The Foundations of Artificial Intelligence. Our Working Definition of AI Artificial intelligence is the study of how to make computers do things that.

The Foundations of Artificial Intelligence

Our Working Definition of AI

Artificial intelligence is the study of how to make computers do things that people are better at or would be better at if:

• they could extend what they do to a World Wide Web-sized amount of data and

• not make mistakes.

Why AI?

"AI can have two purposes. One is to use the power of computers to augment human thinking, just as we use motors to augment human or horse power. Robotics and expert systems are major branches of that. The other is to use a computer's artificial intelligence to understand how humans think. In a humanoid way. If you test your programs not merely by what they can accomplish, but how they accomplish it, they you're really doing cognitive science; you're using AI to understand the human mind."

- Herb Simon

A Time Line

View the time line

The Dartmouth Conference and the Name Artificial Intelligence

J. McCarthy, M. L. Minsky, N. Rochester, and C.E. Shannon. August 31, 1955. "We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it."

The Origins of AI Hype

1950 Turing predicted that in about fifty years "an average interrogator will not have more than a 70 percent chance of making the right identification after five minutes of questioning".

1957 Newell and Simon predicted that "Within ten years a computer will be the world's chess champion, unless the rules bar it from competition."

Symbolic vs. Subsymbolic AI

Subsymbolic AI: Model intelligence at a level similar to the neuron. Let such things as knowledge and planning emerge.

Symbolic AI: Model such things as knowledge and planning in data structures that make sense to the programmers that build them.

(blueberry (isa fruit) (shape round) (color purple) (size .4 inch))

The Origins of Subsymbolic AI

1943 McCulloch and Pitts A Logical Calculus of the Ideas Immanent in Nervous Activity

“Because of the “all-or-none” character of nervous activity, neural events and the relations among them can be treated by means of propositional logic”

The Origins of Symbolic AI

• Games

• Theorem proving

Knowledge Acquisition

Hand Crafted Machine Learning

Symbolic

Subsymbolic

What Are the Components of Intelligence?

Image 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

Image Perception

But We’re Still Ahead

http://www.captcha.net/

But We’re Still Ahead

But We’re Still Ahead

Reasoning

We can describe reasoning as search in a space of possible situations.

Recall the 8-Puzzle

What are the states?

http://www.javaonthebrain.com/java/puzz15/

Start state Goal state

Hotel Maid

States:

Start state:

Operators:

Goal state:

What is a Heuristic?

Example

From the initial state, move A to the table. Three choices for what to do next.

A local heuristic function: Add one point for every block that is resting on the thing it is supposed to be resting on. Subtract one point for every block that is sitting on the wrong thing.

A New Heuristic

From the initial state, move A to the table. Three choices for what to do next.

A global heuristic function: For each block that has the correct support structure (i. e., the complete structure underneath it is exactly as it should be), add one point for every block in the support structure. For each block that has an incorrect support structure, subtract one point for every block in the existing support structure.

Hill Climbing – Another Example

Problem: You have just arrived in Washington, D.C. You’re in your car, trying to get downtown to the Washington Monument.

Hill Climbing – Some Problems

Hill Climbing – Is Close Good Enough?

AB

Is A good enough?• Choose winning lottery numbers

Hill Climbing – Is Close Good Enough?

AB

Is A good enough?• Choose winning lottery numbers• Get the cheapest travel itinerary• Clean the house

The Silver Bullet?

Is there an “intelligence algorithm”?

1957 GPS (General Problem Solver)

Start Goal

The Silver Bullet?

Is there an “intelligence algorithm”?

1957 GPS (General Problem Solver)

Start Goal

What we think now:

Probably not

But What About Knowledge?

•Why do we need it?

•How can we represent it and use it?

•How can we acquire it?

Find me stuff about dogs who save people’s lives.

But What About Knowledge?

•Why do we need it?

•How can we represent it and use it?

•How can we acquire it?

Find me stuff about dogs who save people’s lives.

Two beagles spot a fire. Their barking alerts neighbors, who call 911.

Expert Systems

Expert knowledge in many domains can be captured as rules.

Dendral (1965 – 1975)

If: The spectrum for the molecule has two peaks at masses x1 and x2 such that:

• x1 + x2 = molecular weight + 28,

• x1 -28 is a high peak,

• x2 – 28 is a high peak, and

• at least one of x1 or x2 is high,

Then: the molecule contains a ketone group.

To Interpret the Rule

Mass spectometry

Ketone group:

Expert Systems in Medicine

1975 Mycin attached probability-like numbers to rules:

If: (1) the stain of the organism is gram-positive, and (2) the morphology of the organism is coccus, and (3) the growth conformation of the organism is clumps

Then: there is suggestive evidence (0.7) that the identity of the organism is stphylococcus.

Watson

How does Watson win? http://www.youtube.com/watch?v=d_yXV22O6n4

Watch a sample round: http://www.youtube.com/watch?v=WFR3lOm_xhE

From Day 1 of the real match: http://www.youtube.com/watch?v=seNkjYyG3gI

Introduction: http://www.youtube.com/watch?v=FC3IryWr4c8

IBM’s site: http://www-03.ibm.com/innovation/us/watson/what-is-watson/index.html

Bad Final Jeopardy: http://www.youtube.com/watch?v=mwkoabTl3vM&feature=relmfu

Explanation: http://thenumerati.net/?postID=726

Expert Systems – Today: Medicine

Expert systems work in all these areas:

• arrhythmia recognition from electrocardiograms

• coronary heart disease risk group detection

• monitoring the prescription of restricted use antibiotics

• early melanoma diagnosis

• gene expression data analysis of human lymphoma

• breast cancer diagnosis

Dr. Watson

http://www.wired.com/wiredscience/2012/10/watson-for-medicine/

A machine like that is like 500,000 of me sitting at Google and Pubmed.

But What About Things That All of Us Know?