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

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Artificial IntelligencePast, Present, and Future

Olac FuentesComputer Science Department

UTEP

Artificial Intelligence

A definition:• AI is the science and engineering of making

intelligent machines

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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.

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Artificial IntelligenceAnother 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

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Old Times

The pursuit of “General AI”Objective: Build a machine that exhibits ALL

of the AI features

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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.

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Old Times – The Turing TestA 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.

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Old Times – The Turing TestProblems 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

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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

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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

Machine Learning The key enabling technology of AI

Problem Solving in Computer Science

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Problem Solving in Computer Science• Traditional Approach

– Write a detailed sequence of instructions (a program) that tells the computer how to solve the problem.

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• Machine Learning Approach– Give the computer examples of desired results and let it

learn how to solve the problem.

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learn how to solve the problem.– Advantage: It allows to solve problems that we can’t

solve with the traditional approach

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solve with the traditional approach– Most applications in other AI areas are based on machine

learning15






solve with the traditional approach– Most applications in other AI areas are based on machine

learning16

Computers that learn How?

Very active research area

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Very active research area– Extract statistical regularities from data

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Very active research area– Extract statistical regularities from data– Find decision boundaries

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Very active research area– Extract statistical regularities from data– Find decision boundaries– Find decision rules

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Very active research area– Extract statistical regularities from data– Find decision boundaries– Find decision rules– Imitate human brain

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Very active research area– Extract statistical regularities from data– Find decision boundaries– Find decision rules– Imitate human brain– Imitate biological evolution

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Very active research area– Extract statistical regularities from data– Find decision boundaries– Find decision rules– Imitate human brain– Imitate biological evolution– Combine several approaches

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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

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

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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

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

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

What does Stanley learn?A mapping from sensory inputs to driving commands

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

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

Cameras

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What has AI done for us? Computer Vision - Face

Detecting Cameras

What has AI done for us? Reasoning

Successful applications:• Commercial planning systems• Chess playing programs• Checkers playing programs• Optimal solution to Rubik’s cube

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.

What has AI done for us? Natural language processing

Successful applications:• Dictation systems• Text-to-speech systems• Text classification• Automated summarization• Automated translation


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.





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 to Spanish (by Google)Los Dodgers se convirtió en el quinto equipo en la moderna




Translation to Spanish (by Google - 2008)Los Dodgers se convirtió en el quinto equipo en la moderna


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.



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.

The Future of AI

The Future of AIMaking predictions is hard, especially about the future - Yogi

Berra

The Future of AIMaking 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

Conclusions

Conclusions• Artificial Intelligence has made a great deal of progress

since its inception in the 1950s


since its inception in the 1950s• The goal of general AI has been abandoned (at least

temporarily)



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





• The field continues to evolve rapidly• Increased complexity and unpredictability of AI programs

will raise important ethics issues and concerns

Documents

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