CMU Lecture 1: Intro. Teachers: Emma Brunskill Ariel Procaccia (this time)
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1 CMU Lecture 1: Intr Teachers: Emma Brunskill Ariel Prcaccia (this time)
2 Curse website 2
3 Curse inf TAs: Sam Ganzfried, Danai Kutra, Dana+Yair Mvshvitz-Attias Useful but nt required bk: Russel and Nrvig, Intrductin t Artificial Intelligence, 3 rd editin 6 hmewrks (50%), midterm exam (15%), final exam (35%) 3 pint bnus fr quizzes and participatin 3
4 Curse inf Hmewrk: ttal f 8 late days, at mst 2 per HW Subscribe t mailing list via website Clickers Piazza 4
5 Style Prbably mre mathematical than the average (nntrivial prfs n blackbard) Schedule shws teachers preferences: MAS / game thery / scial chice (me), decisin making under uncertainty (Emma) Brad and Fun (especially this talk) 5
6 AI timeline (NYT 2011) 6
7 Perfrmed fr 84 years Defeated Naplen and Franklin Amazn Mechanical Turk: artificial artificial intelligence 7
8 Amazn Mechanical Turk 8
9 Big questin: can machines think? Mre cncrete questin: can machines d well in the imitatin game? Judge cmmunicates via text channel with cmputer and human, must reliably identify the cmputer 9
10 The Chinese Rm Thught experiment prpsed by Searle Suppse AI has prduced a prgram that can pass the Turing Test in Chinese Yu have a handbk with its pseudcde Yu re in a clsed rm and receive Chinese characters thrugh a slt Yu run the prgram s cde manually and return the utput Des this mean yu understand Chinese? 10
11 Cunterarguments Finding the mind: the whle system understands Chinese, the persn is just a part f the system Redesigning the experiment: suppse the prgram simulates the actins f every neurn in the brain f a Chinese speaker 11
12 Cunterarguments Dubts abut the intuitin Brain perfrms 100 billin peratins per secnd, s it wuld take the persn millins f years t simulate a simple answer Churchland s Luminus Rm: suppse yu are standing in a dark rm and quickly mving a magnet up and dwn, then by Maxwell s thery f artificial luminance it will be luminus. Hwever, this requires 450 trillin mvements per secnd 12
13 Audrey culd recgnize digits spken by a single vice In 1962 IBM demnstrated Shebx, which culd understand 16 wrds Biggest milestne in the Seventies: CMU s Harpy system, which culd understand 1011 wrds vcabulary f three-year-ld 13
14 The birth f AI 1956 wrkshp at Dartmuth Participants included Marvin Minsky, Jhn McCarthy, Claude Shannn, Ray Slmnff, Arthur Samuel, Allen Newell, Herbert Simn Prpsal included this assertin: "every aspect f learning r any ther feature f intelligence can be s precisely described that a machine can be made t simulate it" 14
15 Samuel s prgram was based n alpha-beta pruning Actually nly cmpeted at respectable amateur level By the Nineties checkers prgrams were beating the best human players Checkers was slved by Jnathan Schaeffer in 2007 after 18 years f calculatin 15
16 Shakey = first mbile rbt t visually interpret envirnment Can lcate items, navigate arund them, and reasn abut its actins I (4:08) 16
17 Started as ChipTest at CMU, fllwed by Deep Thught After graduatin, develpers were hired by IBM Defeated Kasparv in 1997 Kasparv played anti-cmputer pening mves t get Deep Blue ut f its pening bk Kasparv accused IBM f cheating 17
18 Advanced Step in Innvative Mbility (resemblance t Asimv is a cincidence) Can recgnize mving bjects, pstures, gestures, its surrunding envirnment, sunds and faces, which enables it t interact with humans DfW4 18
19 DARPA Urban Challenge 96 km urban area curse, t be cmpleted < 6 hurs, tk place in 2007 Tartan Racing (CMU+GM) claimed the $2 millin prize Challenge invlves missin planning, mtin planning, behavir generatin, perceptin, wrld mdeling k0 19
20 Watsn defeated the tw greatest-ever Jepardy! champins Invlves natural language prcessing, infrmatin retrieval, knwledge representatin and reasning, and machine learning _9c 20
21 The future 21
22 The technlgical singularity Emergence f superhuman intelligence Key idea: self-imprvement Surce f name: analgy between inability t predict events after the develpment f a superintelligence, and the spacetime singularity beynd the event hrizn f a black hle Sme predict: this century Others argue: never 22
23 What is AI? Simplest (but self-referential) answer: lk at the call fr papers f the Internatinal Jint Cnference n Artificial Intelligence 23
24 IJCAI 11 call fr papers Search and cnstraints Infrmed search Lcal search Adversarial search Cnstraint satisfactin can be seen as searching thrugh assignments Heuristics 24
25 IJCAI 11 call fr papers Planning (and scheduling) Classical planning Mtin/path planning 25
26 IJCAI 11 call fr papers Uncertainty in AI Bayesian netwrks Graphical mdels Markv decisin prcesses Machine learning Reinfrcement learning Clustering Regressin Cmputatinal learning thery 26
27 IJCAI 11 call fr papers Agent-based and multiagent systems The visin: electrnic cmmerce, supply chains, defense systems managed by autnmus sftware agents Cperatin and crdinatin Emergent behavir Cmputatinal game thery and cmputatinal scial chice 27
28 Can GT enable classic AI? Classic AI seeks t design intelligent rbts/agents GT distills ratinality Ratinality is perceived as intelligence Game-theretic MAS may be perceived as intelligent GT enables AI n the multiagent level! 28
29 Nt cvered Rbtics Visin Natural Language Prcessing Knwledge representatin and reasning Web and knwledge-based infrmatin systems 29
30 Themes Cmplex envirnments representatin Cmplex envirnments large size heuristics Mimic natural wrld / human scieties Synergies between different subdisciplines 30
31 rbcup Rbtic sccer cmpetitin Official gal: By mid-21st century, a team f fully autnmus humanid rbt sccer players shall win the sccer game, cmplying with the fficial rule f the FIFA, against the winner f the mst recent Wrld Cup Cmpnents are similar t autnmus driving, with a multiagent twist (1:40) 31
32 Recmmender systems Step 1: find users with similar ratings Step 2: recmmend item based n their ratings Cmmn mdel fr cllabrative filtering 32
33 Kidney exchange In US, 50,000/yr are diagnsed with lethal kidney disease Cure = transplantatin, but cadaver kidney have a lng waiting list (2-5 yrs) Ptential dnrs may be incmpatible with patient Pairs f incmpatible dnr-patient pairs can smetimes exchange kidneys 33
34 Optimizing kidney exchange Cycle cver = ptimize matched vertices under cycles f length L Prblem is NPhard Regularly slved at CMU
35 Challenges Uncertainty: peple enter and leave the pl, matches fail stchastic ptimizatin Hspitals manipulate by hiding sme f their patients and matching internally game thery / mechanism design 35
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