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Henry Lieberman MIT Media Lab Reasoning From (Not Quite) Text Henry Lieberman (with Catherine Havasi, Rob Speer, Ken Arnold, Dustin Smith) MIT Media Lab.

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Presentation on theme: "Henry Lieberman MIT Media Lab Reasoning From (Not Quite) Text Henry Lieberman (with Catherine Havasi, Rob Speer, Ken Arnold, Dustin Smith) MIT Media Lab."— Presentation transcript:

1 Henry Lieberman MIT Media Lab Reasoning From (Not Quite) Text Henry Lieberman (with Catherine Havasi, Rob Speer, Ken Arnold, Dustin Smith) MIT Media Lab & Mind-Machine Project Cambridge, Mass. USA

2 Henry Lieberman MIT Media Lab Big success for Reasoning with Text this week! Yow!

3 Henry Lieberman MIT Media Lab For the play-by-play….

4 Henry Lieberman MIT Media Lab We need better mechanisms for reasoning! Clue: It was this anatomical oddity of US gymnast George Eyser.... Ken Jennings' answer: Missing a hand (wrong) Watson's answer: leg (wrong) Correct answer: Missing a leg

5 Henry Lieberman MIT Media Lab Turings Dream & Knowledge Challenge - Schubert Natural language is a pretty damn good knowledge representation language Has capabilities that formal KR doesnt Resist the urge to simplify so the computer can understand it Dont be so afraid of the Ambiguity bogeyman

6 Henry Lieberman MIT Media Lab Capabilities of Natural Language Representations Generalized Quantifiers Event/Situation Reference Modification (of Predicates & Sentences) Reification (of Predicates & Sentences) Metric/Comparative Attributes Uncertainty & Genericity Metalinguistic Capabilities

7 Henry Lieberman MIT Media Lab Textual Entailment Yow!

8 Henry Lieberman MIT Media Lab Logical Reasoning: Classic example Birds can fly. Tweety is a bird. Therefore… Tweety can fly.

9 Henry Lieberman MIT Media Lab Logical Reasoning: Not-so-classic example Cheap apartments are rare.

10 Henry Lieberman MIT Media Lab Logical Reasoning: Not-so-classic example Cheap apartments are rare. Rare things are expensive.

11 Henry Lieberman MIT Media Lab Logical Reasoning: Not-so-classic example Cheap apartments are rare. Rare things are expensive. Therefore… Cheap apartments are expensive. So, exactly what was wrong with that??

12 Henry Lieberman MIT Media Lab Yeah, what's wrong with that? Logicians say: Not the same sense of "rare", "expensive", etc. I say: Maybe, but punts the problem of translating language/Commonsense to logic Logic is about possible inference; Common Sense is about plausible inference

13 Henry Lieberman MIT Media Lab Not so interested in absolute truth as we are in… Plausibility (not necessarily Probability) Similarity Analogy Relevance Computing "intangible" qualities (affect, point of view, connotation, overall "sense")

14 Henry Lieberman MIT Media Lab Logical vs. Commonsense knowledge PreciseVague FormalNatural language ExpertsGeneral public ExplicitImplicit ConsistentPossibly contradictory Up-front organizationBack-end organization

15 Henry Lieberman MIT Media Lab Logical vs. Statistical Reasoning Big debate, much hot air We need to fill in the gap between them Word occurrences are weak evidence Symbolic expressions much stronger But how do you combine lots of them?

16 Henry Lieberman MIT Media Lab Open Mind Common Sense

17 Henry Lieberman MIT Media Lab Open Mind Common Sense The Wikipedia version of Cyc since Million English statements, other languages How much Commonsense does an average person know? 1 human lifetime = 3 billion seconds Less than a billion - Maybe 100 million How much domain knowledge does a single expert know? Less than a million - Maybe thousand

18 Henry Lieberman MIT Media Lab Open Mind Commons - Speer

19 Henry Lieberman MIT Media Lab Granularity How much parsing should you do? Stemming, Lemmatizing, Chunking, Tagging, … Somethings lost and somethings gained Adjustable granularity

20 Henry Lieberman MIT Media Lab Effect of the parser

21 Henry Lieberman MIT Media Lab ConceptNet relations

22 Henry Lieberman MIT Media Lab ConceptNet - Liu, Singh, Eslick

23 Henry Lieberman MIT Media Lab AnalogySpace – Speer, Havasi

24 Henry Lieberman MIT Media Lab What AnalogySpace can do It can generalize from sparsely-collected knowledge It can identify the most important dimensions in a knowledge space It can classify concepts along those dimensions It can create ad-hoc categories (and classify accordingly) It can confirm or question existing knowledge

25 Henry Lieberman MIT Media Lab AnalogySpace matrix

26 Henry Lieberman MIT Media Lab Dimensionality Reduction

27 Henry Lieberman MIT Media Lab Singular Value Decomposition

28 Henry Lieberman MIT Media Lab Traditional Logical Inference Inferences goes from True assertion -> True assertion via Inference Rules Good news: Very precise and reliable Bad news: Proof search blows up exponentially Requires precise definitions and assertions GIGO

29 Henry Lieberman MIT Media Lab AnalogySpace Inference All possible assertions put in a (big, sparse) box You can rearrange the box along semantic axes Good news: Computationally efficient Tolerant of imprecision, contradiction, disagreement… Stronger than statistical inference Bad news: Cant be guaranteed to be very precise

30 Henry Lieberman MIT Media Lab Not-so-Common Sense Use Common Sense tools & methodology, but knowledge only common to a small group Collect knowledge from natural language sources Collect knowledge from games Collect knowledge from existing DBs, Ontologies,.. "Blend" with general Commonsense knowledge -> AnalogySpace for specific domain

31 Henry Lieberman MIT Media Lab Blending - Havasi Inference combining two AnalogySpaces Specialized and generalized knowledge bases Blending factor

32 Henry Lieberman MIT Media Lab CrossBridge - Krishnamurthy AnalogySpace-based technique for Structure Mapping analogy Indexes small networks of concepts & assertions Can do Case-Based Reasoning Electricity flows through Wires -> Water flows through Pipes, or Light flows through Fiber-Optic Cables?

33 Henry Lieberman MIT Media Lab CrossBridge - Krishnamurthy

34 Henry Lieberman MIT Media Lab Applications in Interface Agents Predictive typing, Speech recognition Storytelling with Media Libraries Detection and mitigation of online bullying Opinion Analysis Goal-oriented interfaces for Consumer Electronics Mobile to-do lists, location-aware context-sensitive maps Translation, language learning & multi-lingual communication Help and customer service Recommendation systems, scenario-based recommendation Programming and code sharing in natural language … and more

35 Henry Lieberman MIT Media Lab Example: Earth Sciences Knowledge Collaboration with Schlumberger Collect Earth Sciences Knowledge for intelligent search & browsing ~ 2000 assertions = 300 manual from game Game = 2 one-hour sessions x 10 people 350 concepts, read glossary document

36 Henry Lieberman MIT Media Lab Geology sentences Petroleum is a mixture of hydrocarbons. [IsA] Air gun array is used for seismic surveying offshore. [UsedFor] A seismic survey is a measure of seismic-wave travel. [Measures] A wildcat is an exploration well drilled in an unproven area. [IsA] You would drill an exploration well because you want to determine whether hydrocarbons are present. [MotivatedByGoal]

37 Henry Lieberman MIT Media Lab Knowledge collection: Common Consensus - Smith

38 Henry Lieberman MIT Media Lab Knowledge collection: Common Consensus - Smith

39 Henry Lieberman MIT Media Lab Geology knowledge space You can find oil where there are lizards

40 Henry Lieberman MIT Media Lab Luminoso – Speer, Havasi Turnkey Opinion Analysis & Visualization platform Constructs AnalogySpace from sets of text files

41 Henry Lieberman MIT Media Lab Opinions of software When people talk about the mechanics of using software, that means they don't like it When people talk about what they want to do with software, that means they like it

42 Henry Lieberman MIT Media Lab You can use our stuff!

43 Henry Lieberman MIT Media Lab Event Networks – Dustin Smith Tomorrow at 4!

44 Henry Lieberman MIT Media Lab ToDoGo – Dustin Smith Yow!

45 Henry Lieberman MIT Media Lab Conclusion Theres been a controversy between logical and statistical reasoning We need to fill in the gap Symbolic representations as source Do the math to combine large numbers of them New thinking about Commonsense reasoning

46 Henry Lieberman MIT Media Lab Thanks! Henry Lieberman MIT Media Lab

47 Henry Lieberman MIT Media Lab Title Yow!

48 Henry Lieberman MIT Media Lab Title Yow!

49 Henry Lieberman MIT Media Lab Title Yow!

50 Henry Lieberman MIT Media Lab Title Yow!

51 Henry Lieberman MIT Media Lab Title Yow!


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