →“The vodka is strong but the meat is rotten.”

44. Blocks world (1960s – 1970s) ??? Roberts, 1963

45. A dose of reality • 1940s McCulloch & Pitts neurons; Hebb’s learning rule • Turing’s “Computing Machinery and Intelligence” • Shannon’s computer chess • 1954 Georgetown-IBM machine translation experiment • 1956 Dartmouth meeting: “Artificial Intelligence” adopted • 1957 Rosenblatt’s perceptrons • 1950s-1960s “Look, Ma, no hands!” period: • Samuel’s checkers program, Newell & Simon’s • Logic Theorist, Gelernter’s Geometry Engine • 1966-73 Setbacks in machine translation • Neural network research almost disappears • Intractability hits home

46. The rest of the story • 1974-1980 The first “AI winter” • 1970s Knowledge-based approaches • 1980-88 Expert systems boom • 1988-93 Expert system bust; the second “AI winter” • 1986 Neural networks return to popularity • 1988 Pearl’s Probabilistic Reasoning in Intelligent Systems • 1990 Backlash against symbolic systems; Brooks’ “nouvelle AI” • 1995-present Increasing specialization of the field • Agent-based systems • Machine learning everywhere • Tackling general intelligence again?

47. Course Overview • AI fundamentals • Terminology • Methodologies • Logic Representation • Search • Game playing • Decision-making under uncertainty • Machine learning

48. Some Famous Imitation Games • 1960s ELIZA • Rogerian psychotherapist • 1970s SHRDLU • Blocks world reasoner • 1980s NICOLAI • unrestricted discourse • 1990s Loebner prize • win $100,000 if you pass the test

49. The problem with ELIZA • Eliza used simple pattern matching • “Well, my friend made me come here” • “Your friend made you come here?” • Eliza written by Joseph Weizenbaum

50. Who does AI? • Academic researchers (perhaps the most Ph.D.-generating area of computer science in recent years) • Some of the top AI schools: CMU, Stanford, Berkeley, MIT, UIUC, UMd, U Alberta, UT Austin, ... (and, of course, Swarthmore!) • Government and private research labs • NASA, NRL, NIST, IBM, AT&T, SRI, ISI, MERL, ... • Lots of companies! • Google, Microsoft, Honeywell, Teknowledge, SAIC, MITRE, Fujitsu, Global InfoTek, BodyMedia, ...