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Introduction
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
Hardware
1011 neurons1014 synapsescycle time: 10-3 sec
107 transistors1010 bits of RAMcycle time: 10-9 sec
Computer vs. Brain
Evolution of Human & Computers
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!
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.
Chatbot Alice:
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.
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.
...
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.
Is Alice intelligent?
• ABSOLUTELY NOT !
• ~ 50000 fairly trivial input-response rules.– + some pattern matching– + some knowledge– + some randomness
• NO reasoning component• BUT: demonstrates ‘human-like’ behaviour.
– Won the ‘turing award’
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
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?
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...
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
“
IBM 超级电脑人机对战• 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。
Mathematical Calculation
Shuttle Repair Scheduling
courtesy JPL
Started: January 1996Launch: October 15th, 1998Experiment: May 17-21
Compiled into 2,000 variableSAT problem
Real-time planning and diagnosis
Mars Rover
Europa Mission ~ 2018
Credit Card Fraud Detection
Speech Recognition
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.
Data mining:
• A similar application:– In marketing products ...
Predicting customer Predicting customer behavior inbehavior insupermarkets issupermarkets isanother.another.
Many other applications:
• In language and speech processing:
• In robotics:
• Computer vision:
DARPA Grand Challenge
• http://en.wikipedia.org/wiki/DARPA_Grand_Challenge
• Google 自动驾驶汽车全揭露 人工智能霸占车辆？– http://www.evolife.cn/html/2010/56330.html
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
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
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”
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
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?
Recurrent Themes IV
• Importance of Representation• Features in ML• Reformulation
• The mutilated checkerboard
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
Intelligent Agents• Have sensors, effectors
• Implement mapping from percept sequence to actions
Environment Agent
percepts
actions
• Performance Measure
Implementing ideal rational agent
• Agent program– Simple reflex agents
– Agents with memory• Reflex agent with internal state• Goal-based agents• Utility-based agents
Simple reflex agentsE
NV
IRO
NM
EN
T
AGENT
Effectors
Sensors
what world islike now
Condition/Action ruleswhat action should I do now?
Reflex agent with internal state
EN
VIR
ON
ME
NT
AGENT Effectors
Sensors
what world islike now
Condition/Action ruleswhat action should I do now?
What world was like
How world evolves
Goal-based agentsE
NV
IRO
NM
EN
T
AGENT Effectors
Sensors
what world islike now
Goalswhat action should I do now?
What world was like
How world evolves
what it’ll be likeif I do acts A1-An
What my actions do
Utility-based agentsE
NV
IRO
NM
EN
T
AGENT Effectors
Sensors
what world islike now
Utility functionwhat action should I do now?
What world was like
How world evolves
What my actions do
How happy would I be?
what it’ll be likeif I do acts A1-An
Properties of Environments
• Observability: full vs. partial vs. non
• Deterministic vs. stochastic
• Episodic vs. sequential
• Static vs. … vs. dynamic
• Discrete vs. continuous
• Travel agent
• WWW shopping agent
• Coffee delivery mobile robot
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