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Slide 1 - Artificial Intelligence
Slide 2 - 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.
Slide 3 - 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
Slide 4 - 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."
Slide 5 - Time Line – The Big Picture 50 60 70 80 90 00 10 1956 Dartmouth conference. 1981 Japanese Fifth Generation project launched as the Expert Systems age blossoms in the US. 1988 AI revenues peak at $1 billion. AI Winter begins. academic $ academic and routine
Slide 6 - 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."
Slide 7 - Evolution of the Main Ideas Wings or not? Games, mathematics, and other knowledge-poor tasks The silver bullet? Knowledge-based systems Hand-coded knowledge vs. machine learning Low-level (sensory and motor) processing and the resurgence of subsymbolic systems Robotics Natural language processing
Slide 8 - 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))
Slide 9 - 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”
Slide 10 - Interest in Subsymbolic AI 40 50 60 70 80 90 00 10
Slide 11 - The Origins of Symbolic AI Games Theorem proving
Slide 12 - Games 1950 Claude Shannon published a paper describing how a computer could play chess. 1952-1962 Art Samuel built the first checkers program 1957 Newell and Simon predicted that a computer will beat a human at chess within 10 years. 1967 MacHack was good enough to achieve a class-C rating in tournament chess. 1994 Chinook became the world checkers champion 1997 Deep Blue beat Kasparpov 2007 Checkers is solved Summary
Slide 13 - Games AI in Role Playing Games – now we need knowledge
Slide 14 - Logic Theorist Debuted at the 1956 summer Dartmouth conference, although it was hand-simulated then. Probably the first implemented A.I. program. LT did what mathematicians do: it proved theorems. It proved, for example, most of the theorems in Chapter 2 of Principia Mathematica [Whitehead and Russell 1910, 1912, 1913]. LT began with the five axioms given in Principia Mathematica. From there, it began to prove Principia’s theorems.
Slide 15 - Logic Theorist LT used three rules of inference: Substitution (which allows any expression to be substituted, consistently, for any variable): From: A  B  A, conclude: fuzzy  cute  fuzzy Replacement (which allows any logical connective to be replaced by its definition, and vice versa): From A  B, conclude A  B Detachment (which allows, if A and A  B are theorems, to assert the new theorem B): From man and man  mortal, conclude: mortal
Slide 16 - Logic Theorist In about 12 minutes LT produced, for theorem 2.45: (p  q)  p (Theorem 2.45, to be proved.) 1. A  (A  B) (Theorem 2.2.) 2. p  (p  q) (Subst. p for A, q for B in 1.) 3. (A  B)  (B  A) (Theorem 2.16.) 4. (p  (p  q))  ((p  q)  p) (Subst. p for A, (p  q) for B in 3.) 5. (p  q)  p (Detach right side of 4, using 2.) Q. E. D.
Slide 17 - Logic Theorist The inference rules that LT used are not complete. The proofs it produced are trivial by modern standards. For example, given the axioms and the theorems prior to it, LT tried for 23 minutes but failed to prove theorem 2.31: [p  (q  r)]  [(p  q)  r]. LT’s significance lies in the fact that it opened the door to the development of more powerful systems.
Slide 18 - Mathematics 1956 Logic Theorist (the first running AI program?) 1961 SAINT solved calculus problems at the college freshman level 1967 Macsyma Gradually theorem proving has become well enough understood that it is usually no longer considered AI.
Slide 19 - Discovery AM “discovered”: Goldbach’s conjecture Unique prime factorization theorem
Slide 20 - What About Things that People Do Easily? Common sense reasoning Vision Moving around Language
Slide 21 - What About Things People Do Easily? If you have a problem, think of a past situation where you solved a similar problem. If you take an action, anticipate what might happen next. If you fail at something, imagine how you might have done things differently. If you observe an event, try to infer what prior event might have caused it. If you see an object, wonder if anyone owns it. If someone does something, ask yourself what the person's purpose was in doing that.
Slide 22 - They Require 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.
Slide 23 - Why? 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.
Slide 24 - Even Children Know a Lot A story described in Charniak (1972): Jane was invited to Jack’s birthday party. She wondered if he would like a kite. She went into her room and shook her piggy bank. It made no sound.
Slide 25 - We Divide Things into Concepts What’s a party? What’s a kite? What’s a piggy bank?
Slide 26 - What is a Concept? Let’s start with an easy one: chair
Slide 27 - Chair?
Slide 28 - Chair?
Slide 29 - Chair?
Slide 30 - Chair?
Slide 31 - Chair?
Slide 32 - Chair?
Slide 33 - Chair?
Slide 34 - Chair?
Slide 35 - Chair?
Slide 36 - Chair?
Slide 37 - Chair?
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Slide 39 - Chair?
Slide 40 - Chair?
Slide 41 - Chair? The bottom line?
Slide 42 - How Can We Teach Things to Computers? A quote from John McCarthy: In order for a program to be capable of learning something, it must first be capable of being told it. Do we believe this?
Slide 43 - Some Things are Easy If dogs are mammals and mammals are animals, are dogs mammals?
Slide 44 - Some Things Are Harder If most Canadians have brown eyes, and most brown eyed people have good eyesight, then do most Canadians have good eyesight?
Slide 45 - Some Things Are Harder If most Canadians have brown eyes, and most brown eyed people have good eyesight, then do most Canadians have good eyesight? Maybe not for at least two reasons: It might be true that, while most brown eyed people have good eyesight, that’s not true of Canadians. Suppose that 70% of Canadians have brown eyes and 70% of brown eyed people have good eyesight. Then assuming that brown-eyed Canadians have the same probability as other brown-eyed people of having good eyesight, only 49% of Canadians are brown eyed people with good eyesight.
Slide 46 - Concept Acquisition Pat Winston’s program (1970) learned concepts in the blocks micro-world.
Slide 47 - Concept Acquisition The arch concept:
Slide 48 - Further Complications from How Language is Used After the strike, the president sent them away. After the strike, the umpire sent them away. The word “strike” refers to two different concepts.
Slide 49 - When Other Words in Context Aren’t Enough I need a new bonnet. The senator moved to table the bill.
Slide 50 - Compiling Common Sense Knowledge CYC (http://www.cyc.com) UT (http://www.cs.utexas.edu/users/mfkb/RKF/tree/ ) WordNet (http://www.cogsci.princeton.edu/~wn/)
Slide 51 - Distributed Knowledge Acquisition Acquiring knowledge for use by people Oxford English Dictionary (http://oed.com/about/contributors/ ) Wikipedia Acquiring knowledge for use by programs ESP (http://www.espgame.org/) Open Mind (http://commons.media.mit.edu:3000/) CYC (http://www.cyc.com)
Slide 52 - Reasoning We can describe reasoning as search in a space of possible situations.
Slide 53 - Breadth-First Search
Slide 54 - Depth-First Search
Slide 55 - The British Museum Algorithm A simple algorithm: Generate and test When done systematically, it is basic depth-first search. But suppose that each time we end a path, we start over at the top and choose the next path randomly. If we try this long enough, we may eventually hit a solution. We’ll call this The British Museum Algorithm or The Monkeys and Typewriters Algorithm http://www.arn.org/docs2/news/monkeysandtypewriters051103.htm
Slide 56 - A Version of Depth-First Search: Branch and Bound Consider the problem of planning a ski vacation. Fly to A $600 Fly to B $800 Fly to C $2000 Stay D $200 (800) Stay E $250 (850) Total cost (1200)
Slide 57 - Problem Reduction Goal: Acquire TV Steal TV Earn Money Buy TV Or another one: Theorem proving in which we reason backwards from the theorem we’re trying to prove.
Slide 58 - Hill Climbing Problem: You have just arrived in Washington, D.C. You’re in your car, trying to get downtown to the Washington Monument.
Slide 59 - Hill Climbing – Some Problems
Slide 60 - Hill Climbing – Is Close Good Enough? A B Is A good enough? Choose winning lottery numbers
Slide 61 - Hill Climbing – Is Close Good Enough? A B Is A good enough? Choose winning lottery numbers Get the cheapest travel itinerary Clean the house
Slide 62 - 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.
Slide 63 - To Interpret the Rule Mass spectometry Ketone group:
Slide 64 - Expert Systems 1975 Mycin attaches 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.
Slide 65 - Expert Systems – Today: Medicine One example domain, medicine, has expert systems whose tasks include: 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
Slide 66 - Expert Systems – Today: Build Your Own eXpertise2Go: http://www.expertise2go.com/ AcquiredIntelligence: http://www.aiinc.ca/demos/ (whales, graduate school)
Slide 67 - Taking the AP Chemistry Exam QUESTION: Sodium azide is used in air bags to rapidly produce gas to inflate the bag. The products of the decomposition reaction are:(a) Na and water.(b) Ammonia and sodium metal.(c) N2 and O2(d) Sodium and nitrogen gas.(e) Sodium oxide and nitrogen gas. (d) Sodium and nitrogen gas.
Slide 68 - SYSTEM-GENERATED ANSWER: # What are the products of the given decomposition reaction? By definition, oxidation-reduction reactions occur when electrons are transferred from the atom that is oxidized to the atom that is reduced. We need to look for changes in the oxidation states of the elements in the reaction. In the reactants, the oxidation state(s) of the element Na is/are (1). In the product, the oxidation state(s) is/are (0). Therefore, the reaction causes a change in oxidation state. Therefore, this is an oxidation reduction reaction. By definition, a Binary Ionic-Compound Decomposition Reaction occurs when a binary ionic compound is heated. Therefore, this reaction is a Binary-Ionic Compound Decomposition reaction. In general, a Binary Ionic-Compound Decomposition Reaction converts a binary ionic-compound into basic elements. In this reaction, NaN3 reacts to produce Na and N2. # The products of the decomposition reaction are:      (d) Sodium and nitrogen gas. The work of Bruce Porter et al here at UT