SuperToy : Using Early Childhood Development to Guide AGI

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SuperToy

: Using Early Childhood
Development to Guide AGI

Scott Settembre

University at Buffalo,
SNePS

Research Group

ss424@cse.buffalo.edu

June 24, 2008

Overview


Demonstration of the
SuperToy

interface


Explore capabilities and progression of human
baby communication and thinking


What studies have been done in psychology?


Implications to developing an AGI?


How is AGI different from classical AI?


Different approaches in AGI


My approach and observations


Demonstrate


Baby
LyLy

: the learning agent interface

SuperToy

Interface


Visual Interface


3D Head, ability to mimic natural movements


Accurate lip
-
syncing


Ability to display emotional state


Listening Interface


Speech Recognition, allowing for hand
-
coded grammars or
free
-
speech


Ability to view HMM probabilities (and choose for myself)


Talking Interface


Realistic voice, child
-
like voice preferred


Ability to change prosody (rhythm, stress and intonation)

Baby Agent


Video teaser

Adult Agent


Video Teaser

Child Psychology Studies


Purpose: to understand how one AGI develops
may indicate a progression of learning path
that is possible (or perhaps necessary)


Progression in world representation/objects


Progression in language development


Interesting indication of dependency


What does this imply in terms of


Conversation and interaction


Mental representation and manipulation

Newborns


12 hr old newborns can distinguish between
native and foreign languages



Maybe not so innate, since they probably learn
rhythms of language in the womb



24 hr old


“Lexical Words” experiment



Can distinguish between content words, like
nouns/verbs, and words with no meaning of their
own, like prepositions/articles. (Occurs even in
different languages)

† Studies done by Janet
Werker

at the University of British Columbia, Canada.

Newborns


10 days old


“Face Recognition” experiment



Newborn prefers correct orientation of features,
but favors contrast over features (A & C)





6 weeks old


same experiment


Baby prefers features over contrasts (A & D)

† Study done by Daphne Maurer, McMaster University, Canada.

A.

B.

C.

D
.

Newborns


What is innate?


Crying


an innate signal of distress


Soothing


mother’s voice will soothe baby, but
any human voice will work


Attention


visual attention drawn to contrasts
within first two weeks


Language
development
? We will return to this…


Few week old Babies


Develop simple rules for conversation


Gaze of the eyes, directed at speaker


Facial expressions, match emotion in voice



“Eye gaze” experiment




Baby gets frustrated if mother averts her eyes when talking
to the baby.


“Upside
-
down face” experiment




An inverted face of mother is not recognized


“Happy
-
Sad face” experiment




Emotions of face and in voice should match


† Study done by Darwin Muir lab, Queens University, Canada.

Few week old Babies


8 weeks old


Connections are being made between modalities


Correlations between sights and sounds


Beginning to understand what an object is



10 mo.
-

“Object Permanence” experiment




Moving a doll behind an obstruction, baby expects doll not
to move magically


“an object must continue to exist in
time and space”

† Study done by Andrea
Aguiar
, University of Waterloo, Canada.

Few month old Babies


Ability to “search” and understand objects
increases


6 mo.


“Hidden Object” experiment




Toy hidden under a blanket cannot be found, but
can at 8 mo.


9 mo.


“The Search” experiment




Toy hidden behind wall cannot be found at 8 mo.,
but can at 9 mo.

† Study done by Andrea
Aguiar
, University of Waterloo, Canada.

Few month old Babies


18 mo.


“Object Permanence” experiment




Cannot find a toy twice hidden. Hidden under
hand, then hand under blanket. Once hand is
removed, baby thinks it is still in hand.



These experiments are significant because
although objects are understood at 2.5
months, not all properties & physical laws are.

† Study done Andrew
Meltzoff
, Center for Mind Brain & Learning, U. at Washington.

Object Properties Timeline


2.5 mo.


understand there are objects
1


5 mo.


width of objects, no big into small
1


6 mo.


simple addition, 1 toy and 1 toy = 2 toys
2


7.5 mo.


height of objects, no long into short
1


9 mo.


occlusion, can find obscured object
1


9 mo.


motion, can predict path of ball
3


But cannot do occlusion and motion at same time


11 mo.


tool use
4


18+ mo.


twice hidden object can be found
5

Object Learning
-

Summary


Children first learn by observation, then by
experimenting


How objects behave


Size and Shape


Under, behind, inside


Falling, inclines, planes, volume, liquids… etc.


They learn properties/categories one at a time


At some stages, they cannot apply two physical
laws/properties/categories at the same time

Baby
vs

Computer
-

Communication

Initial Language Development


6 mo. old


still able to distinguish all sounds




10 mo. old


loses this ability




Beginning to filter out sounds that are not part of
the language they hear all the time.



Implies that babies “start out as universal
linguists and then become culture bound
specialists”

† Study done by Andrea
Aguiar
, University of Waterloo, Canada.

New Words and Pointing


13 mo. old


“Joint Visual Attention” ex.



Uniquely human gesture of pointing


Pointing must coincide with the gaze of the
speaker in order to be meaningful



How to do this in a text based interface?


Even
Hellen

Keller was able to select things to be
named (or have them brought to her attention by
touch and then named)

† Study done by Andrea
Aguiar
, University of Waterloo, Canada.

Conversation and Turn Taking


14 mo. old


“The Robot” experiment



Green fur makes noise after baby makes noise,
and they begin taking turns making noise.


18 mo. old


“Imitation” experiment



Taking turns with a toy may be important to
understand taking turns with a conversation.

† Study done by Susan Johnson, Stanford University.

‡ Study done Andrew
Meltzoff
, Center for Mind Brain & Learning, U. at Washington.

New Words and Shape


17
-
24mo. olds


“Shape Bias” experiment




Identifies object by shape, not color or texture


Getting a child to pay attention to shape can increase
vocabulary 3x faster



Implication? Perhaps language in humans is
intertwined with the learning of objects and
properties of the world.


Is “Language Explosion” at two years old due to
understanding objects and categories better?

† Study done by Susan Jones, Indiana University.

Language Learning Timeline


24 week old fetus


can hear voices


Melody and rhythms of speech make heart beat faster


Birth


crying, mother’s voice soothes baby


12 hrs old


distinguish native language
1


24 hrs old


distinguish between POS
1


Weeks old


needs eye contact and matching voice & face
emotions
2


6 mo old


can distinguish all language sounds
1


10 mo old


filters out non
-
native language sounds
1


13 mo old


uses gestures and pointing to understand
3


14 mo old


understands give and take of conversation
4


18 mo old


imitation useful in learning how to converse
5


18mo
-
2years


Everything has a name, shape bias
6


“Language explosion”


micro
-
sentences, mimic actions



Language Development
-

Summary


Language initially taught by Mother
-
ese

and Father
-
ese


Sing song quality, pitched higher


Sentences reduced to short phrases


Exaggerated words, stretched vowels


Repeating of the words


Emphasis on most important “meaningful” words


Gesturing and facial expressions


Can convey intentions, feelings, desires


Imitation and repetition


“primary engine for learning” language


Understand more words than can use


At 2 yrs old child uses 300 words, but understands 1000.



Anything Applicable to AGI


An underlying representation of objects helps
learn

a language


Maybe words are “verbal objects” and are subject
to the same rules that objects are to categories?


Maybe grammar is equivalent to physical laws?


Maybe articles/prepositions, non
-
content words,
are properties/relations between objects?

…Applications cont.


Humans have a clear progression in object
understanding and language development


Is this sequential progression a limitation of neural
architecture?


Is this sequential progression necessary for
any

kind of language learning and development?

…Applications cont.


Are we having difficulty because we do not
process visual data in connection with
language development?


Forms of
gesturing

and
pointing

are necessary in
language development (required)


At early ages (weeks old)
eye contact
and

matching emotions
of the voice and face occurs
(must be advantageous?)


Shape bias occurs at/around the time of the
“language explosion” (coincidence?)


Baby
vs

Computer
-

Story Time

Artificial General Intelligence


“…the
construction of a software
program that
can solve a variety of complex problems in
a
variety
of different
domains, and
that controls
itself autonomously, with its own thoughts,
worries,
feelings, strengths
, weaknesses and
predispositions
.”




Whereas normal AI would be “
creating programs
that demonstrate intelligence in one or
another
specialized
area”

† Section content from “Contemporary Approaches to Artificial General Intelligence”
by
Cassio

Pennachin

and Ben
Goertzel

Taxonomy of AGI Approaches


AGI approaches:


symbolic


symbolic
and probability
-

or
uncertainty
-
focused


neural net
-
based


evolutionary


artificial life


program
search
based


embedded


integrative

AGI
-

Symbolic


GPS


General Problem Solver


U
ses
heuristic
search, break goals into
subgoals


No learning involved



Doug
Lenat’s

CYC
project


Encodes
all common sense knowledge in first
-
order
predicate
logic


Uses humans to encode knowledge in
CycL


New effort called “
CognitiveCyc
” to address
creating
autonomous,
creative,
interactive
intelligence



Alan Newell’s
SOAR


N
o
real autonomy or
self
-
understanding


Used as limited
-
domain, problem solving tool

AGI


Symbolic & Probabilistic


ACT
-
R
framework


similar to SOAR


Modeling of
human performance on relatively narrow and
simple
tasks [modularity of mind mimicked]


Uses probability, similar to some human cognition tasks



Bayesian
networks


Embody
knowledge about probabilities
and dependencies
between events in the
world


Learning the probabilities (what to learn) is a problem



NARS
-

uncertainty
-
based, symbolic AI
system


uncertain logic


with
fuzzy logic, certainty
theory



Failed Japanese
5th
Generation Computer System

AGI


Neural net
-
based


Attempts include using:


Network models
carrying out specialized functions
modeled on particular
brain regions


Cooperative
use of a variety of different neural net
learning
algorithms


Evolving differing net
-
assemblies and piecing
them together


Physical simulation of brain tissue

AGI


Evolutionary


John Holland’s Classifier System


Hybridization
of evolutionary algorithms and
probabilistic
-
symbolic AI


S
pecifically
oriented
toward
integrating memory,
perception, and cognition to allow an AI system
to
act
in the
world



CAM
-
Brain
machine


Hugo de
Garis


Evolving differing net
-
assemblies and piecing
them together

AGI


Artificial Life


Approaches are interesting, but not fruitful
above a basic level of cognition


“Creatures” games (social)


“Network Tierra” project (
multicellular
)



AlChemy
” project (lower biological processes)


No
Alife

agent with significant general intelligence

AGI
-

Program search


General approach


Begins
with
a formal
theory of general
intelligence


Defines
impractical algorithms that
are provably
known to achieve general
intelligence


Then approximate
these
algorithms
with
related, but
less comprehensive, algorithms



There is actually a solution to AGI, but the search
for the algorithm takes a long time


AIXI (
Marcus
Hutter
) can work, but requires infinite
memory and infinitely fast processor!

AGI


Integrative


Taking
elements
from various approaches
and creating a
combined, synergistic
system



Create
a unified
knowledge
representation and dynamics
framework


Manifest the
core ideas of the various AI paradigms within the
universal
framework.



Novamente

project


Goertzel
/
Pennachin


Uses Semantic Networks, Genetic Algorithms, Description Logic,
Probability integration, Fuzzy Truth values


Uses
psynet

model
of mind
http://www.goertzel.org/books/wild/chapPsynet.html

My Approach


Develop two systems


Top down


hand coded AI and KR&R


Bottom up


learn through interaction


Key idea is to use the same representation scheme
for both systems


My intent is to develop an environment for which
the needs of each system will constrain or expand
the representation for the benefit of the other
system

My Approach


cont.


Visual modality as
input

is out


Too costly in processing and effort


This means there will be:


No eye gaze consideration


No visual emotional content


No processing of gestures/pointing


No shape bias benefits


These are serious drawbacks that will need to be
compensated for

My Approach


cont.


Verbal processing partly done by Speech
Recognition engine


Benefits include


No need for learning to become a “culture dependent
language specialist”, it is already done


Drawbacks include:


No verbal emotional cues (face/voice matching active
within first few weeks, so might be important)


No distinguishing different people from voice alone


My Approach


cont.


Speech output done by TSS engine


Benefits include:


Speech production learning is unnecessary


We can still produce emotional output in the speech for
the benefit of listener

Baby
LyLy


Purposes:


Playmate for Snow


Interact with Snow the way Snow interacts


Mimicry


of words, micro
-
sentences


Association


one word recognized may induce another word


Imitation


repeat last word(s) of longer sentence


Repetition


repeat one word over and over until repeated


Emotional


frustration, happiness, boredom, clinical/serious


Acquire words and micro
-
sentence use similar to Snow


Only verbal interface for learning


Restrict teaching and feedback to speech


Discover what is necessary in such an interface

Adult Lyly


Purposes:


Productive interaction with OS/humans


Hand
-
coded knowledge and conversation protocols


Innate knowledge of environment and use of some web


Integration with various tools (like email and schedule)


Specialized grammar and modules


Kitchen
-

recipe, calorie, timers


Living room


TV schedules, movie database, lyric lookup


Office


web search, question answering, calculator


Bedroom


read stories, play games


Assist in development of high
-
level knowledge
representation and reasoning framework


Needs dictate KR scheme


Needs dictate specialized AI functions and tools to use


Additional Considerations


Forms of communication from computer to
human we can exploit (common references)


Visual


Facial emotions and actions


Diagrams, pictures, timelines, representations


Auditory


Speech


with prosody


Recorded sound effects and speech


Other


Sliders/progress bars to indicate intensity of emotion,
how well something is understood, certainty

Demonstration of Interface