An Introduction to
Artificial Intelligence
Alexander Yates
Temple University
Computer and Information Sciences
Temple University
Computer and Information Sciences
Popular Conception of Artificial Intelligence
Temple University
Computer and Information Sciences
Artificial Intelligence, more realistically
Temple University
Computer and Information Sciences
Autonomous Navigation
• Boston Dynamics’ Big Dog (or fly-dog)
– 0:30, 1:25
• Self-driving cars
– DARPA’s Grand Challenge
“Ghostrider” (DARPA Grand Challenge entry)
• 1:00, 2:20
– Google’s self-driving car:
http://www.youtube.com/watch?v=cdgQpa1pUUE
0:30-1:00
Temple University
Computer and Information Sciences
What is Artificial Intelligence?
It’s the science of making machines behave as if they
understand the world the way humans do.
Specifically, AI researchers build machines that can
• know (or believe)
• reason (or think or infer or draw conclusions)
• learn
• communicate (talk and listen)
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Expert Systems:
Machines than can know and reason
Idea: Make a machine that’s better than a
human expert.
Early example:
• MYCIN: identifies bacteria causing severe
infections, such as bacteremia and meningitis.
– It could propose an acceptable therapy in about 69% of
cases, which was better than the performance of infectious
disease experts who were judged using the same criteria.
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Computer and Information Sciences
Expert Systems: Machines than can reason
Second example: Chinook
Schaeffer et al.: "Checkers Is Solved“ (2007)
– They proved that the best an opponent can do against their “Chinook” system
is to draw.
– It had already been world champion since 1994
Interesting Sidebar:
Dr. Marion Tinsley (February 3, 1927–April 3, 1995) is
considered the greatest checkers player who ever lived.
He was world champion from 1955–1958 and 1975–1991.
Tinsley never lost a World Championship match, and lost
only nine games (two of them to the Chinook computer
program) in his entire 45 year career. He withdrew from
championship play during the years 1958–1975,
relinquishing the title during that time.
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Computer and Information Sciences
Machines that can learn
One of the more recent advances of AI has been
the development of systems that can
– Learn from training
– Learn from experience
– Adapt to changing conditions
25-30 years ago, this was crazy.
Now, there are toolkits: e.g., WEKA and LibSVM
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Machines that can learn
Some sample applications:
1) Machines that can determine the amount of air
pollution, based on satellite readings
2) Software that can detect network intruders based on
network traffic patterns
3) Programs that can diagnose tumors based on MRI
scans and CAT scans
All of these are available today.
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Conversational Agents:
Machines that can hold a conversation
• Chomsky chatbot demo
• Video of two chatbots talking with each other
• Speech Recognition
– (e.g., Siri)
– You’ve probably interacted with this many times
– Right now, so-so accuracy, but it’s going to change
human-machine interactions
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Some problems for AI:
1. How can you make a machine know (or believe)?
2. How can you make a machine think (or reason)?
3. How can you make machines perceive and act upon
the real world?
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Computational Linguistics
Computational linguistics is the area of AI that
deals with language.
A brief tour of computational linguistics today:
–
–
–
–
Machine translation
Question Answering
Information retrieval
Information Extraction
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Machine Translation
Machine translation is the task of translating
automatically between languages, like a
human interpreter would.
• Google translate demo
– English Malay  Hungarian English
– “I think, Watson, that you have put on seven and a
half pounds since I saw you.”
• Very hard problem – it’s considered “AI-complete”
• But a solution is worth $$$$$
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Information Retrieval
Information retrieval = Web search
(the Google kind)
The academic study focuses on how to best find the
most informative document in a large collection.
1996: 2 Stanford students start working on an
information retrieval system called “BackRub”
2004: Their company, Google, has its IPO and makes
them billionaires.
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Question Answering
Question Answering is the task of finding a
specific answer to an arbitrary question in
English, using the Web.
Watson
Qualim demo
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Information Extraction
Definition:
The automatic extraction of structured information from
unstructured documents.
Overall Goals:
– Making information more accessible to people
– Making information machine-processable
Practical Goal: Build large databases from the
information contained in text
TextRunner Demo
16
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How AI fits into Computer Science
Approximation
Algorithms and
SAT Solvers
Databases
Data Mining
Human
Computer
Interaction
Theory
Bug
Detection,
Fault
Analysis
AI
Graphics
Autonomous
Video Game
Characters
Expert
Systems and
Applications
Systems
Software
Engineering
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Why study or work on AI?
• It’s exciting – everything is always changing!
– True in Computer Science, especially true for AI
– Pressure to innovate: we’re so far from true AI, we
have to constantly re-think how we’re doing things
• It’s useful and it works
– AI ideas show up everywhere in everyday life
• It can have a huge impact
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Impact of AI
Some of AI’s contributions to the world:
– Fraud detection by banks
– Control systems for car brakes
– Automatic Zip Code readers used by the U.S.P.S.
– License plate recognition for EZ-Pass
– Logistic planning used by U.S. Army since the first Iraq War (saving
more money than ever was invested into AI research by the
government)
– Deep Blue, and other games
– Intelligent user interfaces
– Google & web search
– Mars rovers
– …
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Impact of Computational Linguistics
– Web search
– Speech Recognition
– Speech interfaces used in car GPS systems
– Automatic Arabic translators used by the U.S. Army
– Semantic Web applications (eg, Google News)
– Faster desktop search applications
– Siri
– …
– Many more to come (still a young field)