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Artificial Intelligence

Artificial Intelligence

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Artificial Intelligence

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  1. Artificial IntelligencePast, Present, and Future Olac FuentesAssociate ProfessorComputer Science DepartmentUTEP

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

  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.

  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

  5. Artificial Intelligence • Weak AI Claim - Machines can possibly act as if they were intelligent • Strong AI Claim - Machines can actually think intelligently

  6. Artificial IntelligenceWhy?

  7. Artificial IntelligenceWhy? • Building an intelligent machine will help us better understand natural intelligence

  8. Artificial IntelligenceWhy? • Building an intelligent machine will help us better understand natural intelligence • An intelligent machine can be used to perform difficult and useful tasks - whether it models human intelligence or not

  9. Artificial IntelligenceBrief History • Field was founded in 1956, initially led by John Mc Carthy, Marvin Minsky, Allen Newell and Herbert Simon (known as the “fourfathers” of A.I.) • Great initial optimism, grandiose objectives (“machines will be capable, within twenty years, of doing any work a man can do” – H. Simon) • Emphasis on symbolic reasoning • Huge government spending • Disappointing results

  10. Artificial Intelligence - Brief HistoryThe A.I. Winter • 1966: the failure of machine translation, • 1970: the abandonment of connectionism, • 1971−75: DARPA's frustration with the Speech Understanding Research program at Carnegie Mellon University, • 1973: the large decrease in AI research in the United Kingdom in response to the Lighthill report, • 1973−74: DARPA's cutbacks to academic AI research in general, • 1987: the collapse of the Lisp machine market, • 1988: the cancellation of new spending on AI by the Strategic Computing Initiative, • 1993: expert systems slowly reaching the bottom, • 1990s: the quiet disappearance of the fifth-generation computer project's original goals,

  11. Artificial IntelligenceBrief History – The Comeback • Rebirth of Connectionism • The backpropagation algorithm (Hinton and others, 1986, PDP group) • Machine learning becomes usable • The ID3 and C4.5 algorithms – decision trees for the masses - R. Quinlan, 86 • Increased computing power • Increased availability of data in electronic form • Behavior-based (or “emerging”) A.I. • “A robust layered control system for a mobile robot” – R. Brooks, 85 • “Intelligence is in the eye of the beholder”, “The world is its own best model”, “Elephants don’t play chess”, “We don’t need no representation” • Agent-based architectures (Maes, and many others) • Active Vision • The goal of machine perception is not to build a 3D model of the world, but to extract information to perform useful tasks (D. Ballard, Y. Aloimonos)

  12. Artificial IntelligenceBrief History – Present Times • Realistic expectations • Lots of useful applications • Research divided into subareas (vision, learning, NLP, planning, etc.) • Little work on overall intelligence

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

  14. 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.

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

  16. Old Times – The Turing Test Problems with the Turing test:

  17. 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

  18. 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

  19. 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?

  20. 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? • Testing the machine vs. testing the judge

  21. 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? • Testing the machine vs. testing the judge • Too hard! – Very useful applications can be built that don’t pass the Turing test

  22. 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 • Robotics • Computer vision • Natural language processing • Knowledge representation and reasoning

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

  24. 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. 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

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

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

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

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

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

  31. What has AI done for us? Reasoning Successful applications: • Route planning systems • Game playing programs

  32. What has AI done for us? Reasoning 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.

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

  34. What has AI done for us? Natural language processingAutomated 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.

  35. What has AI done for us? Natural language processingAutomated 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 - 2009) 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.

  36. What has AI done for us? Natural language processingAutomated Translation Translation to Spanish (by Google - 2009) 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 processingAutomated Translation Translation to Spanish (by Google – 2009) 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 Google – 2009) 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.

  38. What has AI done for us? Natural language processingAutomated 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.

  39. What has AI done for us? Natural language processingAutomated 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 Grandes Ligas en ganar un juego en el que no recibieron una respuesta positiva, derrotando a los Angelinos 1-0. error de Weaver en una rola lenta dio lugar a una carrera sucia por los Dodgers en el quinto.

  40. What has AI done for us? Natural language processingAutomated Translation Translation to Spanish (by Google – 2010) Los Dodgers se convirtió en el quinto equipo en la historia moderna de Grandes Ligas en ganar un juego en el que no recibieron una respuesta positiva, derrotando a los Angelinos 1-0. error de Weaver en una rola lenta dio lugar a una carrera sucia por los Dodgers en el quinto. Translation back to English (by Google – 2010) The Dodgers became the fifth side in the modern history of baseball to win a game that did not get a hit, defeating the Angels 1-0. Weaver's error on a slow roller led to an unearned run for the Dodgers in the fifth.

  41. The Future of AI

  42. The Future of AI 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. Conclusions • 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

  50. AI and Psychology Some questions/issues: • Can A.I. algorithms be used to model natural intelligence? • How can we exploit our knowledge of human intelligence to develop artificially intelligent systems? • Can psychology help settle the Strong A.I. vs. Weak A.I. debate?