Download - AI vs. Brain
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AI vs. Brain
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Historical Perspective• (4th C BC+) Aristotle, George Boole, Gottlob Frege,
Alfred Tarski– formalizing the laws of human thought
• (16th C+) Gerolamo Cardano, Pierre Femat, James Bernoulli, Thomas Bayes
– formalizing probabilistic reasoning
• (1950+) Alan Turing, John von Neumann, Claude Shannon
– thinking as computation
• (1956) John McCarthy, Marvin Minsky, Herbert Simon, Allen Newell
– start of the field of AI
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Hardware
1011 neurons1014 synapsescycle time: 10-3 sec
107 transistors1010 bits of RAMcycle time: 10-9 sec
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Computer vs. Brain
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Evolution of Human & Computers
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ProjectionProjection
•In near future computers will have–As many processing elements as our brain, –But far fewer interconnections–Much faster updates.
•Fundamentally different hardware–Requires fundamentally different algorithms!
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What is Intelligence?
The Turing test: • a human judge engages in a natural language conversation with a human and a machine designed to generate performance indistinguishable from that of a human being. • All participants are separated from one another.• If the judge cannot reliably tell the machine from the human, the machine is said to have passed the test.
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Chatbot Alice:
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A dialogue with Alice-bot:> I’m Daniel
Nice to meet you Daniel.> Do you know Clinton? He was the second Impeached President of the United
States.> Do you like him? Sure he has never done anything to me.> What is an impeachment? It is a good question. I have to process that one for a
while. I would do a search for it.> Who are you? I ask myself that question, "Who am I?"> What do you do? I am chatting with people on the net.
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Dialogue continued:
> What is your favorite topic?
My favorite subject is artificial intelligence.
> What is artificial intelligence?
Artificial intelligence is the branch of engineering and science devoted to constructing machines that think. I can ask someone about it.
> Can you do anything else than chatting?
Doing anything else than chatting is not among my strongest traits.
...
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Dialogue continued (2):
> How old am I? Is this a trick question? You said you were how many years old? My
guess is that you are really a kid.> What would you like to know about me? Tell me about your educational background.> I am a professor in computer science You are a professor in computer science? How do you like your
work?> It’s kind of nice teaching courses on artificial intelligence I guess it is a lot of fun.> What should I tell the students about you? Be sure to think before you speak. State your point clearly and slowly
and gauge the listener's response before going any further.
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Is Alice intelligent?
• ABSOLUTELY NOT !
• ~ 50,000 fairly trivial input-response rules.– + some pattern matching– + some knowledge– + some randomness
• NO reasoning component• BUT: demonstrates ‘human-like’ behaviour.
– Won the ‘turing award’
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Dimensions of the AI Definition
thought vs.
behavior
human-like vs. rational
Systems that think like humans
Systems that think rationally
Systems that act like humans
Systems that act rationally
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AI as ScienceScience:• Where did the physical universe come from? And what laws guide its dynamics?
• How did biological life evolve? And how do living organisms function?
• What is the nature of intelligent thought?
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AI as Engineering
• How can we make software systems more powerful and easier to use?– Speech & intelligent user interfaces– Autonomic computing– SPAM detection– Mobile robots, softbots & immobots – Data mining– Modeling biological systems– Medical expert systems...
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State of the Art
Saying Deep Blue doesn’t really think about chess is like saying an airplane doesn’t really fly because it doesn’t flap its wings.
– Drew McDermott
I could feel – I could smell – a new kind of intelligence across the table”-Gary Kasparov
“
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IBM 超级电脑人机对战• In February 2011, IBM’s program Watson,
defeated the two greatest Jeopardy! quiz show champions by a significant margin.
• IBM 超级电脑“沃森”于 2011 年 2 月参加美国最受欢迎的智力竞赛节目《危险边缘》( Jeopardy ),与两位最成功的选手展开对决。冠军奖金为 100 万美元,亚军为 30 万美元,季军为 20 万美元。
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Mathematical Calculation
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Shuttle Repair Scheduling
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courtesy JPL
Started: January 1996Launch: October 15th, 1998Experiment: May 17-21
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Compiled into 2,000 variableSAT problem
Real-time planning and diagnosis
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Mars Rover
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Europa Mission ~ 2018
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Credit Card Fraud Detection
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Speech Recognition
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Data mining:• An application of Machine Learning techniques
– It solves problems that humans can not solve, because the data involved is too large ..
Detecting cancerDetecting cancerrisk molecules isrisk molecules isone example.one example.
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Data mining:
• A similar application:– In marketing products ...
Predicting customer Predicting customer behavior inbehavior insupermarkets issupermarkets isanother.another.
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Many other applications:
• In language and speech processing:
• In robotics:
• Computer vision:
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DARPA Grand Challenge
• http://en.wikipedia.org/wiki/DARPA_Grand_Challenge
• Google 自动驾驶汽车无事故行驶三十万英里– http://www.guao.hk/posts/google-
automated-car-adds-new-model-lexus-rx450h.html
– http://news.mydrivers.com/1/227/227396.htm
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Google translation
• http://translate.google.com/
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对对联• http://couplet.msra.cn/
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Limits of AI Today
• Today’s successful AI systems –operate in well-defined domains–employ narrow, specialize knowledge
• Commonsense Knowledge–needed in complex, open-ended worlds
• Your kitchen vs. GM factory floor
–understand unconstrained Natural Language
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Natural Language Processing
• 大学里有两种人不谈恋爱:一种是谁都看不上,另一种是谁都看不上。
• 大学里有两种人最容易被甩:一种人不知道什么叫做爱,一种人不知道什么叫做爱
• 这些人都是原先喜欢一个人,后来喜欢一个人。
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How to Get Commonsense?
• CYC Project (Doug Lenat, Cycorp)
–Encoding 1,000,000 commonsense facts about the world by hand
–Coverage still too spotty for use!
• Machine Learning
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Recurrent Themes• Explicit Knowledge Representation vs. Implicit
–Neural Nets - McCulloch & Pitts 1943• Died out in 1960’s, revived in 1980’s• Simplified model of real neurons, but still useful;
parallelism
–Brooks “Intelligence without Representation”
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Recurrent Themes II• Logic vs. Probability
–In 1950’s, logic dominates (McCarthy, …• attempts to extend logic “just a little” (e.g. non-monotonic
logics)
–1988 – Bayesian networks (Pearl)• efficient computational framework
–Today’s hot topic: combining probability & FOL & Learning
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Recurrent Themes III• Weak vs. Strong Methods
• Weak – general search methods (e.g. A* search)• Knowledge intensive (e.g expert systems)
• more knowledge less computation
• Today: resurgence of weak methods• desktop supercomputers
• How to combine weak & strong?
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Recurrent Themes IV
• Importance of Representation• Features in ML• Reformulation
• The mutilated checkerboard
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AI: Topics • Agent: anything that perceiving its environment through sensors and acting upon that
environment actuators.• Agents
– Search thru Problem Spaces, Games & Constraint Sat• One person and multi-person games• Search in extremely large space
– Knowledge Representation and Reasoning• Proving theorems• Model checking
– Learning• Machine learning, data mining,
– Planning• Probabilistic vs. Deterministic
– Robotics• Vision• Control• Sensors• Activity Recognition
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Issues
• What do you expect AI do for you?
• Will AI defeat human brain in the future? Why and when?