Understanding

1. Understanding • I Hear and I Forget • I See and I Remember • I Do and I Understand Attributed to Confucius, ~500 BCE

3. How could a mass of chemical cells produce language and thought? Will computers think and speak? How much can we know about our own experience? How do we learn new concepts? Does our language determine how we think? Is language Innate? How do children learn grammar? How did languages evolve? Why do we experience everything the way that we do?

4. Constrained Best Fit in Nature inanimate animate

6. Embodiment Of all of these fields, the learning of languages would be the most impressive, since it is the most human of these activities. This field, however, seems to depend rather too much on the sense organs and locomotion to be feasible. Alan Turing (Intelligent Machines,1948)

11. Artist’s Rendition of a Typical Cell Membrane

15. Pre-Natal Tuning: Internally generated tuning signals • But in the womb, what provides the feedback to establish which neural circuits are the right ones to strengthen? • Not a problem for motor circuits - the feedback and control networks for basic physical actions can be refined as the infant moves its limbs and indeed, this is what happens. • But there is no vision in the womb. Recent research shows that systematic moving patterns of activity are spontaneously generated pre-natally in the retina. A predictable pattern, changing over time, provides excellent training data for tuning the connections between visual maps. • The pre-natal development of the auditory system is also interesting and is directly relevant to our story. • Research indicates that infants, immediately after birth, preferentially recognize the sounds of their native language over others. The assumption is that similar activity-dependent tuning mechanisms work with speech signals perceived in the womb.

16. Post-natal environmental tuning • The pre-natal tuning of neural connections using simulated activity can work quite well – • a newborn colt or calf is essentially functional at birth. • This is necessary because the herd is always on the move. • Many animals, including people, do much of their development after birth and activity-dependent mechanisms can exploit experience in the real world. • In fact, such experience is absolutely necessary for normal development. • As we saw, early experiments with kittens showed that there are fairly short critical periods during which animals deprived of visual input could lose forever their ability to see motion, vertical lines, etc. • For a similar reason, if a human child has one weak eye, the doctor will sometimes place a patch over the stronger one, forcing the weaker eye to gain experience.

18. Tinbergen’s Four Questions How does it work? How does it improve fitness? How does it develop and adapt? How did it evolve?

19. 5levels of Neural Theory of Language Spatial Relation Motor Control Metaphor Grammar Cognition and Language Computation Structured Connectionism abstraction Neural Net SHRUTI Computational Neurobiology Triangle Nodes Biology Neural Development Quiz Midterm Finals

20. 1000 operations/sec 100,000,000,000 units 10,000 connections/ graded, stochastic embodied fault tolerant evolves, learns 1,000,000,000 ops/sec 1-100 processors ~ 4 connections binary, deterministic abstract crashes designed, programmed Brains ~ Computers

21. Freud’s Original Connectionist Model

22. Connectionist Model of Word Recognition (Rumelhart and McClelland) Interactive Activation Reading Model

23. Modeling lexical access errors • Semantic error • Formal error (i.e. errors related by form) • Mixed error (semantic + formal) • Phonological access error

24. Phonological access error: Selection of incorrect phonemes Syl FOG DOG CAT RAT MAT On Vo Co f r d k m ae o t g Onsets Vowels Codas Adapted from Gary Dell, “Producing words from pictures or from other words”

25. Representing concepts using triangle nodes

26. Connectionist Circuit for Gender(RD5) = female RD5 2 gender female Also shown as a triangle node

27. How does activity lead to structural change? • The brain (pre-natal, post-natal, and adult) exhibits a surprising degree of activity dependent tuning and plasticity. • To understand the nature and limits of the tuning and plasticity mechanisms we study • How activity is converted to structural changes (say the ocular dominance column formation) • It is centrally important for us to understand these mechanisms to arrive at biological accounts of perceptual, motor, cognitive and language learning • Biological Learning is concerned with this topic.

28. Long Term Potentiation (LTP) • These changes make each of the winning synapses more potent for an intermediate period, lasting from hours to days (LTP). • In addition, repetition of a pattern of successful firing triggers additional chemical changes that lead, in time, to an increase in the number of receptor channels associated with successful synapses - the requisite structural change for long term memory. • There are also related processes for weakening synapses and also for strengthening pairs of synapses that are active at about the same time.

29. During normal low-frequency trans-mission, glutamate interacts with NMDA and non-NMDA (AMPA) and metabotropic receptors. With high-frequency stimulation

30. The ICSI/BerkeleyNeural Theory of Language Project

31. Rods and Cones in the Retina http://www.iit.edu/~npr/DrJennifer/visual/retina.html

32. Color Naming Basic Color Terms (Berlin & Kay) Criteria: 1. Single words -- not “light-blue” or “blue-green” 2. Frequently used -- not “mauve” or “cyan” 3. Refer primarily to colors -- not “lime” or “gold” 4. Apply to any object -- not “roan” or “blond” © Stephen E. Palmer, 2002

33. The WCS Color Chips • Basic color terms: • Single word (not blue-green) • Frequently used (not mauve) • Refers primarily to colors (not lime) • Applies to any object (not blonde) FYI: English has 11 basic color terms

34. Results of Kay’s Color Study If you group languages into the number of basic color terms they have, as the number of color terms increases, additional terms specify focal colors

35. Ideas from Cognitive Linguistics • Embodied Semantics (Lakoff, Johnson, Sweetser, Talmy • Radial categories (Rosch 1973, 1978; Lakoff 1985) • mother: birth / adoptive / surrogate / genetic, … • Profiling (Langacker 1989, 1991; cf. Fillmore XX) • hypotenuse, buy/sell (Commercial Event frame) • Metaphor and metonymy (Lakoff & Johnson 1980, …) • ARGUMENT IS WAR, MORE IS UP • The ham sandwich wants his check. • Mental spaces (Fauconnier 1994) • The girl with blue eyes in the painting really has green eyes. • Conceptual blending (Fauconnier & Turner 2002, inter alia) • workaholic, information highway, fake guns • “Does the name Pavlov ring a bell?” (from a talk on ‘dognition’!)

36. Concepts are not categorical

37. Radial Structure of Mother The radial structure of this category is defined with respect to the different models Geneticmother Stepmother Unwedmother Adoptivemother CentralCase Surrogatemother Birthmother Biologicalmother Naturalmother Fostermother

38. Language, Learning and Neural Modelingwww.icsi.berkeley.edu/AI • Scientific Goal Understand how people learn and use language • Practical Goal Build systems that analyze and produce language • Approach Embodied linguistic theories with advanced biologically-based computational methods

39. General and Domain Knowledge • Conceptual Knowledge and Inference • Embodied • Language and Domain Independent • Powerful General Inferences • Ubiquitous in Language • Domain Specific Frames and Ontologies • Framenet (www.icsi.berkeley.edu/framenet) • Metaphor links domain specific to general • E.g., France slipped into recession.

40. boundary bounded region Image schemas • Trajector / Landmark (asymmetric) • The bike is near the house • ? The house is near the bike • Boundary / Bounded Region • a bounded region has a closed boundary • Topological Relations • Separation, Contact, Overlap, Inclusion, Surround • Orientation • Vertical (up/down), Horizontal (left/right, front/back) • Absolute (E, S, W, N) LM TR

41. Learning System We’ll look at the details next lecture dynamic relations (e.g. into) structured connectionistnetwork (based on visual system)

42. Cafe Simulation-based language understanding Utterance “Harry walked to the cafe.” Constructions Analysis Process General Knowledge Simulation Specification Schema Trajector Goal walk Harry cafe Belief State Simulation

43. Simulation Semantics • BASIC ASSUMPTION: SAME REPRESENTATION FOR PLANNING AND SIMULATIVE INFERENCE • Evidence for common mechanisms for recognition and action (mirror neurons) in the F5 area (Rizzolatti et al (1996), Gallese 96, Boccino 2002) and from motor imagery (Jeannerod 1996) • IMPLEMENTATION: • x-schemas affect each other by enabling, disabling or modifying execution trajectories. Whenever the CONTROLLERschema makes a transition it may set, get, or modify stateleading to triggering or modificationof other x-schemas. State is completely distributed (a graph marking) over the network. • RESULT: INTERPRETATION IS IMAGINATIVE SIMULATION!

44. walker at goal energy walker=Harry goal=home Active representations • Many inferences about actions derive from what we know about executing them • Representation based on stochastic Petri nets captures dynamic, parameterized nature of actions • Walking: • bound to a specific walker with a direction or goal • consumes resources (e.g., energy) • may have termination condition(e.g., walker at goal) • ongoing, iterative action

45. Learning Verb MeaningsDavid Bailey A model of children learning their first verbs. Assumes parent labels child’s actions. Child knows parameters of action, associates with word Program learns well enough to: 1) Label novel actions correctly 2) Obey commands using new words (simulation) System works across languages Mechanisms are neurally plausible.

46. food toys misc. people sound emotion action prep. demon. social Words learned by most 2-year olds in a play school (Bloom 1993)

47. System Overview

48. Learning Two Senses of PUSH Model merging based on Bayesian MDL

49. Training ResultsDavid Bailey English • 165 Training Examples, 18 verbs • Learns optimal number of word senses (21) • 32 Test examples : 78% recognition, 81% action • All mistakes were close lift ~ yank, etc. • Learned some particle CXN,e.g., pull up Farsi • With identical settings, learned senses not in English

50. Task: Interpret simple discourse fragments/ blurbs France fell into recession. Pulled out by Germany US Economy on the verge of falling back into recession after moving forward on an anemic recovery. Indian Government stumbling in implementing Liberalization plan. Moving forward on all fronts, we are going to be ongoing and relentless as we tighten the net of justice. The Government is taking bold new steps. We are loosening the stranglehold on business, slashing tariffs and removing obstacles to international trade.