1<br />Artificial IntelligencePast, Present, and Future Olac FuentesComputer Science DepartmentUTEP<br />
Artificial Intelligence<br />A definition:<br />AI is the science and engineering of making intelligent machines<br />2<br />
Artificial Intelligence<br />A definition:<br />AI is the science and engineering of making intelligent machines<br />But,...
Artificial Intelligence<br />Another definition:<br />AI is the science and engineering of making machines that are capabl...
Old Times<br />The pursuit of “General AI”<br />Objective: Build a machine that exhibits ALL of the AI features<br />5<br />
Old Times – The Turing Test<br />How do we know when AI research has succeed?<br />When a program that can consistently pa...
Old Times – The Turing Test<br />A human judge engages in a natural language conversation with one human and one machine, ...
Old Times – The Turing Test<br />Problems with the Turing test:<br />Human intelligence vs. general intelligence<br />Comp...
More Recent Research<br />Goal: Build “intelligent” programs that are useful for a particular task<br />Normally restricte...
What has AI done for us? State of the Art<br />It has provided computers that are able to:<br />Learn (some simple concept...
Machine Learning The key enabling technology of AI<br />Problem Solving in Computer Science<br />11<br />
Machine Learning The key enabling technology of AI<br />Problem Solving in Computer Science<br />Traditional Approach <br ...
Machine Learning The key enabling technology of AI<br />Problem Solving in Computer Science<br />Traditional Approach <br ...
Machine Learning The key enabling technology of AI<br />Problem Solving in Computer Science<br />Traditional Approach <br ...
Machine Learning The key enabling technology of AI<br />Problem Solving in Computer Science<br />Traditional Approach <br ...
Machine Learning The key enabling technology of AI<br />Problem Solving in Computer Science<br />Traditional Approach <br ...
Computers that learn How?<br />Very active research area<br />17<br />
Computers that learn How?<br />Very active research area<br />	Extract statistical regularities from data<br />18<br />
Computers that learn How?<br />Very active research area<br />	Extract statistical regularities from data<br />	Find decis...
Computers that learn How?<br />Very active research area<br />	Extract statistical regularities from data<br />	Find decis...
Computers that learn How?<br />Very active research area<br />	Extract statistical regularities from data<br />	Find decis...
Computers that learn How?<br />Very active research area<br />	Extract statistical regularities from data<br />	Find decis...
Computers that learn How?<br />Very active research area<br />	Extract statistical regularities from data<br />	Find decis...
What has AI done for us? <br />It has provided computers that are able to:<br />Learn (some simple concepts and tasks)<br ...
What has AI done for us? Machine Learning – Netflix movie recommender system<br />Very active research area<br />	Extract ...
What has AI done for us? Machine Learning – Netflix movie recommender system<br />Idea:<br />After returning a movie, user...
27<br />What has AI done for us? Robotics - Stanley, a self-driving car<br />
28<br />What has AI done for us?  Robotics - Stanley, a self-driving car<br />What does Stanley learn?<br />A mapping from...
29<br />What has AI done for us?  Robotics - Lexus self-parking system<br />
30<br />What has AI done for us? Computer Vision - Face Detecting Cameras<br />
31<br />What has AI done for us? Computer Vision - Face Detecting Cameras<br />
What has AI done for us? Reasoning<br />Successful applications:<br />Commercial planning systems<br />Chess playing progr...
What has AI done for us?  Reasoning<br />The ZohirushiNeuro Fuzzy® Rice Cooker & Warmer features advanced Neuro Fuzzy® log...
What has AI done for us? Natural language processing<br />Successful applications:<br />Dictation systems<br />Text-to-spe...
What has AI done for us? Natural language processingAutomated Translation<br />Original English Text:<br />The Dodgers bec...
What has AI done for us? Natural language processingAutomated Translation<br />Original English Text:<br />The Dodgers bec...
What has AI done for us? Natural language processingAutomated Translation<br />Original English Text:<br />The Dodgers bec...
What has AI done for us? Natural language processingAutomated Translation<br />Translation to Spanish (by Google)<br />Los...
What has AI done for us? Natural language processingAutomated Translation<br />Translation to Spanish (by Google - 2008)<b...
What has AI done for us? Natural language processingAutomated Translation<br />Translation to Spanish (by Google - 2010)<b...
The Future of AI<br />
The Future of AI<br />Making predictions is hard, especially about the future - Yogi Berra<br />
The Future of AI<br />Making predictions is hard, especially about the future - Yogi Berra<br />But…<br />Continued progre...
Conclusions<br />
Conclusions<br />Artificial Intelligence has made a great deal of progress since its inception in the 1950s<br />
Conclusions<br />Artificial Intelligence has made a great deal of progress since its inception in the 1950s<br />The goal ...
Conclusions<br />Artificial Intelligence has made a great deal of progress since its inception in the 1950s<br />The goal ...
Conclusions<br />Artificial Intelligence has made a great deal of progress since its inception in the 1950s<br />The goal ...
Conclusions<br />Artificial Intelligence has made a great deal of progress since its inception in the 1950s<br />The goal ...
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Aprendizaje Automático en Astrofísica, Óptica y Otras Áreas Olac ...

  1. 1. 1<br />Artificial IntelligencePast, Present, and Future Olac FuentesComputer Science DepartmentUTEP<br />
  2. 2. Artificial Intelligence<br />A definition:<br />AI is the science and engineering of making intelligent machines<br />2<br />
  3. 3. Artificial Intelligence<br />A definition:<br />AI is the science and engineering of making intelligent machines<br />But, what is intelligence?<br />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.<br />3<br />
  4. 4. Artificial Intelligence<br />Another definition:<br />AI is the science and engineering of making machines that are capable of:<br />Reasoning<br />Representing knowledge<br />Planning<br />Learning<br />Understanding (human) languages<br />Understanding their environment<br />4<br />
  5. 5. Old Times<br />The pursuit of “General AI”<br />Objective: Build a machine that exhibits ALL of the AI features<br />5<br />
  6. 6. Old Times – The Turing Test<br />How do we know when AI research has succeed?<br />When a program that can consistently pass the Turing test is written.<br />6<br />
  7. 7. Old Times – The Turing Test<br />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.<br />7<br />
  8. 8. Old Times – The Turing Test<br />Problems with the Turing test:<br />Human intelligence vs. general intelligence<br />Computer is expected to exhibit undesirable human behaviors<br />Computer may fail for being too smart<br />Real intelligence vs. simulated intelligence<br />Do we really need a machine that passes it?<br />Too hard! – Very useful applications can be built that don’t pass the Turing test<br />8<br />
  9. 9. More Recent Research<br />Goal: Build “intelligent” programs that are useful for a particular task<br />Normally restricted to one target intelligent behavior. <br />Thus AI has been broken into several sub-areas:<br />Machine learning <br />Computer vision<br />Natural language processing<br />Robotics<br />Knowledge representation and reasoning<br />9<br />
  10. 10. What has AI done for us? State of the Art<br />It has provided computers that are able to:<br />Learn (some simple concepts and tasks)<br />Understand images (of restricted predefined types)<br />Understand human languages (some of them, mostly written, with limited vocabularies)<br />Allow robots to navigate autonomously (in simplified environments)<br />Reason (using brute force, in very restricted domains)<br />10<br />
  11. 11. Machine Learning The key enabling technology of AI<br />Problem Solving in Computer Science<br />11<br />
  12. 12. Machine Learning The key enabling technology of AI<br />Problem Solving in Computer Science<br />Traditional Approach <br />Write a detailed sequence of instructions (a program) that tells the computer how to solve the problem.<br />12<br />
  13. 13. Machine Learning The key enabling technology of AI<br />Problem Solving in Computer Science<br />Traditional Approach <br />Write a detailed sequence of instructions (a program) that tells the computer how to solve the problem.<br />Machine Learning Approach<br />Give the computer examples of desired results and let it learn how to solve the problem.<br />13<br />
  14. 14. Machine Learning The key enabling technology of AI<br />Problem Solving in Computer Science<br />Traditional Approach <br />Write a detailed sequence of instructions (a program) that tells the computer how to solve the problem.<br />Machine Learning Approach<br />Give the computer examples of desired results and let it learn how to solve the problem.<br />Advantage: It allows to solve problems that we can’t solve with the traditional approach<br />14<br />
  15. 15. Machine Learning The key enabling technology of AI<br />Problem Solving in Computer Science<br />Traditional Approach <br />Write a detailed sequence of instructions (a program) that tells the computer how to solve the problem.<br />Machine Learning Approach<br />Give the computer examples of desired results and let it learn how to solve the problem.<br />Advantage: It allows to solve problems that we can’t solve with the traditional approach<br />Most applications in other AI areas are based on machine learning<br />15<br />
  16. 16. Machine Learning The key enabling technology of AI<br />Problem Solving in Computer Science<br />Traditional Approach <br />Write a detailed sequence of instructions (a program) that tells the computer how to solve the problem.<br />Machine Learning Approach<br />Give the computer examples of desired results and let it learn how to solve the problem.<br />Advantage: It allows to solve problems that we can’t solve with the traditional approach<br />Most applications in other AI areas are based on machine learning<br />16<br />
  17. 17. Computers that learn How?<br />Very active research area<br />17<br />
  18. 18. Computers that learn How?<br />Very active research area<br /> Extract statistical regularities from data<br />18<br />
  19. 19. Computers that learn How?<br />Very active research area<br /> Extract statistical regularities from data<br /> Find decision boundaries<br />19<br />
  20. 20. Computers that learn How?<br />Very active research area<br /> Extract statistical regularities from data<br /> Find decision boundaries<br /> Find decision rules<br />20<br />
  21. 21. Computers that learn How?<br />Very active research area<br /> Extract statistical regularities from data<br /> Find decision boundaries<br /> Find decision rules<br /> Imitate human brain<br />21<br />
  22. 22. Computers that learn How?<br />Very active research area<br /> Extract statistical regularities from data<br /> Find decision boundaries<br /> Find decision rules<br /> Imitate human brain<br /> Imitate biological evolution<br />22<br />
  23. 23. Computers that learn How?<br />Very active research area<br /> Extract statistical regularities from data<br /> Find decision boundaries<br /> Find decision rules<br /> Imitate human brain<br /> Imitate biological evolution<br /> Combine several approaches<br />23<br />
  24. 24. What has AI done for us? <br />It has provided computers that are able to:<br />Learn (some simple concepts and tasks)<br />Understand images (of restricted predefined types)<br />Understand human languages (some of them, mostly written, with limited vocabularies)<br />Allow robots to navigate autonomously (in simplified environments)<br />Reason (using brute force, in very restricted domains)<br />24<br />
  25. 25. What has AI done for us? Machine Learning – Netflix movie recommender system<br />Very active research area<br /> Extract statistical regularities from data<br /> Find decision boundaries<br /> Find decision rules<br /> Imitate human brain<br /> Imitate biological evolution<br /> Combine several approaches<br />25<br />
  26. 26. What has AI done for us? Machine Learning – Netflix movie recommender system<br />Idea:<br />After returning a movie, user assigns a grade to it (from 1 to 5)<br />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<br />
  27. 27. 27<br />What has AI done for us? Robotics - Stanley, a self-driving car<br />
  28. 28. 28<br />What has AI done for us? Robotics - Stanley, a self-driving car<br />What does Stanley learn?<br />A mapping from sensory inputs to driving commands<br />
  29. 29. 29<br />What has AI done for us? Robotics - Lexus self-parking system<br />
  30. 30. 30<br />What has AI done for us? Computer Vision - Face Detecting Cameras<br />
  31. 31. 31<br />What has AI done for us? Computer Vision - Face Detecting Cameras<br />
  32. 32. What has AI done for us? Reasoning<br />Successful applications:<br />Commercial planning systems<br />Chess playing programs<br />Checkers playing programs<br />Optimal solution to Rubik’s cube<br />
  33. 33. What has AI done for us? Reasoning<br />The ZohirushiNeuro 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. <br />
  34. 34. What has AI done for us? Natural language processing<br />Successful applications:<br />Dictation systems<br />Text-to-speech systems<br />Text classification<br />Automated summarization<br />Automated translation<br />
  35. 35. What has AI done for us? Natural language processingAutomated Translation<br />Original English Text:<br />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.<br />
  36. 36. What has AI done for us? Natural language processingAutomated Translation<br />Original English Text:<br />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.<br />Translation to Spanish (by Google - 2008)<br />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.<br />
  37. 37. What has AI done for us? Natural language processingAutomated Translation<br />Original English Text:<br />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.<br />Translation to Spanish (by Google - 2010)<br />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.<br />
  38. 38. What has AI done for us? Natural language processingAutomated Translation<br />Translation to Spanish (by Google)<br />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.<br />
  39. 39. What has AI done for us? Natural language processingAutomated Translation<br />Translation to Spanish (by Google - 2008)<br />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.<br />Translation back to English (by Yahoo)<br />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.<br />
  40. 40. What has AI done for us? Natural language processingAutomated Translation<br />Translation to Spanish (by Google - 2010)<br />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.<br />Translation back to English (by Yahoo)<br />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.<br />
  41. 41. The Future of AI<br />
  42. 42. The Future of AI<br />Making predictions is hard, especially about the future - Yogi Berra<br />
  43. 43. The Future of AI<br />Making predictions is hard, especially about the future - Yogi Berra<br />But…<br />Continued progress expected<br />Greater complexity and autonomy<br />New enabling technology - Metalearning<br />Once human-level intelligence is attained, it will be quickly surpassed<br />
  44. 44. Conclusions<br />
  45. 45. Conclusions<br />Artificial Intelligence has made a great deal of progress since its inception in the 1950s<br />
  46. 46. Conclusions<br />Artificial Intelligence has made a great deal of progress since its inception in the 1950s<br />The goal of general AI has been abandoned (at least temporarily) <br />
  47. 47. Conclusions<br />Artificial Intelligence has made a great deal of progress since its inception in the 1950s<br />The goal of general AI has been abandoned (at least temporarily) <br />Useful applications have appeared in all subfields of AI, including: Machine learning, computer vision, robotics, natural language processing and knowledge representation<br />
  48. 48. Conclusions<br />Artificial Intelligence has made a great deal of progress since its inception in the 1950s<br />The goal of general AI has been abandoned (at least temporarily) <br />Useful applications have appeared in all subfields of AI, including: Machine learning, computer vision, robotics, natural language processing and knowledge representation<br />The field continues to evolve rapidly<br />
  49. 49. Conclusions<br />Artificial Intelligence has made a great deal of progress since its inception in the 1950s<br />The goal of general AI has been abandoned (at least temporarily) <br />Useful applications have appeared in all subfields of AI, including: Machine learning, computer vision, robotics, natural language processing and knowledge representation<br />The field continues to evolve rapidly<br />Increased complexity and unpredictability of AI programs will raise important ethics issues and concerns<br />

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