Post on 06-May-2015
Introduction ArtificialIntroduction Artificial IntelligenceIntelligence
Lecture 1
Albert Orriols i Puightt // lb t i l thttp://www.albertorriols.net
aorriols@salle.url.edu
Artificial Intelligence – Machine Learningg gEnginyeria i Arquitectura La Salle
Universitat Ramon Llull
Today’s Agenda
Brainstorming from your “postits”g y pSome DefinitionsPrehistory and History of AIWhere are we headed?
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BrainstormingWhat’s AI?
A
AA
…
Do you know of some real-world applications?Do you know of some real-world applications?A
A
…
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What’s Intelligence?Intelligence (dictionary)g ( y)
capacity for learning, reasoning, understanding, and similar forms of mental activity; aptitude in grasping truths, o s o e a ac y; ap ude g asp g u s,relationships, facts, meanings, etc.
In particular, we could say:pa cu a , e cou d sayAbility to act as human beings
Solve problemsThink rationally
Artificial intelligence … Building a machine that is (or seems to be at the eyes of the beholder) intelligent
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Can You Be More Formal?What is artificial intelligence? g
It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.o co e se o e ods a a e b o og ca y obse ab e
Yes, but what is intelligence? I t lli i th t ti l t f th bilit t hi l iIntelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines.
Isn't there a solid definition of intelligence that doesn't depend on relating it to human intelligence?
Not yet. The problem is that we cannot yet characterize in general what kinds of computational procedures we want to call intelligent. We
d t d f th h i f i t lli d t th
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understand some of the mechanisms of intelligence and not others. See the complete interview at: http://www-formal.stanford.edu/jmc/whatisai/node1.html
Artificial Intelligence Machine Learning
What’s Involved in Intelligence?Ability to interact with the real world
to perceive, understand, and acte.g., speech recognition and understanding
Searching the best solution
Reasoning and PlanningReasoning and Planningmodeling the external world, given input
solving new problems, planning, and making decisions
ability to deal with unexpected problems, uncertainties
Learning and Adaptationwe are continuously learning and adaptingy g p g
our internal models are always being “updated”e.g., a baby learning to categorize and recognize animals
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g , y g g g
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AI Is Not Alone at HomeCrossbreeding of a lot of fieldsg
Philosophy Logic, methods of reasoning, mind as physical system, foundations of learning language rationalityfoundations of learning, language, rationality.
Mathematics Formal representation and proof, algorithms, computation, (un)decidability, (in)tractability
Statistics Modeling uncertainty, learning from data
Economics Utility, decision theory, rational economic agents
Neuroscience Neurons as information processing units
Psychology / NeuroScience
How do people behave, perceive, process cognitive information represent knowledgeScience information, represent knowledge
Computer Engineering Building fast computers
Control Theory Design systems that maximize an objective function y g y jover time
Linguistics Knowledge representation, grammars
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Prehistory of AIThrough history, people though of mythic “artificial” g y, p p g yrobots
golden robots of Hephaestus and Pygmalion's Galateagolden robots of Hephaestus and Pygmalion s Galatea
alchemical means of placing mind into matter
More specific, tangible advances5th century B.C.
Aristotle invented syllogistic logic, the first formal deductive reasoning system.
13th century.Talking heads were said to have been created (Roger Bacon
d Alb t th G t)and Albert the Great).Ramon Lull, Spanish theologian, invented machines for discovering nonmathematical truths through combinatory
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discovering nonmathematical truths through combinatory.
Artificial Intelligence Machine Learning
Prehistory of AIMore specific, tangible advances (cont.)p , g ( )
15th centuryInvention of printing using moveable type Gutenberg BibleInvention of printing using moveable type. Gutenberg Bible printed (1456).
15th-16th century15th 16th centuryClocks, the first modern measuring machines, were first produced using lathes.
16th centuryClockmakers extended their craft to creating mechanicalClockmakers extended their craft to creating mechanical animals and other novelties.
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Prehistory of AIMore specific, tangible advances (cont.)p , g ( )
17th century - The revolution of thinking about thinkingDescartes proposed that bodies of animals are nothingDescartes proposed that bodies of animals are nothing more than complex machines (strong AI). Variations and elaborations of Cartesian mechanism.
Hobbes published The Leviathan, containing a material and combinatorial theory of thinking.Pascal created the first mechanical digital calculating machine (1642).
Leibniz improved Pascal's machine to do multiplication & division (1673) and envisioned a universal calculus of reasoning by which
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( ) g yarguments could be decided mechanically.
Artificial Intelligence Machine Learning
Prehistory of AIMore specific, tangible advances (cont.)p , g ( )
18th century – Mechanical toys
Vaucanson’s Duck Von Kempelen’s phony mechanical chess player
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Prehistory of AIMore specific, tangible advances (cont.)p , g ( )
19th century – Frankenstein’s birthGeorge Boole developed a binary algebra representing (some)George Boole developed a binary algebra representing (some) "laws of thought," published in The Laws of Thought.Charles Babbage and Ada Byron (Lady Lovelace) worked on g y ( y )programmable mechanical calculating machines.
Mary Shelley published the story of Frankenstein's monster (1818).Crossing the century bridgeCrossing the century bridge
Behaviorism was expounded by psychologist Edward Lee Thorndike in
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"Animal Intelligence."
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Pre-birth of AIBeginning of the 20th centuryg g yRussell and Whitehead published Principia Mathematica.
Capek's play “Rossum's Universal Robots” produced in 1921 (LondonCapek s play Rossum s Universal Robots produced in 1921 (London opening, 1923). First use of the word 'robot' in English.
McCulloch and Pitts publish "A Logical Calculus of the Ideas Immanent inMcCulloch and Pitts publish A Logical Calculus of the Ideas Immanent in Nervous Activity" (1943), laying foundations for neural networks.
Rosenblueth, Wiener and Bigelow coin the term cybernetics (1943).
Bush published As We May Think (1945) a prescient vision of the future in which computers assist humans in many activities.
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The 3 Key IngredientsThe first key ingredient: The computer and the programy g p p g
ENIAC (1945). The first electronic digital computer
EDVAC (1949) Th fi t t d tEDVAC (1949). The first stored program computer
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The 3 Key IngredientsThe second key ingredient: The TURING TEST.y g
(Human) judge communicates with a human and a machine over text-only channel.o e e o y c a e
Both human and machine try to act like a human
J d t i t t ll hi h i hi hJudge tries to tell which is which.
Numerous variants.
Loebner prize.
Current programs nowhere close Cu e t p og a s o e e c oseto passing this
http://www.jabberwacky.com/http://turingtrade.org/
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The Turing TestMore on Turing testg
Objective: The machine needs to fool the machine[INT] I heard that a striped rhinoceros flow on the[INT] I heard that a striped rhinoceros flow on the Mississippi in a pink balloon this morning. What do you think about?[COMP] That sound rather ridiculous to me[COMP] That sound rather ridiculous to me[INT] Really? My uncle did this one... Why this sound ridiculous?[COMP] Option 1: Rhinoceros don't have stripes[COMP] Option 1: Rhinoceros don t have stripes[COMP] Option 2: Rhinoceros can't fly
Tr to change ON for UNDER the Mississipi
Is this unfair for the computer?
Try to change ON for UNDER the Mississipi
[INT] What’s the result of 324 x 678?[COMP] This is too difficult. I’m not a calculator!
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Needs to seem more foolish than it actually is (has to lie!)
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The 3 Key IngredientsThe third key ingredient: THE DARMONT CONFERENCE. y gPeople working on building intelligent machines.
J. McCarthy, M. L. Minsky, N. Rochester, and C.E. Shannon August 31 1955 "We propose that a 2 monthShannon. 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 induring 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
i l t it "simulate it."
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Brief History of AIThe Golden years (1956 – 1974)y ( )
‘1960s Strong funding of AI centersStrong funding of AI centersBuilding intelligent automataSearching in complex search spacesSearching in complex search spaces
First AI programs that workS l’ h k ( hi h l )Samuel’s checker program (which learns)Newell and Simon’s Logic TheoristG l t ’ t iGelernter’s geometry engineRobinson’s complete algorithm for logical reasoning
First programming languages for AIMcCarthy - Lisp (1958)
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Brief History of AIThe Golden years (1956 – 1974)y ( )
And the first chatterbots:ELIZA (1966)ELIZA (1966).
It carried out very realistic conversations. It searched for key words in the conversation and asked yinformation about that
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Brief History of AIThe Winter: After expansion, there’s always a contractionp , y
First doubts on the feasibility of all the approach
P blProblems:Limited computer power C bi t i l l i ( ti l ti )Combinatorial explosion (exponential time)Commonsense knowledge and reasoningM ’ dMoravec’s paradoxThe Chinese room argument undermined the goal of building intelligent machinesintelligent machinesEND OF FUNDING
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Brief History of AIThe Chinese room argument (Searle, 1980)g ( , )
Person who knows English but not Chinese sits in roomC ese s s oo
Receives notes in Chinese
H t ti E li h l b k fHas systematic English rule book for how to write new Chinese characters based on input Chinese characters, returns his notesbased o put C ese c a acte s, etu s s otes
Person=CPU, rule book=AI program, really also need lots of paper (storage)
Has no understanding of what they meanBut from the outside, the room gives perfectly reasonable
i Chi !answers in Chinese!
Searle’s argument: the room has no intelligence in it!
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Brief History of AIBut in parallel… expert systems rise and growp p y g
MYCIN(1972): Diagnosed infection blood diseasesDiagnosed infection blood diseases. It had a set of about 600 rules and started to ask questions.In some cases better than human expertsIn some cases, better than human experts.
XCON (1980): P d ti l b d t th t i t d th d i fProduction-rule-based system that assisted the ordering of a type of computers systems by automatically selecting the computer systems components based on the customers requirements.Saving $40 million dollars to the company. 2500 rules and processed 80000 orders with 95%-98% accuracy. The gain in money was because it reduced the need to give free components when the technicians made errors by speeding
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components when the technicians made errors, by speeding the assembly process and by increasing customer satisfaction
Artificial Intelligence Machine Learning
Brief History of AIBut in parallel… expert systems rise and growp p y g
PROSPECTOR (1981) A computer-based consultation system for mineralA computer-based consultation system for mineral exploration. Recommending exploratory drillingg p y g
And many others. Search the web for more!
New funding due to this successNew funding due to this successAI groups were formed in many large companies to develop
t texpert systems.
1986 sales of AI-based hardware and software were $425 illimillion.
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Brief History of AIQuick pace in the ‘90sQ p
NCSA releases the first web browser, Mosaic
D Bl b t G KDeep Blue beats Gary Kasparov
Robotic soccer players in RoboCup
Sony corporation introduced the robotic dog AIBO
Remote agent autonomously drive deep space 1e ote age t auto o ous y d e deep space
Even moving faster in the 00’siRobot introduces the vacuum cleaning robot Roomba
DARPA grand challenge (we’ll see it in a minute)A Touareg R5 won the challenge
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Some Cool ApplicationsThree cool applications among hundredspp g
Deep Blue
DARPA G d Ch llDARPA Grand Challenge
Robotics Cog
Loebner Prize
Roombaoo ba
Rob-Cup
ASIMOASIMO
Data miningStock MarketMedical Diagnosis
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Deep Blue
Origins at CMU
It was a massively parallel, RS/6000 SP Thin P2SC-based system with 30-nodes
Deep Blue took Gary Kasparovto the cleaners
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DARPA Grand ChallengeGrand Challenge
Cash prizes ($1 to $2 million) offered to first robots to complete a long course completely unassistedStimulates research in vision robotics planning machineStimulates research in vision, robotics, planning, machine learning, reasoning, etc
2004 Grand Challenge:2004 Grand Challenge: 150 mile route in Nevada desertFurthest any robot went was about 7 milesFurthest any robot went was about 7 miles … but hardest terrain was at the beginning of the course
2005 G d Ch ll2005 Grand Challenge:132 mile raceN t l i di t i tNarrow tunnels, winding mountain passes, etcStanford 1st, CMU 2nd, both finished in about 6 hours
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DARPA Grand Challengehttp://cs.stanford.edu/group/roadrunner/p g p
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DARPA Grand ChallengeThe challenge: a driverless car competes for wining the g p grace
150 mile off-road robot race across the Mojave desertNatural and manmade hazardsN d i t t l
150 mile off-road robot race across the Mojave desertNatural and manmade hazardsN d i t t lNo driver, no remote controlNo dynamic passingFastest vehicle wins the race (and 2 million dollar prize)
No driver, no remote controlNo dynamic passingFastest vehicle wins the race (and 2 million dollar prize)
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(and 2 million dollar prize)(and 2 million dollar prize)
Robotics - CogHumanoid intelligence requires humanoid interactions g qwith the world
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Loebner PrizePrizes the chatterbots considered to be the most human-like
Th t t b i 1990The contest begun in 1990
$25,000 is offered for the first chatterbot that judges cannot j gdistinguish from a real human and that can convince judges that the human is the computer program
$100,000 is the reward for the first chatterbot thatfor the first chatterbot that judges cannot distinguish from a real human in a Turing test that includesTuring test that includes deciphering and understanding text, visual, and auditory inputand auditory input
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RobCupFirst official Rob-Cup soccer match (1997)p ( )
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ASIMO
Advanced Step in InnovativeMobilityy
Able ofMovingMovingInteracting with human beingsHelp peopleHelp people
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Data Mining ExplosionData mining: Extract novel, useful, and interesting g , , ginformation from data
Why so a big deal?Companies are generating lots of data about the business
They want to process these data and obtain useful information
Wh no not before?Why now, not before?Computers have a lot of power nowadays
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Modeling the Stock MarketModeling market tradersg
LETS project: Evolving artificial traders for successful market trading (Sonia Schulenburg et al, 2007)ad g (So a Sc u e bu g et a , 00 )
Evolutionary economics:Evolutionary economics:Create trend followersand value investors
Let them interact
Evolve a population ofstrategies
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Medical DiagnosisData mining
An important application domain of artificial intelligence
John H. HolmesEpidemiologic study by means of LCSsHidden relationships among variables discovered by LCSs
Xavier Llorà et al.Better than Human Capability in Diagnosing Prostate Cancer Using Infrared SpectroscopicProstate Cancer Using Infrared Spectroscopic imaging
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But… Slow it down!
There are no castles in the sky
All these applications rely on:Search & Optimization
Knowledge representation
LearningLearning
Planning
These are the four topics that we’ll see in this course. And we will start for the beginning
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Detailed Outline AI12. Solving search problems
1. Introduction to search problems
2. Blind search
3. Informed/heuristic search
4. Adversary search (first project)4. Adversary search (first project)
5. Constraint satisfaction problems
3 Knowledge representation3. Knowledge representation
1. Introduction to knowledge representation
2 Knowledge representation based on logics2. Knowledge representation based on logics
3. Knowledge and uncertainty
F L i4. Fuzzy Logics
4. Lisp
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Detailed Outline AI25. Machine learning
1. Introduction to machine learning
2. Supervised learning
1. Decision trees, Instance-based learning, Bayesian decision theory, Support vector machines and Neural networks
3 Unsupervised learning – association rules3. Unsupervised learning association rules
4. Unsupervised learning – clustering
5. Reinforcement learning g
6. New challenges in data mining
6. Planning
1. Introduction to planningp g
2. STRIPS
3. Search through the state world
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g
4. Search through the plan world
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