The Foundations of Artificial Intelligence Our Working

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. Mc. Carthy, 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: 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 Mc. Culloch 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 Symbolic Subsymbolic Machine Learning

What Are the Components of Intelligence?

Image Perception 424 d 961 d 03000003 e 00000028000000 b 2030000 a 506000001000000581 d 0300232 e 00000200000000000 ffffff 00 e 0 a 288208 a 388 a 00 a 2880080380288200 a 38 a 0082080380380 a 00 a 28 8038 a 380380000000 a 0080380280 a 00 a 008008 a 380280 a 00 a 000000000 a 00 a 0000000000 a 0000380380380 eb 8 e 00 e 380 e 80 e 38 abf 8 e 00 e 38 aab 8 e 380380 a 38 a 388 abfe 3 fffffc 000 e 1 c 71 c 775 c 71 c 701 c 71 c 074071 c 700775 e 01 c 71 c 0740700700701 c 71 c 01 c 774070000 0001 e 01 c 0740700700701 e 01 c 7740700700701 e 00000007007 01 e 0000000001 e 00001 c 074070071 c 700700775 c 71 c 7 fc 701 c 71 c 7740700700775 c 71 ff 7 fffffc 000 e 08208208208208008 0200208200220 a 00820802002008208008220020000 a 00800802 00200200200 a 0080082200200 a 0000000200200 a 00000 a 000008020020020820002002002082 a 02008208208220020 0200220820820 aa 2 aaaabc 000 e 111101111101111001001001101 01111101011001001001011011114011110010000000150100100101 101101001111001001011011000000000000000000 11000010011000001001010100110150001011011001001001010100 110151511 c 000 e 00000000000000000000000000000000000000000000000000000 000000000000000000

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 Start state What are the states? http: //www. javaonthebrain. com/java/puzz 15/ 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? B A Is A good enough? • Choose winning lottery numbers

Hill Climbing – Is Close Good Enough? B A 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 Start GPS (General Problem Solver) 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? Find me stuff about dogs who save people’s lives. • How can we represent it and use it? • How can we acquire it?

But What About Knowledge? • Why do we need it? Find me stuff about dogs who save people’s lives. Two beagles spot a fire. Their barking alerts neighbors, who call 911. • How can we represent it and use it? • How can we acquire it?

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 x 1 and x 2 such that: • x 1 + x 2 = molecular weight + 28, • x 1 -28 is a high peak, • x 2 – 28 is a high peak, and • at least one of x 1 or x 2 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 IBM’s site: http: //www-03. ibm. com/innovation/us/watson/what-is-watson/index. html Introduction: http: //www. youtube. com/watch? v=FC 3 Iry. Wr 4 c 8 Watch a sample round: http: //www. youtube. com/watch? v=WFR 3 l. Om_xh. E From Day 1 of the real match: http: //www. youtube. com/watch? v=se. Nkj. Yy. G 3 g. I Bad Final Jeopardy: http: //www. youtube. com/watch? v=mwkoab. Tl 3 v. M&feature=relmfu Explanation: http: //thenumerati. net/? post. ID=726 How does Watson win? http: //www. youtube. com/watch? v=d_y. XV 22 O 6 n 4

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 A machine like that is like 500, 000 of me sitting at Google and Pubmed. http: //www. wired. com/wiredscience/2012/10/watson-for-medicine/

But What About Things That All of Us Know?