Fusing Animals and Humans

1. Fusing Animals and Humans Jonathan Connell IBM T.J. Watson Research Center

2. Criteria for perceived intelligence human level Communicative Can express internal ideas and ingest situational descriptions, true language Abstract Can conceptualize situations remote in space and time, planning Social Aware of social order, use other beings as agents Personality Individuals have different likes and dislikes, preferences learned over time Aware Responds and changes actions based on human-perceptible environment change Animate Coordinated movement, many degrees of freedom The above seems to be the layered ordering in natural organisms. Note that language is a uniquely human ability.

3. How to achieve AI? • Silver bullet:Language Human veneer on top of base system • Construct a language interpreter • Focuses attention & partitions world • Enables one-shot “learning” • Core value:Motivation Animal substrate underlying control • Build symbols & decide how to act • Needs innate segmentation, comparison, and interest • Bootstraps to more elaborate concepts Artificial General Intelligence needs both parts

4. apple car cup house flower HUMAN LANGUAGE • Scripting system for sensory-motor subroutines • Linguistic interpreter needs grounding • Objects – show examples and give same label (needed for others) “horse” = • Properties – show different named objects and give same label “red” = • Relations – show configurations with named objects • Actions – show temporal sequences with named objects

5. “moth” “butterfly” “moth” “butterfly” Language & cognition With – “No, this is a moth not a butterfly. Look at its fuzzy antennae.” • Can guide attention, which helps learning • Without – Show lots of contrasting examples • Can impart procedures more directly Without – Trial and error until suddenly “Hurray!” With – “Hold the jar in your left hand, grasp the top with your right hand, and twist hard.”

6. Internal dialog • Sapir-Whorf revised: • Simply remember speech verbatim • Replay it through interpreter to actualize • Cf. Vygotsky’s model of child development • Example: • new driver operating a car • “Okay, the stop sign is coming up. Slow down and watch for other cars at the intersection. It the car on the cross street arrives first he gets to go first. It looks like there is no one around, so you can go now …”

7. Compiling patterns Condense sequences by removing reliable intermediate steps • Compiling out echoic situations yields direct encoding: (see: shaggy animal → say: “It’s a dog”) → (hear: “It’s a dog.” → represent: dog) see: shaggy animal → represent: dog • Compiling in narrative patterns enhances perception: (see: bird → hear: “It’s a bird!”) → (hear: “What shape is its beak?” → look: at beak) see: bird → look: at beak

8. ANIMAL MOTIVATION Needs: • Underlying proto-symbolic system • Objects – representing spatial-temporal loci • Properties – characterizing the objects • Relations – between objects and places • Reflexes – for generating for motions • Innate base cases • Segmentation: color, depth, texture, motion, loudness, pitch • Comparison: hue, brightness, template match, nearness, acoustic spectrum • Interest: Bright lights, loud noises, colorful objects, high motion • Methods for extending each • to produce and interpret more complex representations

9. isolated word acoustic spectra HMM model G I N G E R isolated object located in clutter new “isolated” object visual pixels residual template model error Segmentation

10. ? straight brown black at-back at-front straight white black “dog” at-back at-front curvy black whiskers X “dog” at-back at-front Comparison Checking if some situation matches a precondition: • Measure intrinsic feature similarity • Count number of exactly matching features • Estimate compatibility of parts Decide whether the beagle example matches the poodle or the cat “dog” Brown is an intrinsic mismatch to white, but everything else is exact Brown is close to black, but both subparts mismatch

11. Interest ? • Guides system’s overall activity • Traditional goal-driven systems are brittle • Many respond only to human-imposed goals • Do not spontaneously take the initiative if stuck • Sit idle if no goal (as opposed to exploring, etc.) • Indirect control systems are more robust Policies: sets of free-running situation-action rules Interest: general “goals” in terms of desired situations Directives: activate policies (K-lines) likely to achieve situation Affordances: detect that environment offers certain opportunities

12. <S, E, A> E I(x) Affordance Detector Persistent Directives Interest D(x) K-Line Policies Sensors Actuators S A Autonomous control S = prevailing sensory context E = exciting affordance sensed A = action selected to take I(x) = system is interested in x D(x) = system intends to achieve x

13. Bootstrap rules • Walking by pond and hear “splash”! • Interesting event occurred (splash) • So remember situation (pond) and event (walk-by) S & E & A & I(E) → <S, E, A> • See a pond and interested in splash noises • Remembered pattern mostly matches current conditions (pond) • And interested in remaining portion (splash) • So set a directive to obtain that portion (splash) <S, E, A> & S & I(E) → D(E) • Want a splash and near pond • Intend to obtain condition (splash) • And current conditions match remembered context (pond) • So do associated action in record (walk-by) D(E) & <S, E, A> & S → A • Want a splash and remember about dropping rocks • Intend to obtain condition (splash) • And condition happened in another memory • So become interested in the context of that memory (drop-rock) D(E) & <S, E, A> → I(S)

14. Summary • Human-level AI requires language • Makes learning classifications faster • Makes learning procedures easier • Internalized dialog can guide cognition • An interpreter can be built on an animal substrate • Use operant conditioning to obtain grounding • Needs proto-symbolic structures to work with • Self-motivation is essential to animals (& humans) • Actions not necessarily driven by explicit or imposed goals • Needs some innate segmentations, comparisons, and interests • Bootstrap procedure can make progressively more complex