Patrick Henry Winston

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1 The Past and Future of Artificial Intelligence Patrick Henry Winston 27 March 2017

3 Vision To know where we are going, we have to know where we are how we got here what is happing now what we can do next

4 What s new Programs that explain their own work Self-aware systems

5 How we got here: The Beginning

7 From Turing s paper I propose to answer the question, Can machines think? Average interrogator Not better than 70% correct Five minutes

8 From Turing s paper Objections: 1. Theological 2. Head in the sands 3. Mathematical 4. Lady Lovelace s Arguments from: 5. Disabilities 7. Continuity in the nervous system 8. Informality of behavior 9. Extrasensory perception

9 So why the Turing Test?

11 What we learned from Turing Serious people can think seriously about computers thinking There is no reason to doubt that computers will think someday

12 How we got here: The Pioneers

14 At Stanford: Formal logic

15 At Carnegie Mellon: Human problem solving

16 At MIT: Many methods working together

17 AI History The First Wave

18 James Slagle 1961

19 Analogical reasoning Anytime algorithms Bayesian techniques Case-based reasoning Computational linguistics Connectionism Constraint satisfaction Cross-modal clustering Cyc Explanation based learning Frames Fuzzy sets Genetic algorithms Image understanding Information retrieval Intermediate features K-lines Knowledge engineering Layered reasoning Legged locomotion Lisp, Smalltalk, and Logo Logic systems Machine learning Means-ends Memory based control Model based systems Multiple methods Multiple resolution Neural nets Open mind Planning and scheduling Probabilistic reasoning Problem reduction Propagator architecture Question answering Reasoning hierarchies Regularity based learning Reinforcement learning Representation Rule based systems Search Self-organizing maps Soar Speech understanding Streams and counter streams Structure mapping Subsumption Symbolic mathematics Visual and motor routines Wait-and-see algorithms

20 Winograd Winston Waltz Sussman

21 If you get the representation right Then you are almost done.

22 What we learned from early AI Architectures that deploy Methods that exploit Constraints exposed by Representations that support Models of Language, Vision, Problem solving, and Learning

23 AI History The Second Wave

24 Mycin Program: medical diagnosis If x s type is primary bacteremia and x s suspected portal is gastrointestinal and the site of the culture of x is sterile then there is evidence that x is bacteroides

25 Analogical reasoning Anytime algorithms Bayesian techniques Case-based reasoning Computational linguistics Connectionism Constraint satisfaction Cross-modal clustering Cyc Explanation based learning Frames Fuzzy sets Genetic algorithms Image understanding Information retrieval Intermediate features K-lines Knowledge engineering Layered reasoning Legged locomotion Lisp, Smalltalk, and Logo Logic systems Machine learning Means-ends Memory based control Model based systems Multiple methods Multiple resolution Neural nets Open mind Planning and scheduling Probabilistic reasoning Problem reduction Propagator architecture Question answering Reasoning hierarchies Regularity based learning Reinforcement learning Representation Rule based systems Search Self-organizing maps Soar Speech understanding Streams and counter streams Structure mapping Subsumption Symbolic mathematics Visual and motor routines Wait-and-see algorithms

26 Artificial Intelligence Cognition Machine learning Natural language Vision Robotics

27 Artificial Intelligence Robotics Machine learning Cognition Natural language Vision

30 AI History Third Wave

33 A group of young people playing a game of frisbee

34 Analogical reasoning Anytime algorithms Bayesian techniques Case-based reasoning Computational linguistics Connectionism Constraint satisfaction Cross-modal clustering Cyc Explanation based learning Frames Fuzzy sets Genetic algorithms Image understanding Information retrieval Intermediate features K-lines Knowledge engineering Layered reasoning Legged locomotion Lisp, Smalltalk, and Logo Logic systems Machine learning Means-ends Memory based control Model based systems Multiple methods Multiple resolution Neural nets Open mind Planning and scheduling Probabilistic reasoning Problem reduction Propagator architecture Question answering Reasoning hierarchies Regularity based learning Reinforcement learning Representation Rule based systems Search Self-organizing maps Soar Speech understanding Streams and counter streams Structure mapping Subsumption Symbolic mathematics Visual and motor routines Wait-and-see algorithms

37 A refrigerator filled with lots of food and drink

38 Lesson Learned Deep nets are amazing, but they do not See like us Think like us An important part of the story, but not the whole story

39 Third wave Worried people

40 2014 Musk: With Artificial Intelligence, we are summoning the demon. AI is our biggest existential threat

41 1970 Once the computers get control, we might never get it back. We would survive at their sufferance. If we re lucky, they might keep us as pets.

42 ~1970 Newell and Simon: The SOAR reasoning system is the answer; Alert the people

43 ~1980 Feigenbaum: Rule based expert systems are the answer

44 Brooks: ~1990 Subsumption is the answer

45 Present Hinton: Deep neural nets are the answer

46 What we learned from Minsky We need it all: representations, methods, agents, all working together

47 AlphaGo vs Lee Sedol, 2016

48 Messages for worried people New technology has unexpected dangers Ordinary computing is changing the world AI is not our top threat We are our biggest threat

49 Third wave: What s next

50 Computational statistics, machine learning, deep nets enabled by massive computing, massive training data

51 Companies protecting data, not programs

52 What next after next: The fourth wave

53 Why I am optimistic Massive, free computing Excited people Emerging round table Accumulated progress Better questions

55 Merge an inner language We, and only we, build complex, deeply nested, symbolic descriptions of situations and events

56 The Fourth Wave AI Artificial Perception

57 The Fourth Wave AI Artificial Perception Reasoning

58 The Fourth Wave AI Recipe following Artificial Perception Reasoning

59 The Fourth Wave AI Story understanding Recipe following Artificial Perception Reasoning

60 The Strong Story Hypothesis The mechanisms that enable us humans to tell, understand, and recombine stories separate our intelligence from that of other primates.

61 Story Understanding Symbolic Description Perception Merge Sequencing

62 Fairy and folk tales Religious parables Ethnic narratives History Literature Experience News Law Business Medicine Defense Diplomacy Science Engineering

64 What is read Macbeth is a thane and Macduff is a thane. Lady Macbeth is evil and greedy. Duncan is the king, and Macbeth is Duncan's successor. Duncan is an enemy of Cawdor. Macduff is an enemy of Cawdor. Duncan is Macduff's friend. Macbeth defeated Cawdor. Duncan becomes happy because Macbeth defeated Cawdor. The witches danced and had visions. Macbeth talks with the witches. The witches predicted that Macbeth will become king. The witches astonish Macbeth. Duncan executes Cawdor. Macbeth becomes Thane of Cawdor. Duncan rewarded Macbeth because Duncan became happy. Macbeth wants to become king because Lady Macbeth persuaded Macbeth to want to become the king. Macbeth invites Duncan to dinner. Duncan complements Macbeth. Duncan goes to bed. Duncan's guards become drunk and sleep. In order to murder Duncan, Macbeth murders the guards and Macbeth stabs Duncan. Mabeth becomes king. Malcolm and Donalbain flee. Macbeth's murdering Duncan leads to Macduff's fleeing to England. In order to flee to England, Macduff rides to the coast and Macduff sails on a ship. Then, Macduff's fleeing to England leads to Macbeth's murdering Lady Macduff. Macbeth hallucinates at a dinner. Lady Macbeth says he hallucinates often. Everyone leaves because Lady Macbeth tells everyone to leave. Macbeth's murdering Duncan leads to Lady Macbeth's becoming distraught. Lady Macbeth has bad dreams. Lady Macbeth thinks she has blood on her hands. Lady Macbeth kills herself. Birham Wood is a forest. Burnham Wood goes to Dunsinane. Macduff's army attacks Dunsinane. Macduff curses Macbeth. Macbeth refuses to surrender. Macduff kills Macbeth. The end.

65 Is Duncan dead? Why did Macduff kill Macbeth? Is there revenge? Pyrrhic victory?

66 Deduction rules If x kills y, then y becomes dead. Macbeth murders Duncan. Macbeth murders Duncan Duncan becomes dead

67 Explanation rules If x angers y, then y may kill x. Macbeth angers Macduff. Macduff kills Macbeth. Macbeth angers Macduff Macduff kills Macbeth

68 The Doctrine of Necessity Introduce only mechanisms that are needed to model an observed behavior.

69 Guess reason: abduction rule If x murders y, then x must be insane. Macbeth murders Duncan. Macbeth is insane Macbeth murders Duncan

70 Censor If y becomes dead, y cannot become unhappy. Duncan becomes dead. Duncan becomes dead Duncan becomes unhappy

71 Concept patterns X's harming Y leads to Y's harming X.

72 Situational explanation, Chinese Dispositional explanation, American

73 Duncan is a person. Lady Macbeth is a person. Macduff is a person. Macbeth is a person. A thane is a noble. Macbeth is a thane. Macduff is a thane. Lady Macbeth is greedy. Macbeth defeats a rebel. Appear is a success. Macbeth has a success. Witches talk with Macbeth. Witches have visions. Duncan rewards Macbeth because Duncan becomes happy. Macbeth wants to become king because Lady Macbeth persuades Macbeth to want to become king. Macbeth murders Duncan. Duncan becomes dead because Person x becomes dead whenever Person y kills person x.

74 Macbeth is a thane and Macduff is a thane. Lady Macbeth is evil and greedy. Duncan is the king, and Macbeth is Duncan's successor. Duncan is an enemy of Cawdor. Macduff is an enemy of Cawdor. Duncan is Macduff's friend. Macbeth defeated Cawdor. Duncan becomes happy because Macbeth defeated Cawdor. The witches danced and had visions. Macbeth talks with the witches. The witches predicted that Macbeth will become king. The witches astonish Macbeth. Duncan executes Cawdor. Macbeth becomes Thane of Cawdor. Duncan rewarded Macbeth because Duncan became happy. Macbeth wants to become king because Lady Macbeth persuaded Macbeth to want to become the king. Macbeth invites Duncan to dinner. Duncan complements Macbeth. Duncan goes to bed. Duncan's guards become drunk and sleep. In order to murder Duncan, Macbeth murders the guards and Macbeth stabs Duncan. Mabeth becomes king. Malcolm and Donalbain flee. Macbeth's murdering Duncan leads to Macduff's fleeing to England. In order to flee to England, Macduff rides to the coast and Macduff sails on a ship. Then, Macduff's fleeing to England leads to Macbeth's murdering Lady Macduff. Macbeth hallucinates at a dinner. Lady Macbeth says he hallucinates often. Everyone leaves because Lady Macbeth tells everyone to leave. Macbeth's murdering Duncan leads to Lady Macbeth's becoming distraught. Lady Macbeth has bad dreams. Lady Macbeth thinks she has blood on her hands. Lady Macbeth kills herself. Birham Wood is a forest. Burnham Wood goes to Dunsinane. Macduff's army attacks Dunsinane. Macduff curses Macbeth. Macbeth refuses to surrender. Macduff kills Macbeth. The end. The story is about Pyrrhic victory. Lady Macduff was Macduff's wife. Macbeth wanted to become king because Lady Macbeth persuaded Macbeth to want to become king. Macbeth murdered Duncan, probably because Duncan was a king and Macbeth was Duncan's successor. Macduff fled to England. Macbeth killed Lady Macduff. Macduff killed Macbeth, probably because Macbeth angered Macduff.

76 On a common-sense level, the Healthy reader thinks Samantha grabs an umbrella, probably because Samantha wants to reach the handle, and Samantha fails to reach the handle. The Schizophrenic reader thinks Samantha grabs an umbrella because Samantha wants to go into the rain.

77 What we learned about stories Simple mechanisms support many abilities: Instruction with student model Summary via concepts Story composition Mental illness explanation Hypothetical reasoning Self-aware processing

78 Can a machine be self aware?

79 Suitcase Words Intelligence Creativity Emotion Self awareness

80 Lu is a student. Shan is a student. Lu inhabits America. Lu fails his dissertation defense. Goertz is Lu's advisor. Goertz and Lu are not friends. Lu is Chinese. Lu went to US to do phd. Lu had highest entrance exam score. Lu is a bachelor. Lu is lonely. Lu owns a gun. Lu practices shooting. Lu passes his second dissertation defense. Lu becomes a lab assistant because Lu does not find a job. Shan is younger than Lu. Shan graduates with Lu. Shan received national award. Faculty rejected Lu's appeal. Goertz angers Lu. Shan comes from a small Chinese village. Shan is married. Shan is social. Shan has friends. Shan is successful. Shan outperforms Lu. In order to kill Shan, Lu shoots Shan. Then, Lu shoots himself.

81 Did Lu kill Shan because America is individualistic? Can you explain why you think so?

88 Did Lu murder Shan? Why did Lu murder Shan? How did Lu murder Shan? Was Lu lonely? Was Lu lonely before he murdered Shan? What happened after Lu murdered Shan? Do you believe America is individualistic? What is the story about?

89 Story Processor Neural net Neural net Neural net Probabilistic program Probabilistic program Rule-based system Subsumption system Planning system

90 What you need to know Part of the answer = computational statistics. That part enabled by massive computing, massive data. Tomorrow s answer is humanlike thinking Tomorrow s answer is on the horizon.

91 Expected contributions Smart systems on another level. Smart systems with explanations. Smart systems that understand us. A better understanding of ourselves and each other and that will make the world a better place

The Genesis Manifesto: Story Understanding and Human Intelligence

The Genesis Manifesto: Story Understanding and Human Intelligence Patrick Henry Winston and Dylan Holmes November 13, 2017 This is β draft #4. We plan to supply additional references and to address a long