An Introduction to Artificial Intelligence

Alexander Yates

Temple UniversityComputer and Information Sciences

Temple UniversityComputer and Information Sciences

Popular Conception of Artificial Intelligence

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Artificial Intelligence, more realistically

Temple UniversityComputer 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

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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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Expert Systems: Machines than can reason

Second example: ChinookSchaeffer 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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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

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How AI fits into Computer Science

AI

Graphics

Human Computer Interaction

Databases

Data Mining

Systems

Expert Systems and Applications

Theory

Software Engineering

Bug Detection,

Fault Analysis

Approximation Algorithms and

SAT Solvers

Autonomous Video Game Characters

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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)