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1 : Artificial Intelligence Our Attempt to Build Models of Ourselves Elaine Rich
2 : One Vision of an AI
3 : A Calmer Vision
4 : Could AI Stop This?
5 : What is Artificial Intelligence? A.I. is the study of how to make computers do things at which, at the moment, people are better.
6 : Or, Stepping Back Even Farther, Can We Build Artificial People? Historical attempts The modern quest for robots and intelligent agents Us vs. Them
7 : Historical Attempts - Frankenstein Frankenstein creates the fiend - illustration by Bernie Wrightson (© 1977)  The original story, published by Mary Shelley, in 1818, describes the attempt of a true scientist, Victor Frankenstein, to create life.
8 : Historical Attempts – The Turk
9 : Historical Attempts - Euphonia Joseph Faber's Amazing Talking Machine (1830-40's). The Euphonia and other early talking devices are described in detail in a paper by David Lindsay called "Talking Head", Invention & Technology, Summer 1997, 57-63. From About this device, Lindsay writes: It is "... a speech synthesizer variously known as the Euphonia and the Amazing Talking Machine. By pumping air with the bellows ... and manipulating a series of plates, chambers, and other apparatus (including an artificial tongue ... ), the operator could make it speak any European language. A German immigrant named Joseph Faber spent seventeen years perfecting the Euphonia, only to find when he was finished that few people cared."
10 : Historical Attempts - RUR "CHEAP LABOR. ROSSUM'S ROBOTS."  "ROBOTS FOR THE TROPICS.  150 DOLLARS EACH." "EVERYONE SHOULD BUY HIS OWN ROBOT."  "DO YOU WANT TO CHEAPEN YOUR OUTPUT?   ORDER ROSSUM'S ROBOTS"  In 1921, the Czech author Karel Capek produced the play R.U.R. (Rossum's Universal Robots). Some references state that term "robot" was derived from the Czech word robota, meaning "work", while others propose that robota actually means "forced workers" or "slaves." This latter view would certainly fit the point that Capek was trying to make, because his robots eventually rebelled against their creators, ran amok, and tried to wipe out the human race. However, as is usually the case with words, the truth of the matter is a little more convoluted. In the days when Czechoslovakia was a feudal society, "robota" referred to the two or three days of the week that peasants were obliged to leave their own fields to work without remuneration on the lands of noblemen. For a long time after the feudal system had passed away, robota continued to be used to describe work that one wasn't exactly doing voluntarily or for fun, while today's younger Czechs and Slovaks tend to use robota to refer to work that’s boring or uninteresting.
11 : The Roots of Modern Technology 5thc B.C. Aristotelian logic invented 1642 Pascal built an adding machine 1694 Leibnitz reckoning machine
12 : The Roots, continued 1834 Charles Babbage’s Analytical Engine Ada writes of the engine, “The Analytical Engine has no pretensions whatever to originate anything. It can do whatever we know how to order it to perform.” The picture is of a model built in the late 1800s by Babbage’s son from Babbage’s drawings.
13 : The Roots: Logic 1848 George Boole The Calculus of Logic chocolate nuts mint chocolate and ? nuts and mint
14 : Mathematics in the Early 20th Century – (Looking Ahead: Will Logic be the Key to Thinking?) 1900 Hilbert’s program and the effort to formalize mathematics 1931 Kurt Gödel’s paper, On Formally Undecidable Propositions 1936 Alan Turing’s paper, On Computable Numbers with an application to the Entscheidungs problem
15 : The Advent of the Computer 1945 ENIAC The first electronic digital computer
16 : 1949 EDVAC The first stored program computer
17 : 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."
18 : 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
19 : How Will We Recognize AI? 1950 Alan Turing’s paper, Computing Machinery and Intelligence, described what is now called “The Turing Test”. 1990 Loebner Prize established. Grand Prize of $100,000 and a Gold Medal for the first computer whose responses are indistinguishable from a human.
20 : 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."
21 : Was Turing Right? Jabberwacky: Have you been to the Moon? User: No. Few have. Have you? Jabberwacky: Many a time my dear chap! User: What was the most interesting thing you saw? Jabberwacky: Fred. User: Why did you find him interesting? Jabberwacky: It just came into my head. User: What sorts of things do you generally find interesting? Jabberwacky: Nothing but smelly socks. A conversation with Jabberwacky, 2003
22 : Another One A Conversation with Alice, 2004 >What's the best place to eat Chinese food? ALICE: My favorite places are California and Maine. Holland is cool too. >What do you like about Maine? ALICE: The special effects are the best.
23 : Alice, Again >Who invented paper towels? ALICE: The Chinese, long before Europeans. >Who invented pet rocks? ALICE: The Wright Brothers. >Who invented Barbie dolls? ALICE: Thomas Edison.
24 : Were Newell and Simon Right? 1950 Claude Shannon published a paper describing how a computer could play chess. 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.
25 : Chess Today In 1997, Deep Blue beat Gary Kasparov.
26 : Why Did They Get it Wrong? They failed to understand at least three key things: The need for knowledge (lots of it) Scalability and the problem of complexity and exponential growth The need to perceive the world
27 : Scalability Solving hard problems requires search in a large space. To play master-level chess requires searching about 8 ply deep. So about 358 or 2?1012 nodes must be examined.
28 : Exponential Growth
29 : But Chess is Easy The rules are simple enough to fit on one page The branching factor is only 35.
30 : A Harder One John saw a boy and a girl with a red wagon with one blue and one white wheel dragging on the ground under a tree with huge branches.
31 : How Bad is the Ambiguity? Kim (1) Kim and Sue (1) Kim and Sue or Lee (2) Kim and Sue or Lee and Ann (5) Kim and Sue or Lee and Ann or Jon (14) Kim and Sue or Lee and Ann or Jon and Joe (42) Kim and Sue or Lee and Ann or Jon and Joe or Zak (132) Kim and Sue or Lee and Ann or Jon and Joe or Zak and Mel (469) Kim and Sue or Lee and Ann or Jon and Joe or Zak and Mel or Guy (1430) Kim and Sue or Lee and Ann or Jon and Joe or Zak and Mel or Guy and Jan (4862) The number of parses for an expression with n terms is the n’th Catalan number:
32 : Can We Get Around the Search Problem ?
33 : How Much Compute Power Does it Take? From Hans Moravec, Robot Mere Machine to Transcendent Mind 1998.
34 : How Much Compute Power is There? From Hans Moravec, Robot Mere Machine to Transcendent Mind 1998.
35 : 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 Programming languages Cognitive modeling
36 : 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))
37 : 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”
38 : Interest in Subsymbolic AI 40 50 60 70 80 90 00 10
39 : Low-level (Sensory and Motor) Processing and the Resurgence of Subsymbolic Systems Computer vision Motor control Subsymbolic systems perform cognitive tasks Detect credit card fraud The backpropagation algorithm eliminated a formal weakness of earlier systems Neural networks learn.
40 : The Origins of Symbolic AI Games Theorem proving
41 : Games Chess Checkers: 1952-1962 Art Samuel built the first checkers program Chinook became the world checkers champion in 1994 Othello: Logistello beat the world champion in 1997
42 : Games Chess Checkers: Chinook became the world checkers champion in 1994 Othello: Logistello beat the world champion in 1997 Role Playing Games: now we need knowledge Go:
43 : 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 1996 J Moore and others verified the correctness of the AMD5k86 Floating-Point Division algorithm
44 : The Silver Bullet? Is there an “intelligence algorithm”? 1957 GPS (General Problem Solver) Start Goal
45 : 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. Around midnight, two beagles spotted a fire in the house next door. Their barking alerted their owners, who called 911.
46 : Representing Knowledge - Logic McCarthy’s paper, “Programs with Common Sense” at(I, car) ? can (go(home, airport, driving)) at(I, desk) ? can(go(desk, car, walking)) 1965 Resolution theorem proving invented
47 : Representing Knowledge- Semantic Nets 1961
48 : Representing Knowledge – Capturing Experience Representing Experience with Scripts, Frames, and Cases 1977 Scripts Joe went to a restaurant. Joe ordered a hamburger. When the hamburger came, it was burnt to a crisp. Joe stormed out without paying. The restaurant script: Did Joe eat anything?
49 : Representing Knowledge - Rules Expert knowledge in many domains can be captured in rules. From XCON (1982): If: the most current active context is distributing massbus devices, and there is a single-port disk drive that has not been assigned to a massbus, and there are no unassigned dual-port disk drives, and the number of devices that each massbus should support is known, and there is a massbus that has been assigned at least one disk drive that should support additional disk drives, and the type of cable needed to connect the disk drive to the previous device on the massbus is known Then: assign the disk drive to the massbus.
50 : Representing Knowledge – Probabilistically 1975 Mycin attaches probability-like numbers to rules 1970s Probabilistic models of speech recognition 1980s Statistical Machine Translation systems 1990s large scale neural nets If: (1) the stain of the ogranism 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.
51 : The Rise of Expert Systems 1967 Dendral – a rule-based system that infered molecular structure from mass spectral and NMR data 1975 Mycin – a rule-based system to recommend antibiotic therapy 1975 Meta-Dendral learned new rules of mass spectrometry, the first discoveries by a computer to appear in a refereed scientific journal 1979 EMycin – the first expert system shell 1980’s The Age of Expert Systems
52 : Expert Systems – The Heyday 1979 Inference 1980 IntelliCorp 1981 Teknowledge 1983 Carnegie Group 1980 XCON (R1) – first real commercial expert system at DEC, configures VAX systems 1981 Japanese Fifth Generation project launched as the Expert Systems age blossoms in the US. 1984 Gold Hill Common Lisp 1986 neural net start up companies appear 1988 AI revenues peak at $1 billion. AI Winter begins. Selling expert system shells
53 : 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
54 : Expert Systems – Today: Build Your Own eXpertise2Go: AcquiredIntelligence: (whales, graduate school)
55 : Taking the SAT 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.
56 : 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
57 : What About Things that People Do Easily? Common sense Moving Around Language
58 : What About Things that People Do Easily? Common sense CYC UT ( ) WordNet ( Moving around Language
59 : Hand-Coded Knowledge vs. Machine Learning How much work would it be to enter knowledge by hand? Do we even know what to enter? 1952-62 Samuel’s checkers player learned its evaluation function Winston’s system learned structural descriptions from examples and near misses 1984 Probably Approximately Correct learning offers a theoretical foundation mid 80’s The rise of neural networks
60 : Robotics - Tortoise 1950 W. Grey Walter’s light seeking tortoises. In this picture, there are two, each with a light source and a light sensor. Thus they appear to “dance” around each other.
61 : Robotics – Hopkins Beast 1964 Two versions of the Hopkins beast, which used sonar to guide it in the halls. Its goal was to find power outlets.
62 : Robotics - Shakey 1970 Shakey (SRI) was driven by a remote-controlled computer, which formulated plans for moving and acting. It took about half an hour to move Shakey one meter.
63 : Robotics – Stanford Cart 1971-9 Stanford cart. Remote controlled by person or computer. 1971 follow the white line 1975 drive in a straight line by tracking skyline 1979 get through obstacle courses. Cross 30 meters in five hours, getting lost one time out of four
64 : Planning vs. Reacting In the early days: substantial focus on planning (e.g., GPS) 1979 – in “Fast, Cheap and Out of Control”, Rodney Brooks argued for a very different approach. (No, I’m not talking about the 1997 movie.) The Ant, has 17 sensors. They are designed to work in colonies.
65 : Robotics - Dante 1994 Dante II (CMU) explored the Mt. Spurr (Aleutian Range, Alaska) volcano. High-temperature, fumarole gas samples are prized by volcanic science, yet their sampling poses significant challenge. In 1993, eight volcanologists were killed in two separate events while sampling and monitoring volcanoes. Using its tether cable anchored at the crater rim, Dante II is able to descend down sheer crater walls in a rappelling-like manner to gather and analyze high temperature gasses from the crater floor.
66 : Robotics - Sojourner Oct. 30, 1999 Sojourner on Mars. Powered by a 1.9 square foot solar array, Sojourner can negotiate obstacles tilted at a 45 degree angle. It travels at less than half an inch per second.
67 : Robotics – Mars Rover Tutorial on Rover:
68 : Sandstorm March 13, 2004 - A DARPA Grand Challenge: an unmanned offroad race, 142 miles from Barstow to Las Vegas.
69 : Moving Around and Picking Things Up Phil, the drug robot, introduced in 2003
70 : Robotics - Aibo 1999 Sony’s Aibo pet dog
71 : What Can You Do with an Aibo? 1997 – First official Rob-Cup soccer match Picture from 2003 competition
72 : Robotics - Cog 1998 – now Cog Humanoid intelligence requires humanoid interactions with the world.
73 : At the Other End of the Spectrum - Roomba 2001 A robot vacuum cleaner
74 : Natural Language Processing 1964 STUDENT solves algebra word problems The distance from New York to Los Angeles is 3000 miles. If the average speed of a jet plane is 600 miles per hour, find the time it takes to travel from New York to Los Angeles by jet. 1965 ELIZA models a Rogerian therapist young woman: Men are all alike. eliza: In what way? young woman: They're always bugging us about something specific or other. eliza: Can you think of a specific example? young woman: Well, my boyfriend made me come here. eliza: Your boyfriend made you come here?
75 : NLP, continued 1966 Alpac report kills work on MT 1971 SHRDLU
76 : NLP, continued 1973 Schank – a richer limited domain: children’s stories Suzie was invited to Mary’s birthday party. She knew she wanted a new doll so she got it for her. 1977 Schank – scripts add a knowledge layer – restaurant stories 1970’s and 80’s sophisticated grammars and parsers But suppose we want generality? One approach is “shallow” systems that punt the complexities of meaning.
77 : NLP Today Grammar and spelling checkers Spelling: Chatbots See the list at: Speech systems Synthesis: The IBM system:
78 : Machine Translation: An Early NL Application 1949 Warren Weaver’s memo suggesting MT 1966 Alpac report kills government funding Early 70s SYSTRAN develops direct Russian/English system Early 80s knowledge based MT systems Late 80s statistical MT systems
79 : MT Today Austin Police are trying to find the person responsible for robbing a bank in Downtown Austin. El policía de Austin está intentando encontrar a la persona responsable de robar un banco en Austin céntrica. The police of Austin is trying to find the responsible person to rob a bank in centric Austin.
80 : MT Today A Florida teen charged with hiring an undercover policeman to shoot and kill his mother instructed the purported hitman not to damage the family television during the attack, police said on Thursday. Un adolescente de la Florida cargado con emplear a un policía de la cubierta interior para tirar y para matar a su madre mandó a hitman pretendida para no dañar la televisión de la familia durante el ataque, limpia dicho el jueves. An adolescent of Florida loaded with using a police of the inner cover to throw and to kill his mother commanded to hitman tried not to damage the television of the family during the attack, clean said Thursday.
81 : MT Today
82 : Why Is It So Hard? Sue caught the bass with her new rod.
83 : Why Is It So Hard? Sue caught (the bass) (with her new rod).
84 : Why Is It So Hard? Sue caught the bass with the dark stripes.


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