Artificial Intelligence Ppt Powerpoint Presentation slides Elaine Rich Medical Slideshow Using Artificial Life to evolve Artificial Intelligence

Artificial Intelligence

Rating :

Rate It:

Send This

Download

Comment

Favourite

more

Post To:

Facebook

Twitter

Digg

Delicious

more share options

poojitha on Jan 28, 2010 Says :

excellent,, could u mail this on pooji.sandra@gmail.com,, ill b very thank ful to u..

hema on Sep 13, 2009 Says :

send this to my id

hema on Sep 13, 2009 Says :

send this slides to my mail

gnanesh on Aug 05, 2009 Says :

its cool

Post a comment

Post Comment on Twitter Post Comment on SlideWorld

Comments:

Subscribe to follow-up comments

1 Favorites

gnanesh, favourited this 6 Months ago.

Notes

Show Notes

Slide 1 :

Artificial Intelligence Our Attempt to Build Models of Ourselves Elaine Rich

Slide 2 :

One Vision of an AI

Slide 3 :

A Calmer Vision

Slide 4 :

Could AI Stop This?

Slide 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.

Slide 6 :

Or, Stepping Back Even Farther, Can We Build Artificial People? Historical attempts The modern quest for robots and intelligent agents Us vs. Them

Slide 7 :

Historical Attempts - Frankenstein Frankenstein creates the fiend - illustration byBernie Wrightson (© 1977) The original story, published by Mary Shelley, in 1818, describes the attempt of a true scientist, Victor Frankenstein, to create life. http://members.aon.at/frankenstein/frankenstein-novel.htm

Slide 8 :

Historical Attempts – The Turk http://www.theturkbook.com

Slide 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 http://www.haskins.yale.edu/haskins/HEADS/SIMULACRA/euphonia.html 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."

Slide 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). http://www.maxmon.com/1921ad.htm 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.

Slide 11 :

The Roots of Modern Technology 5thc B.C. Aristotelian logic invented 1642 Pascal built an adding machine 1694 Leibnitz reckoning machine

Slide 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.

Slide 13 :

The Roots: Logic 1848 George Boole The Calculus of Logic chocolate nuts mint chocolate and ? nuts and mint

Slide 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

Slide 15 :

The Advent of the Computer 1945 ENIAC The first electronic digital computer

Slide 16 :

1949 EDVAC The first stored program computer

Slide 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."

Slide 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

Slide 19 :

How Will We Recognize AI? 1950 Alan Turing’s paper, Computing Machinery and Intelligence, described what is now called “The Turing Test”. http://www.abelard.org/turpap/turpap.htm 1990 Loebner Prize established. Grand Prize of $100,000 and a Gold Medal for the first computer whose responses are indistinguishable from a human.

Slide 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."

Slide 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

Slide 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. http://www.alicebot.org/

Slide 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.

Slide 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.

Slide 25 :

Chess Today In 1997, Deep Blue beat Gary Kasparov.

Slide 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

Slide 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.

Slide 28 :

Exponential Growth

Slide 29 :

But Chess is Easy The rules are simple enough to fit on one page The branching factor is only 35.

Slide 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.

Slide 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:

Slide 32 :

Can We Get Around the Search Problem ?

Slide 33 :

How Much Compute Power Does it Take? From Hans Moravec, Robot Mere Machine to Transcendent Mind 1998.

Slide 34 :

How Much Compute Power is There? From Hans Moravec, Robot Mere Machine to Transcendent Mind 1998.

Slide 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

Slide 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))

Slide 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”

Slide 38 :

Interest in Subsymbolic AI 40 50 60 70 80 90 00 10

Slide 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.

Slide 40 :

The Origins of Symbolic AI Games Theorem proving

Slide 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

Slide 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:

Slide 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

Slide 44 :

The Silver Bullet? Is there an “intelligence algorithm”? 1957 GPS (General Problem Solver) Start Goal

Slide 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.

Slide 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

Slide 47 :

Representing Knowledge- Semantic Nets 1961

Slide 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?

Slide 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.

Slide 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.

Slide 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

Slide 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

Slide 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

Slide 54 :

Expert Systems – Today: Build Your Own eXpertise2Go: http://www.expertise2go.com/ AcquiredIntelligence: http://www.aiinc.ca/demos/ (whales, graduate school)

Slide 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.

Slide 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

Slide 57 :

What About Things that People Do Easily? Common sense Moving Around Language

Slide 58 :

What About Things that People Do Easily? Common sense CYC UT (http://www.cs.utexas.edu/users/mfkb/RKF/tree/ ) WordNet (http://www.cogsci.princeton.edu/~wn/) Moving around Language

Slide 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

Slide 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.

Slide 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.

Slide 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.

Slide 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

Slide 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.) http://www.ai.mit.edu/people/brooks/papers/fast-cheap.pdf http://www.ai.mit.edu/projects/ants/ The Ant, has 17 sensors. They are designed to work in colonies.

Slide 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.

Slide 66 :

Robotics - Sojourner http://antwrp.gsfc.nasa.gov/apod/ap991030.html 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.

Slide 67 :

Robotics – Mars Rover Tutorial on Rover: http://marsrovers.jpl.nasa.gov/gallery/video/animation.html

Slide 68 :

Sandstorm March 13, 2004 - A DARPA Grand Challenge: an unmanned offroad race, 142 miles from Barstow to Las Vegas.

Slide 69 :

Moving Around and Picking Things Up Phil, the drug robot, introduced in 2003

Slide 70 :

Robotics - Aibo 1999 Sony’s Aibo pet dog

Slide 71 :

What Can You Do with an Aibo? 1997 – First official Rob-Cup soccer match Picture from 2003 competition

Slide 72 :

Robotics - Cog http://www.eecs.mit.edu/100th/images/Brooks-Cog-Kismet.html 1998 – now Cog Humanoid intelligence requires humanoid interactions with the world.

Slide 73 :

At the Other End of the Spectrum - Roomba 2001 A robot vacuum cleaner

Slide 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?

Slide 75 :

NLP, continued 1966 Alpac report kills work on MT 1971 SHRDLU

Slide 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.

Slide 77 :

NLP Today Grammar and spelling checkers Spelling: http://www.spellcheck.net/ Chatbots See the list at: http://www.aaai.org/AITopics/html/natlang.html#chat/ Speech systems Synthesis: The IBM system: http://www.research.ibm.com/tts/coredemo.html

Slide 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

Slide 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.

Slide 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.

Slide 81 :

MT Today http://www.shtick.org/Translation/translation47.htm

Slide 82 :

Why Is It So Hard? Sue caught the bass with her new rod.

Slide 83 :

Why Is It So Hard? Sue caught (the bass) (with her new rod).

Slide 84 :

Why Is It So Hard? Sue caught the bass with the dark stripes.

Slide 85 :

Why Is It So Hard? Sue caught (the bass with the dark stripes).

Slide 86 :

Why Is It So Hard? Sue played the bass with her new bow.

Slide 87 :

Why Is It So Hard? Sue played the bass with her new bow. Sue played the bass with her new beau.

Slide 88 :

Why Is It So Hard? Olive oil

Slide 89 :

Why Is It So Hard? Olive oil

Slide 90 :

Why Is It So Hard? Peanut oil

Slide 91 :

Why Is It So Hard? Coconut oil

Slide 92 :

Why Is It So Hard? Baby oil

Slide 93 :

Why Is It So Hard? Cooking oil

Slide 94 :

MT Today Is MT an “AI complete” problem? John saw a bicycle in the store window. He wanted it. John saw a bicycle in the store window. He pressed his nose up against it. John saw the Statue of Liberty flying over New York. John saw a plane flying over New York.

Slide 95 :

Text Retrieval and Extraction Try Ask Jeeves: http://www.askjeeves.com To do better requires: Linguistic knowledge World knowledge Newsblaster: http://www1.cs.columbia.edu/nlp/newsblaster/

Slide 96 :

Programming Languages 1958 Lisp – a functional programming language with a simple syntax. 1972 PROLOG - a logic programming language whose primary control structure is depth-first search ancestor(A,B) :- parent(A,B) ancestor(A,B) :- parent(A,P), ancestor(P,B) 1988 CLOS (Common Lisp Object Standard) published. Draws on ideas from Smalltalk and semantic nets (successor SitA ActionP)

Slide 97 :

Cognitive Modeling Symbolic Modeling 1957 GPS 1983 SOAR Neuron-Level Modeling McCulloch Pitts neurons: all or none response More sophisticated neurons and connections More powerful learning algorithm

Slide 98 :

Making Money – Software Expert systems to solve problems in particular domains Expert system shells to make it cheaper to build new systems in new domains Language applications Text retrieval Machine Translation Text to speech and speech recognition Data mining

Slide 99 :

Making Money - Hardware 1980 Symbolics founded 1986 Thinking Machines introduces the Connection Machine 1993 Symbolics declared bankruptcy Symbolics 3620 System c 1986: Up to 4 Mwords (16 Mbytes) optional physical memory, one 190 Mbyte fixed disk, integral Ethernet interface, five backplane expansion slots, options include an additional 190 Mbyte disk or 1/4'' tape drive, floating point accelerator, memory, RS232C ports and printers.

Slide 100 :

Making Money - Robots 1962 Unimation, first industrial robot company, founded. Sold a die casting robot to GM. 1990 iRobot founded, a spinoff of MIT 2000 The UN estimated that there are 742,500 industrial robots in use worldwide. More than half of these were being used in Japan. 2001 iRobot markets Roomba for $200.

Slide 101 :

The Differences Between Us and Them Emotions Understanding Consciousness

Slide 102 :

Emotions The robot Kismet shows emotions sad surprise http://www.ai.mit.edu/projects/humanoid-robotics-group/kismet/

Slide 103 :

Understanding Searle’s Chinese Room

Slide 104 :

Consciousness Me You

Slide 105 :

Today: The Difference Between Us and Them

Slide 106 :

Today: Computer as Artist Two paintings done by Harold Cohen’s Aaron program:

Slide 107 :

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

	Medslides_PPT 1 Years ago. Category:

Tags:

Embed:
7682 Views, 2 favourite
PowerPoint Slide Presentation on

SWINE Flu

Why do women live longer then men? Humor

History of Medicine

Yoga and Medicine

How to be a Successful Interventional Fellow size 50 MB

Cardiac Pathology

Hypertension: New Concepts, Guidelines, and Clinical Management

Diabetes Pathology

Medical Powerpoint Templates