Transcript Introduction - Tamara L Berg

Introduction
Tamara Berg
CS 590-133 Artificial Intelligence
Many slides throughout the course adapted from Dan Klein, Stuart Russell,
Andrew Moore, Svetlana Lazebnik, Percy Liang, Luke Zettlemoyer
Today
• Course Info
• What is AI?
• History of AI
• Current state of AI
Course Information
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Instructor: Tamara Berg ([email protected])
Office Hours: FB 236, Tues/Thurs 4:45-5:45pm
Course website: http://tamaraberg.com/teaching/Spring_14/
Course mailing list: [email protected]
• TAs: Shubham Gupta & Rohit Gupta
TA office hours: TBD
• Announcements, readings, schedule, etc, will all be posted to
the course webpage. Schedule may be modified as needed
over the semester. Check frequently!
Course Information
• Textbook: “Artificial Intelligence A Modern Approach” Russell & Norvig, 3rd
edition
• Prerequisites:
– Programming knowledge and data structures (COMP 401 and 410) are required
– Reasonable familiarity with probability, algorithms, calculus also highly desired
– There will be a lot of math and programming
• Work & Grading
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Readings (mostly from textbook)
5-6 assignments including written questions, programming, or both
2 midterms (approximate dates are on course website) and final exam
Grading will consist of 60% assignments, 40% exams. For borderline cases
participation in class or via the mailing list may also be considered.
Programming
• Students are expected to know how to program.
• Programming assignments will be in python – useful language
to know, used in many current AI courses, not too hard to pick
up given previous programming experience.
• First week – install python on your laptops, do a python
tutorial
• TAs will also hold drop in tutorials early next week (probably
Mon/Tues, times will be posted to website). Make sure to
attend if you’re new to python or want a refresher.
Course Information
Late policy:
• Assignments must be turned in electronically by 11:59pm
on the listed due date.
• Students will be allowed 5 free homework late days of
their choice over the semester (you don't need to ask
ahead of time, just use them and we will keep track).
• After those are used late homework will be accepted up
to 1 week late, with a 10% reduction in value per day
late.
Course Information
Honor code:
• Students are encouraged to complete the assignments in
groups of 2.
• You may discuss problems at a high level with other
students in the class, but all code and written responses
should be original within your pair.
• To protect the integrity of the course, we will actively
check for code or written plagiarism (both from current
classmates and the internet).
• Exams will be closed book.
About me
1997-2001
Undergrad at U.W. Madison
CS and Math
2001-2007
Grad at U.C. Berkeley
Ph.D. in CS
2007-2008
Postdoc at Yahoo! Research
2008-2013
Assistant Prof at SBU
2013Assistant Prof at UNC
My research
interests
Object Detection
20 object classes
39% accuracy, Girshick et al
leonberg
Yellow lady’s slipper
Image Classification
1000 classes
62.5% accuracy, Krizhevsky et al
Image Parsing
33 labels
55% accuracy,
Tighe et al
Human-centric Computer Vision
Computer Vision
BabyTalk: Generating natural language
image descriptions
This is a picture of one
sky, one road and one
sheep. The gray sky is
over the gray road. The
gray sheep is by the gray
road.
Here we see one road,
one sky and one bicycle.
The road is near the blue
sky, and near the colorful
bicycle. The colorful
bicycle is within the blue
sky.
This is a picture of two
dogs. The first dog is
near the second furry
Recognizing Clothing
Application: Pose Independent Retrieval
Shorts
Blazer
T-shirt
About you?
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Undergrad/grad
Year
Major/Minors
Background in:
– Programming
– Calculus
– Probability
– Python
Sci-Fi AI
Knowledge
representation
Planning
Social
Intelligence
Reasoning
Learning
AI
Natural
Language
Processing
Creativity
Motion &
Manipulation
(robotics)
Perception
(computer
vision, speech)
What is AI?
• Definitions of AI:
1. Thinking humanly
2. Acting humanly
3. Thinking Rationally
4. Acting rationally
AI definition 1: Thinking humanly
• Need to study the brain as an information
processing machine: cognitive science and
neuroscience
AI definition 1: Thinking humanly
Can we build a brain?
AI definition 1: Thinking humanly
• Can we build a brain?
AI definition 2: Acting humanly
• The Turing Test
• What capabilities would a computer need to have to pass the
Turing Test?
– Natural language processing
– Knowledge representation
– Automated reasoning
– Machine learning
A. Turing, Computing machinery and intelligence, Mind 59, pp. 433-460, 1950
The Turing Test
• Turing predicted that by the year 2000, machines would be able to fool 30%
of human judges for five minutes
• Loebner prize
– 2008 competition: each of 12 judges was given five minutes
to conduct simultaneous, split-screen conversations with
two hidden entities (human and chatterbot). The winner,
Elbot of Artificial Solutions, managed to fool three of the
judges into believing it was human [Wikipedia].
Turing Test: Criticism
• Success depends on deception!
• Chatbots can do well using “cheap tricks”
• First example: ELIZA (1966)
• Chinese room argument: one may simulate
intelligence without having true intelligence
(more of a philosophical objection)
A better Turing test?
http://www.newyorker.com/online/blogs/elements/2013/08/why-cant-mycomputer-understand-me.html
A better Turing test?
• Multiple choice questions that can be easily
answered by people but cannot be answered
by computers using “cheap tricks”:
•The trophy would not fit in the brown suitcase
because it was so small.
What was so small?
• The trophy
• The brown suitcase
H. Levesque, On our best behaviour, IJCAI 2013
A better Turing test?
• Multiple choice questions that can be easily
answered by people but cannot be answered
by computers using “cheap tricks”:
•The trophy would not fit in the brown suitcase
because it was so large.
What was so large?
• The trophy
• The brown suitcase
H. Levesque, On our best behaviour, IJCAI 2013
A better Turing test?
• Multiple choice questions that can be easily
answered by people but cannot be answered
by computers using “cheap tricks”:
•The large ball crashed right through the table
because it was made of styrofoam.
What was made of styrofoam?
• The large ball
• The table
H. Levesque, On our best behaviour, IJCAI 2013
A better Turing test?
• Multiple choice questions that can be easily
answered by people but cannot be answered
by computers using “cheap tricks”:
•The large ball crashed right through the table
because it was made of steel.
What was made of steel?
• The large ball
• The table
H. Levesque, On our best behaviour, IJCAI 2013
A better Turing test?
• Multiple choice questions that can be easily
answered by people but cannot be answered
by computers using “cheap tricks”:
•Sam tried to paint a picture of shepherds with
sheep, but they ended up looking like golfers.
What looked like golfers?
• The shepherds
• The sheep
H. Levesque, On our best behaviour, IJCAI 2013
A better Turing test?
• Multiple choice questions that can be easily
answered by people but cannot be answered
by computers using “cheap tricks”:
•Sam tried to paint a picture of shepherds with
sheep, but they ended up looking like rabbits.
What looked like rabbits?
• The shepherds
• The sheep
H. Levesque, On our best behaviour, IJCAI 2013
A better Turing test?
Why are these questions hard for
computers??
H. Levesque, On our best behaviour, IJCAI 2013
A better Turing test?
• Advantages over standard Turing test
• Test can be administered and graded by machine
• Does not depend on human subjectivity
• Does not require ability to generate English
sentences
• Questions cannot be evaded using verbal dodges
• Questions can be made “Google-proof”
H. Levesque, On our best behaviour, IJCAI 2013
AI definition 3&4: Rationality
• A rational agent acts to optimally achieve its goals
• Goals are application-dependent and are
expressed in terms of the utility of outcomes
• Being rational means maximizing your
(expected) utility
• This definition of rationality only concerns the
decisions/actions that are made, not the cognitive process
behind them
• In practice, utility optimization is subject to the agent’s
computational constraints (bounded rationality or bounded
optimality)
Utility maximization formulation
• Advantages
– Generality: goes beyond explicit reasoning, and
even human cognition altogether
– Practicality: can be adapted to many real-world
problems
– Naturally accommodates uncertainty
– Amenable to good scientific and engineering
methodology
– Avoids philosophy and psychology
• Disadvantages?
History of AI
Image source
Origins of AI: Early excitement
1940s
1950s
1956
First model of a neuron (W. S. McCulloch & W. Pitts)
Hebbian learning rule
Cybernetics
Turing Test
Perceptrons (F. Rosenblatt)
Computer chess and checkers (C. Shannon, A. Samuel)
Machine translation (Georgetown-IBM experiment)
Theorem provers (A. Newell and H. Simon,
H. Gelernter and N. Rochester)
Dartmouth meeting: “Artificial Intelligence” adopted
Herbert Simon, 1957
•“It is not my aim to surprise or shock you –
but … there are now in the world
machines that think, that learn and that
create. Moreover, their ability to do these
things is going to increase rapidly until –
in a visible future – the range of problems
they can handle will be coextensive with
the range to which human mind has been applied. More
precisely: within 10 years a computer would be chess
champion, and an important new mathematical theorem
would be proved by a computer.”
• Simon’s prediction came true – but forty years later
instead of ten
Harder than originally thought
• 1966: Eliza chatbot (Weizenbaum)
• “ … mother …” → “Tell me more about your family”
• 1954: Georgetown-IBM experiment
• Completely automatic translation of more than sixty Russian
sentences into English
• Only six grammar rules, 250 vocabulary words, restricted to
organic chemistry
• Promised that machine translation would be solved in three to
five years (press release)
• Automatic Language Processing Advisory Committee (ALPAC)
report (1966): machine translation has failed
• “The spirit is willing but the flesh is weak.” →
“The vodka is strong but the meat is rotten.”
Blocks world (1960s – 1970s)
Larry Roberts, MIT, 1963
???
History of AI: Taste of failure
1940s
First model of a neuron (W. S. McCulloch & W. Pitts)
Hebbian learning rule
Cybernetics
1950s
Turing Test
Perceptrons (F. Rosenblatt)
Computer chess and checkers (C. Shannon, A. Samuel)
Machine translation (Georgetown-IBM experiment)
Theorem provers (A. Newell and H. Simon,
H. Gelernter and N. Rochester)
Late 1960s
Machine translation deemed a failure
Neural nets deprecated (M. Minsky and S. Papert, 1969)*
Late 1970s
The first “AI Winter”
*A sociological study of the official history of the perceptrons controversy
History of AI to the present day
1980s
Expert systems boom
Late 1980sEarly 1990s
Expert system bust; the second “AI winter”
Mid-1980s
Neural networks and back-propagation
Late 1980s
Probabilistic reasoning on the ascent
1990s-Present
Machine learning everywhere
Big Data
Deep Learning
History of AI on Wikipedia
AAAI Timeline
Building Smarter Machines: NY Times Timeline
NY Times article
What accounts for recent successes in AI?
• Faster computers
• The IBM 704 vacuum tube machine that played chess
in 1958 could do about 50,000 calculations per
second
• Deep Blue could do 50 billion calculations per second
– a million times faster!
• Dominance of statistical approaches, machine
learning
• Big data
• Crowdsourcing
What can AI do today?
IBM Watson
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http://www-03.ibm.com/innovation/us/watson/
NY Times article
Trivia demo
IBM Watson wins on Jeopardy (February 2011)
Self-driving cars
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Google’s self-driving car passes 300,000 miles (Forbes, 8/15/2012)
Nissan pledges affordable self-driving car models by 2020
(CNET, 8/27/2013)
Natural Language
• Speech technologies
• Google voice search
• Apple Siri
• Machine translation
• translate.google.com
• Comparison of several translation systems
Vision
• OCR, handwriting recognition
• Face detection/recognition: many consumer
cameras, Apple iPhoto
• Visual search: Google Goggles, search by image
• Vehicle safety systems: Mobileye
Mathematics
• In 1996, a computer program written by researchers at
Argonne National Laboratory proved a mathematical
conjecture unsolved for decades
• NY Times story: “[The proof] would have been
called creative if a human had thought of it”
• Mathematical software:
Games
• IBM’s Deep Blue defeated the reigning world chess
champion Garry Kasparov in 1997
• 1996: Kasparov Beats Deep Blue
“I could feel – I could smell – a new kind
of intelligence across the table.”
• 1997: Deep Blue Beats Kasparov
“Deep Blue hasn't proven anything.”
• In 2007, checkers was “solved” (though checkers programs
had been beating the best human players for at least a
decade before then)
• Science article
Logistics, scheduling, planning
• During the 1991 Gulf War, US forces deployed an
AI logistics planning and scheduling program that
involved up to 50,000 vehicles, cargo, and people
• NASA’s Remote Agent software operated the
Deep Space 1 spacecraft during two experiments
in May 1999
• In 2004, NASA introduced the MAPGEN system to
plan the daily operations for the Mars Exploration
Rovers
Robotics
• Mars rovers
• Autonomous vehicles
– DARPA Grand Challenge
– Self-driving cars
• Autonomous helicopters
• Robot soccer
– RoboCup
• Personal robotics
– Humanoid robots
– Robotic pets
– Personal assistants?
Towel-folding robot
YouTube Video
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J. Maitin-Shepard, M. Cusumano-Towner, J. Lei and P. Abbeel, Cloth Grasp Point Detection based
on Multiple-View Geometric Cues with Application to Robotic Towel Folding, ICRA 2010
More clothes folding
Outline of the Class
• Part 1: Agents & Decisions
– Fast search
– Constraint satisfaction
– Reinforcement Learning
• Part 2: Modeling Uncertainty
– Probability
– Bayes Nets
• Part 3: Learning from labeled Data
– Classification
• Part 4: Sub-Areas of AI
– NLP
– Vision
Philosophy of the class
• Our goal is to use machines to solve hard problems that
traditionally would have been thought to require human
intelligence
• We will try to follow a sound scientific/engineering
methodology
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Consider relatively limited application domains
Use well-defined input/output specifications
Zero in on essential problem features
Focus on principles and basic building blocks
For next week
• Check out the class website
http://www.tamaraberg.com/teaching/Spring_14/
• Get the book. Do the readings.
• Do a python tutorial. TAs will hold an in person
drop-in tutorial. Dates/times will be posted to the
class website (probably Mon&Tues evening).