Transcript Introduction to intelligent systems

Artificial Intelligence
Past, Present, and Future
Olac Fuentes
Computer Science Department
UTEP
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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 Intelligence
Another definition:
• AI is the science and engineering of making
machines that are capable of:
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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 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.
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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
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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)
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Machine Learning
The key enabling technology of AI
Problem Solving in Computer Science
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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.
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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.
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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
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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
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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
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Computers that learn
How?
Very active research area
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Computers that learn
How?
Very active research area
– Extract statistical regularities from data
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Computers that learn
How?
Very active research area
– Extract statistical regularities from data
– Find decision boundaries
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Computers that learn
How?
Very active research area
– Extract statistical regularities from data
– Find decision boundaries
– Find decision rules
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Computers that learn
How?
Very active research area
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Extract statistical regularities from data
Find decision boundaries
Find decision rules
Imitate human brain
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Computers that learn
How?
Very active research area
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Extract statistical regularities from data
Find decision boundaries
Find decision rules
Imitate human brain
Imitate biological evolution
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Computers that learn
How?
Very active research area
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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)
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What has AI done for us?
Machine Learning – Netflix movie recommender system
Very active research area
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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
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
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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
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.
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.
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.
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.
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.
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.
The Future of AI
The Future of AI
Making predictions is hard, especially about the future - Yogi
Berra
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
Conclusions
Conclusions
• Artificial Intelligence has made a great deal of progress
since its inception in the 1950s
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)
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
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
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