1. 1 Artificial Intelligence Past, Present, and Future Olac Fuentes Computer Science Department UTEP

2. Artificial Intelligence A definition: AI is the science and engineering of making intelligent machines 2

3. Artificial Intelligence A definition: AI is the science and engineering of making intelligent machines But, what is intelligence? A very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience. 3

4. Artificial Intelligence Another definition: AI is the science and engineering of making machines that are capable of: –Reasoning –Representing knowledge –Planning –Learning –Understanding (human) languages –Understanding their environment 4

5. Old Times The pursuit of “General AI” Objective: Build a machine that exhibits ALL of the AI features 5

6. Old Times – The Turing Test How do we know when AI research has succeed? When a program that can consistently pass the Turing test is written. 6

7. Old Times – The Turing Test A human judge engages in a natural language conversation with one human and one machine, each of which try to appear human; if the judge cannot reliably tell which is which, then the machine is said to pass the test. 7

8. Old Times – The Turing Test Problems with the Turing test: Human intelligence vs. general intelligence –Computer is expected to exhibit undesirable human behaviors –Computer may fail for being too smart Real intelligence vs. simulated intelligence Do we really need a machine that passes it? Too hard! – Very useful applications can be built that don’t pass the Turing test 8

9. More Recent Research Goal: Build “intelligent” programs that are useful for a particular task Normally restricted to one target intelligent behavior. Thus AI has been broken into several sub-areas: –Machine learning –Computer vision –Natural language processing –Robotics –Knowledge representation and reasoning 9

10. What has AI done for us? State of the Art It has provided computers that are able to: Learn (some simple concepts and tasks) Understand images (of restricted predefined types) Understand human languages (some of them, mostly written, with limited vocabularies) Allow robots to navigate autonomously (in simplified environments) Reason (using brute force, in very restricted domains) 10

11. Machine Learning The key enabling technology of AI Problem Solving in Computer Science 11

12. Machine Learning The key enabling technology of AI Problem Solving in Computer Science Traditional Approach –Write a detailed sequence of instructions (a program) that tells the computer how to solve the problem. 12

13. Machine Learning The key enabling technology of AI Problem Solving in Computer Science Traditional Approach –Write a detailed sequence of instructions (a program) that tells the computer how to solve the problem. Machine Learning Approach –Give the computer examples of desired results and let it learn how to solve the problem. 13

14. Machine Learning The key enabling technology of AI Problem Solving in Computer Science Traditional Approach –Write a detailed sequence of instructions (a program) that tells the computer how to solve the problem. Machine Learning Approach –Give the computer examples of desired results and let it learn how to solve the problem. –Advantage: It allows to solve problems that we can’t solve with the traditional approach 14

15. Machine Learning The key enabling technology of AI Problem Solving in Computer Science Traditional Approach –Write a detailed sequence of instructions (a program) that tells the computer how to solve the problem. Machine Learning Approach –Give the computer examples of desired results and let it learn how to solve the problem. –Advantage: It allows to solve problems that we can’t solve with the traditional approach –Most applications in other AI areas are based on machine learning 15

16. Machine Learning The key enabling technology of AI Problem Solving in Computer Science Traditional Approach –Write a detailed sequence of instructions (a program) that tells the computer how to solve the problem. Machine Learning Approach –Give the computer examples of desired results and let it learn how to solve the problem. –Advantage: It allows to solve problems that we can’t solve with the traditional approach –Most applications in other AI areas are based on machine learning 16

17. Computers that learn How? Very active research area 17

18. Computers that learn How? Very active research area –Extract statistical regularities from data 18

19. Computers that learn How? Very active research area –Extract statistical regularities from data –Find decision boundaries 19

20. Computers that learn How? Very active research area –Extract statistical regularities from data –Find decision boundaries –Find decision rules 20

21. Computers that learn How? Very active research area –Extract statistical regularities from data –Find decision boundaries –Find decision rules –Imitate human brain 21

22. Computers that learn How? Very active research area –Extract statistical regularities from data –Find decision boundaries –Find decision rules –Imitate human brain –Imitate biological evolution 22

23. Computers that learn How? Very active research area –Extract statistical regularities from data –Find decision boundaries –Find decision rules –Imitate human brain –Imitate biological evolution –Combine several approaches 23

24. What has AI done for us? It has provided computers that are able to: Learn (some simple concepts and tasks) Understand images (of restricted predefined types) Understand human languages (some of them, mostly written, with limited vocabularies) Allow robots to navigate autonomously (in simplified environments) Reason (using brute force, in very restricted domains) 24

25. What has AI done for us? Machine Learning – Netflix movie recommender system Very active research area –Extract statistical regularities from data –Find decision boundaries –Find decision rules –Imitate human brain –Imitate biological evolution –Combine several approaches 25

26. What has AI done for us? Machine Learning – Netflix movie recommender system Idea: After returning a movie, user assigns a grade to it (from 1 to 5) Given (millions) of records of users, movies and grades, and the pattern of grades assigned by the user, the system presents a list of movies the user is likely to grade highly

27. 27 What has AI done for us? Robotics - Stanley, a self-driving car

28. 28 What has AI done for us? Robotics - Stanley, a self-driving car What does Stanley learn? A mapping from sensory inputs to driving commands

29. 29 What has AI done for us? Robotics - Lexus self-parking system

30. 30 What has AI done for us? Computer Vision - Face Detecting Cameras

31. 31 What has AI done for us? Computer Vision - Face Detecting Cameras

32. What has AI done for us? Reasoning Successful applications: Commercial planning systems Chess playing programs Checkers playing programs Optimal solution to Rubik’s cube

33. What has AI done for us? Reasoning The Zohirushi Neuro Fuzzy® Rice Cooker & Warmer features advanced Neuro Fuzzy® logic technology, which allows the rice cooker to 'think' for itself and make fine adjustments to temperature and heating time to cook perfect rice every time.

34. What has AI done for us? Natural language processing Successful applications: Dictation systems Text-to-speech systems Text classification Automated summarization Automated translation

35. What has AI done for us? Natural language processing Automated Translation Original English Text: The Dodgers became the fifth team in modern major league history to win a game in which they didn't get a hit, defeating the Angels 1-0. Weaver's error on a slow roller led to an unearned run by the Dodgers in the fifth.

36. What has AI done for us? Natural language processing Automated Translation Original English Text: The Dodgers became the fifth team in modern major league history to win a game in which they didn't get a hit, defeating the Angels 1-0. Weaver's error on a slow roller led to an unearned run by the Dodgers in the fifth. Translation to Spanish (by Google - 2008) Los Dodgers se convirtió en el quinto equipo en la moderna historia de las ligas mayores para ganar un juego en el que no obtener una respuesta positiva, derrotando a los Ángeles 1-0. Weaver's error en un lento rodillo dado lugar a un descontados no correr por la Dodgers en el quinto.

37. What has AI done for us? Natural language processing Automated Translation Original English Text: The Dodgers became the fifth team in modern major league history to win a game in which they didn't get a hit, defeating the Angels 1-0. Weaver's error on a slow roller led to an unearned run by the Dodgers in the fifth. Translation to Spanish (by Google - 2010) Los Dodgers se convirtió en el quinto equipo en la historia moderna de las Grandes Ligas en ganar un partido en el que no obtuvo una respuesta positiva, derrotando a los Angelinos 1-0. De error de Weaver en un rodillo lento condujo a una carrera sucia por los Dodgers en el quinto.

38. What has AI done for us? Natural language processing Automated Translation Translation to Spanish (by Google) Los Dodgers se convirtió en el quinto equipo en la moderna historia de las ligas mayores para ganar un juego en el que no obtener una respuesta positiva, derrotando a los Ángeles 1-0. Weaver's error en un lento rodillo dado lugar a un descontados no correr por la Dodgers en el quinto.

39. What has AI done for us? Natural language processing Automated Translation Translation to Spanish (by Google - 2008) Los Dodgers se convirtió en el quinto equipo en la moderna historia de las ligas mayores para ganar un juego en el que no obtener una respuesta positiva, derrotando a los Ángeles 1-0. Weaver's error en un lento rodillo dado lugar a un descontados no correr por la Dodgers en el quinto. Translation back to English (by Yahoo) The Dodgers became the fifth equipment in the modern history of the leagues majors to gain a game in which not to obtain a positive answer, defeating to Los Angeles 1-0. Weaver' s error in a slow given rise roller to discounting not to run by the Dodgers in fifth.

40. What has AI done for us? Natural language processing Automated Translation Translation to Spanish (by Google - 2010) Los Dodgers se convirtió en el quinto equipo en la historia moderna de las Grandes Ligas en ganar un partido en el que no obtuvo una respuesta positiva, derrotando a los Angelinos 1-0. De error de Weaver en un rodillo lento condujo a una carrera sucia por los Dodgers en el quinto. Translation back to English (by Yahoo) The Dodgers became the fifth equipment in the modern history of the Great Leagues in gaining a party in which it did not obtain a positive answer, defeating to the Angelinos 1-0. Of error of Weaver in a slow roller it lead to a dirty race by the Dodgers in fifth.

41. The Future of AI

42. Making predictions is hard, especially about the future - Yogi Berra

43. The Future of AI Making predictions is hard, especially about the future - Yogi Berra But… Continued progress expected Greater complexity and autonomy New enabling technology - Metalearning Once human-level intelligence is attained, it will be quickly surpassed

44. Conclusions

45. Artificial Intelligence has made a great deal of progress since its inception in the 1950s

46. Conclusions Artificial Intelligence has made a great deal of progress since its inception in the 1950s The goal of general AI has been abandoned (at least temporarily)

47. Conclusions Artificial Intelligence has made a great deal of progress since its inception in the 1950s The goal of general AI has been abandoned (at least temporarily) Useful applications have appeared in all subfields of AI, including: Machine learning, computer vision, robotics, natural language processing and knowledge representation

48. Conclusions Artificial Intelligence has made a great deal of progress since its inception in the 1950s The goal of general AI has been abandoned (at least temporarily) Useful applications have appeared in all subfields of AI, including: Machine learning, computer vision, robotics, natural language processing and knowledge representation The field continues to evolve rapidly

49. Conclusions Artificial Intelligence has made a great deal of progress since its inception in the 1950s The goal of general AI has been abandoned (at least temporarily) Useful applications have appeared in all subfields of AI, including: Machine learning, computer vision, robotics, natural language processing and knowledge representation The field continues to evolve rapidly Increased complexity and unpredictability of AI programs will raise important ethics issues and concerns