Artificial Intelligence: Past, Present, and Future

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1 CSEP 573 Fial Lecture Artificial Itelligece: Past Preset ad Future CSE AI faculty

2 Pla for Today Part I AI History ad Review Select Applicatios The Future: where do we go from here? Break (studet evals) Part II Emergig Area i AI: Brai Computer Iterfaces Guest lecture by Dr. Reihold Scherer 2

3 A log time ago i a galaxy far away August DARTMOUTH SUMMER RESEARCH PROJECT ON ARTIFICIAL INTELLIGENCE J. McCarthy Dartmouth College M. L. Misky Harvard Uiversity N. Rochester I.B.M. Corporatio C.E. Shao Bell Telephoe Laboratories We propose that a 2 moth 0 ma study of artificial itelligece A attempt will be made to fid how to make machies use laguage form abstractios ad cocepts solve kids of problems ow reserved for humas ad improve themselves. We thik that a sigificat advace ca be made i oe or more of these problems if a carefully selected group of scietists work o it together for a summer. 3

Shao Bell Telephoe Laboratories We propose that a 2 moth 0 ma study of artificial itelligece A attempt will be made to fid how to make machies use

4 Samuel s Checkers program (959) First self-learig AI program Used search tree of board positios reachable from curret positio 4

5 Flashback: Search Uiformed Search DFS ad BFS Iterative Deepeig Bidirectioal Search Iformed Search Best first search: Greedy A* admissible heuristics Local Search Hill Climbig Simulated Aealig Geetic Algorithms 5

Best first search: Greedy A* admissible heuristics

6 Flashback: Adversarial Search Miimax Search Alpha Beta Pruig Trucated search ad evaluatio fuctios 6

7 Samuel s Checkers program (959) First use of miimax search First use of alpha-beta pruig First use of trucated search ad evaluatio fuctios First use of machie learig Implemeted o a IBM 70 with 9 KB memory! IBM s stock wet up 5 poits after demo 7

evaluatio fuctios First use of machie learig Implemeted o a

8 96: First Idustrial Robot Uimate by Uimatio Worked o a Geeral Motors assembly lie Trasported die castigs from assembly lie ad welded these oto auto bodies Bega the era of idustrial robots 8

9 Flashback: Robots today Ivited Talks by: Dieter Fox (Probabilistic localiatio i robots) Rawichote Chalodhor (Robot programmig by huma demostratio) 9

10 0 Math Flashback: Recursive Bayesia Updatig ) ( ) ( ) ( ) ( = P x P x P x P K K K K Markov assumptio: is idepedet of... - if we kow x. ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( x P x P x P x x P x P P x P x P x P i i x = = = = α α K K K K Previous estimate Dyamics New data ormalie

11 Applicatio: Robot Localiatio ad Mappig of Alle Ceter (Work of Prof. Dieter Fox ad studets)

12 97: Daw of Classical Symbolic AI Blocks world model Itroduced by Terry Wiograd World is modeled as a set of abstract symbols which may be reasoed about usig logic 2

13 Flashback: Logical Reasoig Propositioal logic Models ad Etailmet Iferece techiques: Soudess completeess Resolutio Forward/backward chaiig WalkSAT First-Order Logic Variables Quatifiers Iferece techiques: Skolemiatio & Uificatio Forward/backward chaiig Resolutio 3

chaiig WalkSAT First-Order Logic Variables Quatifiers Iferece

14 980s: Neural Networks Early eural etworks McCulloch & Pitts (943) simple eural ets Roseblatt (962) perceptro Backpropagatio learig algorithm Iveted i 969 ad agai i 974 Hardware too slow util rediscovered i 985 4

perceptro Backpropagatio learig algorithm Iveted i

15 Flashback: Neural Networks v m i = g ( W jix j j ) x m j = g( k w kj u m k ) E( W w) = ( d i vi 2 w kj de dw kj = w kj i de ε dw m i ( d m i kj 2 ) v m i ) g ( j W ji x m j ) W m u k Backprop rule for iput-hidde weights w: ji g ( k w kj u m k ) u m k 5

16 Applicatio: Hadwritig Recogitio Demo 6

17 990s to preset: Probabilistic Models ad Machie Learig Probabilistic graphical models Pearl (988) Bayesia etworks Machie learig Quila (993) decisio trees (C4.5) Vapik (992) Support vector machies (SVMs) Schapire (996) Boostig Neal (996) Gaussia processes Recet progress: Probabilistic relatioal models deep etworks active learig structured predictio etc. 7

5) Vapik (992) Support vector machies (SVMs) Schapire (996) Boostig Neal (996) Gaussia

18 Flashback: Bayesia Networks 8

19 Applicatio: Trackig a Perso usig GPS Trackig Goal Predictio Aomaly Detectio foot=blue bus=gree car=red (Work of Prof. Fox Prof. Kaut ad studets) 9

20 Applicatio: Robot Learig by Imitatio 20

21 Imitatig from Motio Capture Data Motio Capture Data from Motio Capture Attempted Imitatio 2

22 Bayesia Network for Stable Imitatio ad Learig Idea: Use Bayesia etwork to capture cosequeces of actios (curret body state actio) Next body state State s = [joit agles gyro values foot pressure values] Actio a = [positio commads to motors for each joit] Ifer actios a t give evidece s s T from teacher subject to stability costraits o gyro readigs 22

23 Learig to Imitate a Huma Actio 23

24 Result after Learig Huma Actio Imitatio 24

25 The Future of AI Massive amouts of data + Sophisticated probabilistic reasoig ad machie learig algorithms + Massive computig power = AI revolutio? 25

26 Automated Drivig Wiers of the 2005 ad 2007 DARPA Grad Challeges Driverless pod cars at Heathrow Iteratioal Airport 26

27 AI i a Sesor-rich World Itelliget houses Itelliget refrigerators Itelliget forests Itelliget oceas Itelliget bridges Etc. 27

28 AI i Idustry Joseph Sirosh s talk: Fraud detectio trust ad safety Just-i-time ivetory systems Collaborative filterig Recommedatio i social etworks Behavioral ad targetig Other applicatios Stock market predictio Isider tradig ad market abuse detectio AI-assisted desig Itelliget robots for maufacturig ad testig 28

29 Other future AI applicatios Smart power grids: electric power flows both ways ad is distributed dyamically accordig to chagig demad Security ad military: Bomb diffusig robots umaed vehicles soldier robots Robot firefighters AI Travel Agets AI Accoutats AI Cashiers AI Football Coaches AI Football Players 29

30 Flashback: Machie Learig Classificatio Decisio Trees Neural Networks SVMs 30

31 Applicatio: Brai-Computer Iterfaces Classifyig brai sigals recorded at the scalp Detect what a perso wats from a set of optios Commad a humaoid robot to fetch a object Details i Dr. Scherer s talk CBS News Suday Morig 3

32 Thak you for your attetio! 0 miute break

AdaLab. Adaptive Automated Scientific Laboratory (AdaLab) Adaptive Machines in Complex Environments. n Start Date: 1.4.15

AdaLab. Adaptive Automated Scientific Laboratory (AdaLab) Adaptive Machines in Complex Environments. n Start Date: 1.4.15 AdaLab AdaLab Adaptive Automated Scietific Laboratory (AdaLab) Adaptive Machies i Complex Eviromets Start Date: 1.4.15 Scietific Backgroud The Cocept of a Robot Scietist Computer systems capable of origiatig