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Artificial general intelligence (AGI) building thinking machines © 2007 General Intelligence Research Group.

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Presentation on theme: "Artificial general intelligence (AGI) building thinking machines © 2007 General Intelligence Research Group."— Presentation transcript:

1 Artificial general intelligence (AGI) building thinking machines © 2007 General Intelligence Research Group

2 AGI vs narrow AI examples of narrow AI: – face recognition – spam filtering – data mining – Google

3 Common objections intelligence is not well-defined its too hard computing power is not there yet no unifying theory of AI we dont understand the brain etc… All this is bull shit!

4 AI pioneers Alan Turing (1912-1954) John von Neumann (1903-1957)

5 John McCarthy (1927-) Marvin Minsky (1927-)

6 Implications of AGI complete automation ethical issues Technological Singularity Vernor Vinge (1944-) Ray Kurzweil (1948-)

7 Representative AGI projects Cyc Soar, ACT-R Polyscheme LIDA SNePS AIXI OSCAR NARS Novamente Cog CAM-Brain HTM SAIL a2i2 and many more…. (listed by Pei Wang)

8 Cyc most-funded AI project in history ($10s of millions) based on predicate logic complete ontology millions of facts, concepts Doug Lenat (1950-)

9 Soar Allen Newell (1927-1992) John E Laird based on production rules & rete algorithm learning – chunking

10 Novamente Ben Goertzel (1966-) probabilistic logic based on uncertain probabilities graph-based knowledge representation genetic algorithms for learning robot living in virtual reality 2007 book: Artificial General Intelligence

11 NARS Non-Axiomatic Reasoning System Pei Wang can learn from experience work with insufficient knowledge and resources unified cognition: reasoning, learning, planning, etc… 2006 book: Rigid Flexibility

12 SNePS Semantic Network Processing System Stuart C Shapiro extends first-order logic belief revision / assumption-based truth maintenance natural language understanding

13 AIXI Marcus Hutter highly abstract based on Kolmogorov complexity theory KC is incomputable learning may take forever!

14 Polyscheme Nick Cassimatis integrates multiple methods of representation, reasoning, and problem-solving procedural substrate not one model

15 CAM-brain Hugo de Garis (1947-) neural network evolvable hardware cellular automata currently at Wuhan University

16 SAIL John Weng neural network-based navigates and learns from environment autonomously

17 Jeff Hawkins (1957-) inventor of Palm Pilot founded Redwood Neuroscience Institute 2005 book: On Intelligence HTM (Hierarchical Temporal Memory) neurally-inspired

18 Brain- inspired AI visual cortex

19 Wiring of 6-layer cortex

20 Neurally-inspired AI feedforward neural network Jeff Hawkins approach problem: invariant recognition: translation, rotation, scaling

21 Statistical learning takes place in a vector space requires many examples target = manifold difficult to learn concepts with variables eg: On(apple,table), On(car,road), etc…

22 Spatial pattern recognition ANN, SVM, PCA, Clustering, etc…

23 Logic-based vision visual features logical representation

24 Logical-vision example Quadrilateral() :- e 1 :edge e 2 :edge e 3 :edge e 4 :edge v 1 :vertex v 2 :vertex v 3 :vertex v 4 :vertex Connects(e 1,v 1,v 2 ) ^ Connects(e 2,v 2,v 3 ) ^ Connects(e 3,v 3,v 4 ) ^ Connects(e 4,v 4,v 1 )

25 Syntactic pattern recognition predicate logic formula: feature i relation 1 (feature 1, feature 2, …) ^ relation 2 (feature 3, feature 4, …) ^ … Spatial interpretation?

26 Logic-based AI Avoid reinventing the wheel!

27 Logic-based AI first-order predicate logic (Prolog) common objections: brittle rigid binary not numerical just a theorem prover probabilistic / fuzzy logic non-deductive mechanisms eg: abduction, induction

28 Modules perception (eg vision) pattern recognition inference natural language learning truth maintenance planning

29 Architecture

30 Pattern recognition neural characteristics soft computing Prolog: chair(X) :- leg 1, leg 2, leg 3, leg 4, seat, back, horizontal(seat), vertical(back),... leg 1 chair leg 2 leg 3 leg 4 …... fuzzy values

31 Pattern recognition – chairs

32 more chairs

33 still more chairs

34 Pattern recognition how humans recognize concepts? [Michalski 1989] 2-tiered approach rule-based vs instance-based Prolog: chair :- chair 1 chair :- chair 2 chair :- chair 3... chair :- (rule for general chair)

35 Probabilistic logic classical resolution [JA Robinson 1965] Bayesian networks [eg Judea Pearl]

36 Resolution algorithm try to resolve formulas repeatedly until no more can be resolved P V Q~P V R Q V R

37 Bayesian network propositional

38 First-order Bayes net [Peter Norvig & Stuart Russell 2003] [Kathryn B Laskey 2006] [David Poole 2003] [Manfred Jaeger 1997] etc… BeltStatus(belt)RoomTemp(room) EngineStatus(machine)

39 Bayesian vs classical logic Conditional Probability Table (CPT) classical Bayesian (A ^ B) AB C ABC TT1.0 TF0.0 FT FF ABC TTT TFF FTF FFF

40 KBMC Knowledge-Based Model Construction [Wellman et al 1992] generate Bayesian networks on-the-fly to answer specific queries KB

41 KBMC example


43 Belief bases vs belief sets belief set = Cn( belief base ) set of consequences belief sets are too large to manipulate for AGI, must use belief base

44 Fuzzy logic Johns girl friend is probably very pretty fuzziness probability Lotfi Zadeh (1921-) 1965 fuzzy sets 1973 fuzzy logic

45 Confidence Example: A. 10 girls, 5 have long hair B. 1000 girls, 500 have long hair p = 0.5 but A and B are not the same B has higher confidence used in Pei Wangs NARS logic

46 Probabilistic-fuzzy inference ( P, C, Z ) n ( P, C, Z ) x 1 x 2... Ps and Zs can be point-valued or interval-valued probability confidence fuzziness

47 Probability intervals Example: marry fool [p = 0.8] ! marry loser [p = 0.7] p( fool V loser ) = 0.7 + 0.1 * p( marry ) [ 0.7, 0.8 ] unknown

48 Conditional probability table (CPT) All permutations of fuzzy values Or, store in a distribution-free format? abC z1z1 …(P 1, C 1, Z 1 ) z2z2 …(P 2, C 2, Z 2 ) z3z3 …(P 3, C 3, Z 3 ) z4z4 …(P 4, C 4, Z 4 ) ………

49 Rules of thought If cats have claws, and Juney is a cat, then Juney has claws. P,x,y P(x) ^ isa(y,x) P(y) modus ponens: syllogisms

50 reasoning deduction retroduction inductionabduction

51 Abduction finding explanations eg glass is wet it was raining algorithm: reverse of deduction (eg resolution) very high complexity (within the arithmetical complexity class )

52 Abduction algorithm

53 Induction vs abduction abduction: answer = ground literals eg grass is wet it was raining induction: answer = general formulae eg daughter(X,Y) :- father(Y,X) ^ female(Y)

54 Induction learning general patterns statistically ILP (Inductive Logic Programming) [Stephen Muggleton] 1990s

55 Induction example Given data: male(mary) = false female(mary) = true mother(mary, louise) = true father(mary, bob) = true daughter(bob, mary) = true daughter(X,Y) :- father(Y,X) ^ female(Y)

56 Natural language unifying framework language = knowledge-based inference [Jerry R Hobbs] Abduction as Interpretation eg The Boston office called. apple pie door knob street hawker all we need is a lot of rules can inductively learn the rules

57 Belief maintenance Truth Maintenance System (TMS) belief revision to attain consistency avoid cognitive dissonance

58 Truth maintenance justifications

59 Belief revision Epistemic entrenchmentBelief Base [Mary-Anne Williams 1995] … …6543210 entrenchment ranking

60 Click feeling Perhaps an effect of successful inference, abduction, or belief revision?

61 Paraconsistency holding 2 contradictory beliefs in the knowledge base at the same time

62 Associative memory knowledge base = database special indexing to allow associative recall hard disk = long-term memory RAM = working memory

63 Planning

64 Conclusions neural is problematic blank slate is problematic logic-based is very promising

65 Agenda for Logic-based AI probabilistic-fuzzy logic 2.develop algorithms for: – abduction – belief maintenance 3.acquire common sense knowledge

66 Web 2.0-style collaboration branching voting commercial problem: too few members

67 Thank you

68 [Aliseda 2006] Abductive Reasoning: Logical Investigations into Discovery and Explanation. Synthese Library Series vol 330, Springer [Antoniou 1997] Nonmonotonic Reasoning, MIT Press [Cussens 2001] Integrating probabilistic and logical reasoning. In David Corfield and Jon Williamson eds Foundations of Bayesianism, volume 24 of Applied Logic Series, pages 241-260. Kluwer, Dordrecht [2000 Flach & Kakas eds] Induction and Abduction, Springer Applied Logic Series #18 [Haddawy 1994] Generating Bayesian networks from probability logic knowledge, in Proceedings of the 10 th conference on uncertainty in AI, 1994. [Hobbs 200?] Abduction as Interpretation [Jaeger 1997] Relational Bayesian networks. In Proceedings of the 13 th Annual Conference on Uncertainty in AI (UAI-97), p266-273, San Francisco, CA, 1997, Morgan Kaufman Publishers [Kakas, Kowalski, Toni 1992] Abductive Logic Programming, Journal of Logic and Computation 2(6):719-770. [Laskey 2006] MEBN: A logic for open-world probabilistic reasoning. GMU C4I Center Technical Report C4I-06-01. George Mason Univ, USA. [Milch & Russell 2007] First-Order Probabilistic Languages: Into the Unknown In ILP: Proceedings of the 16th International Conference on Inductive Logic Programming. Berlin: Springer First-Order Probabilistic Languages: Into the Unknown

69 [Michalski 1989] Two-tiered concept meaning, inferential matching, and conceptual cohesiveness. In Vosniadou & Ortony eds, Similarity and analogical reasoning, p122-145. Cambridge University Press, New York. [Muggleton 1996] Stochastic logic programs. In de Raedt, ed, Advances in Inductive Logic Programming, p254-264, IOS Press 1996. [Ngo, Haddawy, & Helwig 1995] A theoretical framework for context- sensitive temporal probability model construction with application to plan projection. In Proceedings of the 11 th Annual Conference on Uncertainty in Artificial Intelligence (UAI-95), p419-426, Montreal, Quebec, Canada. [Norvig & Russell 2003] Artificial Intelligence: A Modern Approach, Prentice Hall. [Poole 1993] Probabilistic horn abduction and Bayesian networks, Artificial Intelligence, 64(1), 81-129, 1993 [Poole 2003] First-order probabilistic inference, Proc, IJCAI-03, Acapulco, August 2003, p985-991First-order probabilistic inferenceIJCAI-03 [Wellman, Breese, Goldman 1992] From knowledge bases to decision models. Knowledge Engineering Review 7(1): 35-52 [Williams 1995] Changing nonmonotonic reasoning inference relations, in Proceedings of the second world conference on the fundamentals of AI, 469-482, Ankgor, Paris, 1995

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