SPltiCdiSparse Population Coding: Toward Brain-Like Machine Learning › ~scai › Courses › CNC2013 › 01-1... · 2015-11-24 · • Evolutionary Learning Latent Variable Models

S P l ti C diSparse Population Coding: Toward Brain-Like Machine Learning

IPIU, JejuIPIU, JejuFebruary 18, 2013

Byoung-Tak ZhangComputer Science and Engineering &

Cognitive Science and Brain Science ProgramsSeoul National UniversitySeoul National University

Biointelligence Laboratoryhttp://bi.snu.ac.kr/

OutlineOutlineOutlineOutline

Introduction Introduction• Artificial Intelligence and Machine Learning• Why Brain-Like Machine Learning?y g

Hypernetworks• Sparse Population Coding• Learning and Inference

Applications• Language, Music, and Robot Motions • Human Mobile Life

C iti E i t Cognitive Experiments• Videome: Learning from Videos• EEG and Eye-Hand Tracking StudiesEEG and Eye Hand Tracking Studies

(c) 2005-2012 SNU Biointelligence Laboratory, http://bi.snu.ac.kr/

2

TuringTuring’’s Dream of Intelligent Machiness Dream of Intelligent Machinesgg gg

Alan Turing(1912-1954)


3Computing Machinery and Intelligence (1950)

1997: IBM “Deep Blue” Chess Machine1997: IBM “Deep Blue” Chess Machine1997: IBM Deep Blue Chess Machine1997: IBM Deep Blue Chess Machine


4

2011: IBM “Watson” Quiz Machine2011: IBM “Watson” Quiz Machine2011: IBM Watson Quiz Machine2011: IBM Watson Quiz Machine


5

2011: Apple “2011: Apple “SiriSiri” Personal Assistant” Personal Assistant2011: Apple 2011: Apple SiriSiri Personal Assistant Personal Assistant


6

Machine LearningMachine Learning

What Is Learning?

• Improving

What Is Learning?

p gperformance

• by knowledge acquisition

• through experienceexperience

• from interaction • with an

environment

• Machine vs. human• Semantic vs. skill• Symbolic vs statistical• Symbolic vs. statistical

Machine learning (ML): Three tasksMachine learning (ML): Three tasks

• Supervised LearningSupervised Learning– Estimate an unknown mapping from known input and target output

pairs– Learn fw from training set D = {(x,y)} s.t. )()( xxw fyf

w g {( ,y)}– Classification: y is discrete– Regression: y is continuous

• Unsupervised Learningp g– Only input values are provided– Learn fw from D = {(x)} s.t.– Compression

xxw )(f

– Clustering• Reinforcement Learning

– Not target, but rewards (critiques) are provided “sequentially”– Learn a heuristic function fw from Dt = {(st,at,rt) | t = 1, 2, …} s.t.– Sequential decision-making– Action selection

l l

( , , )t t tf a rw s

2012 (c) SNU Biointelligence Lab, http://bi.snu.ac.kr/

– Policy learning

Zhang, B.-T., Next-Generation Machine Learning Technologies, Communications of KIISE, 25(3), 2007 9

Machine Learning: An ExampleMachine Learning: An ExampleError Backpropagation

Machine Learning: An ExampleMachine Learning: An Example

EOutput Comparison

Information Propagation otwE 2)(1)( i

iiii wEwwww

,

p g

Input x1 Weights

outputsk

kkd otwE )(2

)(

Input x2 Outputx )(xfo

Input x3

Activation FunctionScaling FunctionInput Layer Hidden Layer Output Layer

Activation Function

(c) 2005-2012 SNU Biointelligence Laboratory, http://bi.snu.ac.kr/ 10

Application Example:Application Example:Autonomous Land Vehicle (ALV)Autonomous Land Vehicle (ALV) NN learns to steer an autonomous vehicle NN learns to steer an autonomous vehicle. 960 input units, 4 hidden units, 30 output units

D i i t d t 70 il h Driving at speeds up to 70 miles per hour

ALVINN SystemCMU

Weight values

Image of aforward -mounted

for one of the hidden units

mountedcamera

(c) 2005-2012 SNU Biointelligence Laboratory, http://bi.snu.ac.kr/ 11

2009: Google “Self2009: Google “Self--Driving Car”Driving Car”2009: Google Self2009: Google Self Driving CarDriving Car

DARPA Grand Challenge (2005) DARPA Grand Challenge (2005) DARPA Urban Challenge (2007)

G l S lf D i i C (2009) Google Self-Driving Car (2009)


12

기계학습의기계학습의 응용응용 분야분야기계학습의기계학습의 응용응용 분야분야

응용 분야 적용 사례

인터넷 정보검색 텍스트 마이닝 웹로그 분석 스팸필터 문서 분류 여과 추출 요약 추천인터넷 정보검색 텍스트 마이닝, 웹로그 분석, 스팸필터, 문서 분류, 여과, 추출, 요약, 추천

컴퓨터 시각 문자 인식, 패턴 인식, 물체 인식, 얼굴 인식, 장면전환 검출, 화상 복구

음성인식/언어처리 음성 인식, 단어 모호성 제거, 번역 단어 선택, 문법 학습, 대화 패턴 분석음성인식/언어처리 음성 인식, 단어 성 제거, 번역 단어 선택, 문법 학습, 대화 패턴 분석

모바일 HCI 동작 인식, 제스쳐 인식, 휴대기기의 각종 센서 정보 인식, 떨림 방지

생물정보 유전자 인식, 단백질 분류, 유전자 조절망 분석, DNA 칩 분석, 질병 진단

바이오메트릭스 홍채 인식, 심장 박동수 측정, 혈압 측정, 당뇨치 측정, 지문 인식

컴퓨터 그래픽 데이터기반 애니메이션, 캐릭터 동작 제어, 역운동학, 행동 진화, 가상현실

로보틱스 장애물 인식, 물체 분류, 지도 작성, 무인자동차 운전, 경로 계획, 모터 제어

서비스업 고객 분석, 시장 클러스터 분석, 고객 관리(CRM), 마켓팅, 상품 추천

제조업 이상 탐지 에너지 소모 예측 공정 분석 계획 오류 예측 및 분류

13

제조업 이상 탐지, 에너지 소모 예측, 공정 분석 계획, 오류 예측 및 분류

장병탁, 차세대 기계학습 기술, 정보과학회지, 25(3), 2007(c) 2005-2012 SNU Biointelligence Laboratory, http://bi.snu.ac.kr/

Machine Learning:Architectures and Algorithms

• Symbolic Learning Probabilistic Learning

g

y g– Version Space Learning– Case-Based Learning

N l (C ti i t) L i

Probabilistic Learning Bayesian Networks Hidden Markov Models Helmholtz Machines• Neural (Connectionist) Learning

– Multilayer Perceptrons– Self-Organizing Maps

Helmholtz Machines Markov Random Fields Conditional Random Fields

L t t V i bl M d l– Support Vector Machines– Kernel Machines

• Evolutionary Learning

Latent Variable Models Generative Topographic Mapping Topic Modelsy g

– Evolution Strategies– Evolutionary Programming

G ti Al ith

Other Methods Decision Trees Reinforcement Learning

– Genetic Algorithms– Genetic Programming– Molecular Programming

g Boosting Algorithms Mixture of Experts Independent Component

2012 (c) SNU Biointelligence Lab, http://bi.snu.ac.kr/

Independent Component Analysis

14

Towards HumanTowards Human--Level Level Artificial IntelligenceArtificial IntelligenceggCreative Uncertain

Adaptive Inattentive

Sociable Emotional

Versatile Illogical

To achieve a true human-level intelligence, brain-like information

1 + 2 = 5 !100 < 10 ?

15

To achieve a true human level intelligence, brain like information processing is required.


Toward Brain Like Machine LearningToward Brain-Like Machine Learning

MindMindTechnological Construction

BrainBrainCellCell

MindMind

MoleculeMolecule

10101111 CellsCells44

∞ ∞ MemoryMemory

>10>1044 MoleculesMoleculesScientific Understanding

Neural Representations and ProcessingNeural Representations and ProcessingNeural Representations and ProcessingNeural Representations and Processing

“Chemical” and “molecular” basis of synapsesy p Distributed representation Multiple overlapping representationsp pp g p Hierarchical representation Associative recallAssociative recall Population coding Assembly coding Assembly coding Sparse coding Temporal coding Temporal coding Synfire chain Dynamic coordination Dynamic coordination Correlation coding 17


Brain-Scale Structural and Functional Networks

18Hagmann et al., 2010

Brain Principle: Sparse Coding in Neural Populationsp g p

[O’Connor et al.,[O Connor et al.,Neuron, 2010]

19

Sparse Population Coding: Maradona CellsMaradona Cells

[Quiroga, Nat Rev Neurosci. 2012 ]

Sparse Population Coding: Basic IdeaBasic Idea

[Quiroga, Nat Rev Neurosci. 2012 ]

Brain Principle: Probabilistic Inference with Population Codesp

[Knill and Pouget, Trends in Neurosciences, 2004]

HypernetworksHypernetworks::HypernetworksHypernetworks: : Towards BrainTowards Brain--Like LearningLike Learning

Zh B T IEEE C i l I lliZhang, B.-T., IEEE Computational Intelligence Magazine, 3(3): 49-63, 2008

Population Coding and AssemblyPopulation Coding and AssemblyPopulation Coding and AssemblyPopulation Coding and AssemblyAssembly Codes

Encoding

3. Module Discovery


24

Decoding

HypernetworkHypernetwork Coding: Population of Coding: Population of HyperedgesHyperedges

E1 E3

v1 2H = (V, E, W)V = {v1 v2 v3 v7}

E1 E3

v1 v2V = {v1, v2, v3, …, v7}E = {E1, E2, E3, E4, E5}W = {w1, w2, w3, w4, w5}

E4E2v3E1 = {v1, v3, v4}

E2 = {v1, v4}

E4E2

v5 v6

v4{ }

E3 = {v2, v3, v6}E4 = {v3, v4, v6, v7}E5 = {v4, v5, v7}

7

E5 {v4, v5, v7}

E525

v7E5


x1=1

x2=0

x3=0

x4=1

x5=0

x6=0

x7 =0

x8=0

x9=0

x10=1

x11=0

x12=1

x13=0

x14=0

x15=0

y= 1

x1=0

x2=1

x3=1

x4=0

x5=0

x6=0

x7 =0

x8=0

x9=1

x10=0

x11=0

x12=0

x13=0

x14=1

x15=0

y= 0

1

2

Datax1=0

x2=0

x3=1

x4=0

x5=0

x6=1

x7 =0

x8=1

x9=0

x10=0

x11=0

x12=0

x13=1

x14=0

x15=0

y=1

4 Data Items3

x1=0

x2=0

x3=0

x4=0

x5=1

x6=1

x7 =0

x8=1

x9=0

x10=0

x11=1

x12=0

x13=0

x14=0

x15=1

y=04

x1x2 x15x4 x10 y=1x1

x4 x12 y=1x11 Round 1Round 2Round 3x3 x14

x4 x12 y 1x1

x10 x12 y=1x4

x3 x9 y=0x2

1

x12

x4 x13x3 x14 y=0x2

x9 x14 y=0x3

2

x12

x5x6 x8 y=1x3

x6 x13 y=1x3

x x y=1x3

x6 x11

x8 x13 y=1x6

x6 x8 y=0x5

4 x8 x11 y=0x6

© 2010, SNU Biointelligence Lab, http://bi.snu.ac.kr/

26x8 x9

x7x10

8 11 y6

x11 x15 y=0x8


26

HypernetworkHypernetwork as a Probabilistic Model of as a Probabilistic Model of DistributedDistributed Parallel Associative MemoryParallel Associative Memory

x1 xxxXWSXH

I,...,, )( ),,(

as definedisrk hypernetwoThe

21[Zhang, DNA-2006]

[Zhang, IEEE CIM, 2008]

Distributed Distributed Parallel Associative MemoryParallel Associative Memory

x1x2

x3 x14

x15

K

iii

i

WWWW

SkXSSS

)()3()2(

tT i i),...,,(

|| , ,

[Zhang, IEEE CIM, 2008]

x4 x13 11rkhypernetwo theofenergy The

NnD 1)( }{ :setTraining

x

321

32132121

2121

)()(

,,

)()()()3(

,

)()()2()(

)];(exp[1 )|(

ondistributiy probabilit The

...61

21 );(

iiiiiiiiii

nn

iii

nnn

ii

nnn

WEWP

xxxwxxwWE

xx

x

x5 x12

321321321

212121

,,

)()()()3(

,

)()()2(

11

...61

21exp

)Z(1

)];(p[)Z(

)|(

iiiiiiiiii

K

iii

nnn

ii

nn xxxwxxwW

W

x6 x11

2121...21

)()()()(

2 ,...,,

)()()()(

1exp)Z(

isfunction partition thewhere

,...)(

1exp)Z(

1 k

kiiikiii

K mmmk

k iii

nnnk

xxxwW

xxxwkcW


27

x7

x8 x9

x10

)( 21

21...212 ,...,,...

)(exp)Z(

m kkiiikiiik iii

xxxwkc

Wx

Brain-Like Propertiesin Hypernetworksyp

Sparse assembly Flexible structure

Analogy to Neuroanatomy Flexible structure

Local insert/delete Fast update

• Variable = Minicolumn

Fast update Partial overlapRobust decision

• Hyperedge= Hypercolumn

Robust decision Large populationStable estimation

= Collection of minicolumns• Hypernetwork

Stable estimation Reassembly Dynamic change

= Network of hypercolumns= Cell assembly

Dynamic change Probabilistic model R ll

y• Layered hypernetwork

= Chain of cell assemblies Recall memory

Comparison to Other ML ModelsComparison to Other ML ModelsComparison to Other ML ModelsComparison to Other ML Models

Higher-order terms Explicit representation fast learning

CompositionalityCreation of new modules symbolic computation fast learning

cf. Bayesian networks

Structural learning

symbolic computation cf. connectionist models

Self-supervised Structural learningEvolving complex networks discovery of modules cf Markov random fields

Self supervised Can learn from unlabeled data no need for labeling

cf supervised cf. Markov random fields

Population codingCollection of modules

cf. supervised

Reconfigurable architecture Run time self assemblyCollection of modules

incremental learning cf. numerical CPT

Run-time self-assembly anytime inference

cf. fixed architecture


29

Paradigms in AI and Cognitive ScienceParadigms in AI and Cognitive ScienceParadigms in AI and Cognitive ScienceParadigms in AI and Cognitive Science

SymbolismSymbolism ConnectionismConnectionism DynamicismDynamicism HyperinterHyperinter--ti iti iyy yy actionismactionism

MetaphorMetaphor symbolsystem

neuralsystem dynamical system biomolecular

system

MechanismMechanism logical electrical mechanical chemical

DescriptionDescription syntactic functional behavioral relational

R t tiR t ti l li t di t ib t d ti ll tiRepresentationRepresentation localist distributed continuous collective

OrganizationOrganization structural connectionist differential combinatorial

AdaptationAdaptation substitution tuning rate change self-assemblyAdaptationAdaptation substitution tuning rate change self-assembly

ProcessingProcessing sequential parallel dynamical massively parallel

StructureStructure procedure network equation hypergraphStructureStructure procedure network equation hypergraph

MathematicsMathematics logic, formallanguage

linear algebra,statistics geometry, calculus graph theory,

probabilistic logic

Space/timeSpace/time formal spatial temporal spatiotemporal

30

Space/timeSpace/time formal spatial temporal spatiotemporal

[Zhang, IEEE Computational Intelligence Magazine, August 2008]


ApplicationsApplications

• Language Learningg g g• Music Learning• Robotic Motions• Robotic Motions

Text Corpus: TV Drama SeriesText Corpus: TV Drama Series

Friends, 24, House, Grey Anatomy, Gilmore Girls, Sex and the CityI don't know what happened.Take a look at this.…

What ? ? ? here.? have ? visit the ? room.

…

289 468 700 Sentences

32

289,468 Sentences

(Training Data)

700 Sentenceswith Blanks(Test Data)


32( a g ata) ( es )

Sentence Completion TaskSentence Completion Task

? gonna ? upstairs ? ? a shower I'm gonna go upstairs and take a shower

? have ? visit the ? room I have to visit the ladies' room

? still ? believe ? did this I still can't believe you did this

? appreciate it if ? call her by ? ?

? ? ? decision to make a decision

pp y I appreciate it if you call her by the way

Would you ? to meet ? ? Tuesday ? Would you nice to meet you in Tuesday and

Why ? you ? come ? down ? Why are you go come on down here

33

Concept Maps for Concept Maps for FriendsFriends and and Prison BreakPrison BreakCorpus: FriendsKeyword: “mother”

Corpus: Prison BreakKeyword: “mother”

you're mother killed herselfit's my mother was shot by a woman at eightwe're just gonna go to your mother that i love it

tells his mother and his familyshe's the mother of my eyesspeak to your mother used to bewe're just gonna go to your mother that i love it

feeling that something's wrong with my mother and fathershe's the single motheri put this on my friend's motherapparently phoebe's mother killed herselfthanks for pleasing my mother killed herselfi'm your mother told you thisi i dibl h

speak to your mother used to betells his mother made it pretty clear on the floor hasspeak to your mother never had life insuranceshe's the mother of lincoln's childshe's the mother of my own crap to deal with youjust lost his mother is finejust lost his mother and his god

ll hi h d hi f hy y

is an incredible motherthat's not his mother or his hunger strikeholy mother of god womani like your mother and father on their honeymoon suitewith her and never called your mother really did like usis my mother was shot by a drug dealer

j gtells his mother and his stepfathershe's the mother of my timehis mother made it clear you couldn't deliver fibonaccishe's the mother of my brother is facing the electric chairsame guy who was it your mother before you do itthey gunned my mother down


34[J.-H. Lee et al., 2009]

Music LearningMusic Learning

Learning to generate melody (sequence of pitch & duration) Learning to generate melody (sequence of pitch & duration) Learning a model that recalls any part of the melody from

various songs given some cuevarious songs given some cue

?HyperMuse

HN( ) ( ) ( ; )P w P w f u u wh hu w

( ) ( ) ( ; )fu u w

h: hyperedgeshw: weights of hyperedges

u' w

''

The next symbol w after some context u can be determined based on weights of the ‘pool of hyperedges’ that


35

u'' wVariable-order context is used to predict w

of the pool of hyperedges that matches the context

Music Generation Result: BeatlesMusic Generation Result: Beatles

0.9

0.95

Setting former/latter half as the training/test set

0.65

0.7

0.75

0.8

0.85

hit ratio(a)

Training set Test setSong 1Song 2

trainingtest

Setting former/latter half as the training/test set

0 9

1

0 10 20 30 40 50 60 70 80 90 1000.55

0.6

epoch

… …Song 2

Song n

Charts(a) Learning curve (1~100 epoch)(b) Hit ratio vs. max order(c) Evolution of the structure of the model 0.5

0.6

0.7

0.8

0.9

hit r

atio

trainingtest

(b)( )

2 4 6 8 10 12 14 16 18 200.2

0.3

0.4

maximum order

1200

All My Loving (good prediction)Generation Results Original / Generated

600

800

1000

1200

ered

ges

per o

rder

order = 2order = 3order = 4order = 5order = 6order = 7

d 8

(c)

Eleanor Rigby (good prediction) Dear Prudence (transit to another song) Yellow Submarine (transit to another song)


360 20 40 60 80 100

0

200

400

epoch#h

ype

order = 8order = 9order = 10 Yesterday (fall in a large loop)

Hey Jude (fall in a monotonic pattern)

Music Generation Result: Cross-CorpusMusic Generation Result: Cross Corpus

Scores generated by Evolutionary Hypernetworks that learnedScores generated by Evolutionary Hypernetworks that learned American (A), Scottish (B), Korean Singer Kim (C), and Korean Singer Shin (D) with the cue (left side of the bar in the middle)Singer Shin (D) with the cue (left side of the bar in the middle)from “Swanee River”, the famous American folk song

[H.-W. Kim and B.-H. Kim]


37

로봇로봇 실험실험 데이터데이터로봇로봇 실험실험 데이터데이터 데이터: Darwin-OP 로봇의 걷는 모션 데이터 [J S Kim]데이터 봇의 데이터 수집 방법: 로봇의 걷는 행동에 대한 각도 값을 기록 수집 주기: 40 ms당 한 번 (25Hz) 데이터 수: 2215개 스텝으로 구성된 시퀀스

[J.-S. Kim]

데이터 차원 (축의 개수): 20개의 Revolute Joint Angle (0~360도)

100

150

−100

−50

0

50

y

100

−200

−150

−100


38−100

−50

0

50

100−50

0

50

xz

Motion GenerationMotion GenerationMotion GenerationMotion Generation[J.-S. Kim]


39

실험실험 결과결과실험실험 결과결과 [J.-S. Kim]

t = 150 t = 900t = 30

100

150

Frm:30

100

150

Frm:150

100

150

Frm:900

−50

0

50

100

y

−50

0

50

100

y

−50

0

50

100

y

−150

−100

−50

−150

−100

−50

−200

−150

−100

−50

−100

−50

0

50

100

−50

0

50

−200

xz

−100

−50

0

50

100−50

0

50

−200

xz

−100

−50

0

50

100−50

0

50

−200

xzz xz z


40

Mobile Phone ApplicationsMobile Phone Applications

• mLife• eHealth

M bil S M bil S S t hS t hMobile Sensors on Mobile Sensors on SmartphonesSmartphones일상 을 통한 사용자의 행동패턴 인식 및 추천 일상 Logging을 통한 사용자의 행동패턴 인식 및 추천

Android 스마트폰을 이용하여 Real-life logging data 를 수집 (삼성Galaxy S 시리즈, HTC Desire)Ga a y S 시리 , C es e)

센서 정보

GPS 절대 위치 정보

Accelerometer 3D 축을 기준으로 가속도 크기 및 방향

Proximity 단말 가까이에 물체의 존재 유무

O i t ti 단말 정면을 기준으로 ll과 it h 값Orientation 단말 정면을 기준으로 roll과 pitch 값

Magnetic fields 자기장 센서

Illuminometer 조도센서

Sound Noise Noise sound의 크기

Bluetooth Bluetooth device address

WIFI SSID 명, 신호 세기

42Action Logger

(MDS)

WIFI SSID 명, 신 세기

(c) 2012 SNU Biointelligence Laboratory, http://bi.snu.ac.kr/

Mobile Sensor DataMobile Sensor DataMobile Sensor DataMobile Sensor Data


집교회학교 학교집 학교낙성대

학교집 학교신림동 집 회학교집 학교신림동 집교회

교회집 학교집 집교회


44

출발점출발점 기준으로기준으로 도착지점에도착지점에 대한대한예측확률예측확률 변화변화예측확률예측확률 변화변화

1일 후 6일 후 11일 후 13일 후 18일 후 20일 후 학습완료 후(33일 데이터 반영)

집집집집

신림역신림역

집집

신림역신림역

교회교회

학교학교 학교학교

교회교회

집집 집집

교회교회

신림역신림역신림역신림역

교회교회


학교학교 학교학교

User Scenario

John’s daily-lifeJohn s daily lifeand

DietAdvisorDietAdvisor

DietAdvisor: A Personalized eHealth Agent in a Mobile Computing Environment 46

Experimental Results: Activity Recognition


Personalized Recommendation Module

Hypernetwork-based learning for menuWeight Item1 Item2

15 쌀밥 배추김치

3 김 깍두기

Weight Item1 Item2

15 쌀밥 배추김치

3 김 깍두기3 김 깍두기

2 현미밥 버섯볶음

4 계란찜 숙주나물

3 김 깍두기

3 현미밥 버섯볶음

4 계란찜 숙주나물

4 쌀밥 두부조림

1 배추김치 깍두기

4 쌀밥 두부조림

1 배추김치 깍두기

3 부추김치 장조림

5 마른김 양념간장

6 현미밥 배추김치

3 부추김치 장조림

5 마른김 양념간장

7 현미밥 배추김치Learning Late: 50%6 현미밥 배추김치

3 탕수육 군만두

4 현미밥 북어국

7 현미밥 배추김치

3 탕수육 군만두

4 현미밥 북어국

Learning Late: 50%


0 X X 1 북어국 버섯볶음

Experimental Results: Menu Recommendation


VideomeVideome: Learning from Videos: Learning from Videos

C S i 2012 Zh t lCogSci-2012, Zhang et al.

50

Digital Videos for Teaching MachinesDigital Videos for Teaching MachinesDigital Videos for Teaching MachinesDigital Videos for Teaching Machines

M l i d l Multimodal Language Vision Vision Audio

“Situated” Situated Contexts “Naturalistic” Naturalistic Dynamic “Quasireal” Quasireal Continuous Educational


51

Educational

LEARNING BY PLAYINGLEARNING BY PLAYINGL i h i f h iLearning the image from the given text

Text QueryText Query

Click the Right Option

Score : 01

L i h f h i i

LEARNING BY PLAYINGLEARNING BY PLAYINGLearning the text from the given image

Image Query

Click the Right Option

53Score : 02

Result 1:Result 1: HumansHumans forfor T2IT2I LearningLearningResult 1: Result 1: Humans Humans for for T2I T2I LearningLearning

0.95

1

0 8

0.85

0.9

0.7

0.75

0.8Player 1

Player 2urac

y

0.6

0.65

Acc

u

0.5

0.55

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15


No. of Sessions

Result 2:Result 2: HumansHumans forfor I2TI2T LearningLearningResult 2: Result 2: HumansHumans for for I2T I2T LearningLearning

0.95

1

0 8

0.85

0.9

urac

y

0.7

0.75

0.8Player 1

Player 2

Acc

u

0.6

0.65

0.5

0.55

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15


No. of Sessions

Result 3:Result 3: MachinesMachines forfor I2TI2T LearningLearning

1

Result 3: Result 3: MachinesMachines for for I2T I2T LearningLearning[Fareed et al., 2009]

0.9

0.95

1

0.75

0.8

0.85

0.6

0.65

0.7

urac

y

0 45

0.5

0.55

0.6

Acc

u

0.4

0.45

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105


No. of Epochs

ImageImage--toto--Text Recall ExamplesText Recall Examples

Matching &

ImageImage toto Text Recall ExamplesText Recall Examples

AnswerQuery

I don't know

Matching &Completion

I don't know what happened

I don't knowdon't know what

know what happened

There's a kitty in my There's a

a kitty in y yguitar case

y…in my guitar case

Maybe there's something I can do to

Maybe there's somethingthere's something I

gmake sure I get pregnant

…I get pregnant


TextText--toto--Image Recall ExamplesImage Recall Examples

Matching &

TextText toto Image Recall ExamplesImage Recall Examples

Query Matching &Completion Answer

I don't know what happened

Take a look at this

There's a kitty in my guitar case

Maybe there's something I can do to make sure I get pregnant


Word Learning from VideoWord Learning from Video

MaisyMaisy ABCABC

© 2012 © 2012 BiointelligenceBiointelligence LabLab Seoul National UniversitySeoul National University5959

Word Learning from VideoWord Learning from Video

UtteranceUtterance--scene representationscene representationOriginal sentence scene pairs Vis al ords Te t al ordsOriginal sentence-scene pairs

rabbit, followed,

Visual words Textual words

Oh, the rabbit's followed you home, Maisy.

home, maisy

forget, panda

Oh, and don't forget panda.

Good night, bird. See you

good, night, bird,see, morning


g yin the morning.

Sparse Population Sparse Population CCode Modelsode Models

Concept representationConcept representation[Zhang et al., CogSci-2012]

wv longwred

w

v

v

yellow

ear

v

run

w w

mouse v

v

w earw

w v

tail

dark

vw

eye

v

v

wtail eye

v

w hop

Concept map for MOUSE Concept map for RABBIT


Concept map for MOUSE Concept map for RABBIT

Experimental ResultsExperimental Results

Emergence patterns of Emergence patterns of conceptsconceptsS( ) S( )

[Zhang et al., CogSci-2012]

0 5

0.6 Max(|H|) = 500

Max(|H|) = 100

S(v)

60

70 rabbitbirdmouse

S(w)

0.4

0.5

50

60

0.330

40

0.1

0.2

10

20

00 300 600 900t

00 300 600 900t


Memory capacity Three visual concepts


Concept Concept generalization generalization and and specializationspecialization[Zhang et al., CogSci-2012]

lunchrabbit

rabbitsix

goodnight

pandayellow

one

enjoy

carrot

love

night

maisy

idea

love maisybird

Episode 1 Episodes 1-2


Episode 1 p


Concept generalization and specialization (cont’d)Concept generalization and specialization (cont’d)[Zhang et al., CogSci-2012]

birdnight

morning

dtreebird

rabbit little

ideagood

want look

helping

tree

digging penguin

f itonion

excited

today

holerabbit

ride

maisy

want

need

penguin

favoriteonion today

doingfarm

watermaisy

new

Episodes 1-4 Episodes 1-6


Episodes 1 4 p

EEG and EyeEEG and Eye Hand Tracking StudiesHand Tracking StudiesEEG and EyeEEG and Eye--Hand Tracking StudiesHand Tracking Studiesfor Cognitive Machine Learningfor Cognitive Machine Learning

C S i 2012 L lCogSci-2012, Lee et al.CogSci-2012, Kim et al.

EyeEye--Hand TrackingHand Tracking: Experiment : Experiment DesignDesignyy gg pp gg

Eye MovementHand Movement

• Arrington research• 60FPS• Binocular

• Javascript• 30FPS

66

[E.-S. Kim]


EyeEye--Hand TrackingHand TrackingEyeEye Hand TrackingHand TrackingRed: Mouse (Hand) MovementBlue: Eye Movement

[E.-S. Kim]


67

EyeEye--Hand MovementsHand MovementsEyeEye Hand MovementsHand MovementsRed: Mouse (Hand) MovementBlue: Eye Movement

68

[E.-S. Kim]


0 925

정답률문제 유형별 정답률

0.885

0.925

0.725 0.755

0.835

문제 유형별 정답률- T2I, I2T의 정답률 비교: 차이가

있어보이나 유의미한 차이인지t-test 필요필

- 다음 내용을 맞추는 문제들에서, pair로 정보를 줄 경우, 정답률이많이 높아짐

Reaction Time (ms)T2I I2T nextImage nextText nextPair

10308 반응 속도의 비교

68346201

7577

103089530

Correct

Wrong

반응 속 의 비- 전체적으로 틀린 문

제에서 반응 시간이큼

6201Average - T2I, I2T, nextPair 유

형에서 맞고 틀리는문제 사이의 반응

T2I I2T nextImage nextText nextPair

시간 차이가 큼[E.-S. Kim]


69

EEG Experimental ResultsEEG Experimental Results

Sti li


Stimuli:• An episode of TV

sitcom, Friends (5th episode of 10th season; 27 min.)

• 20 video clips extracted from the movie were served as retrieval cue

• A task S/W is developed to synchronize the experiment with EEG recordingrecording

EEG Analysis:• Neuroscan SynAmps

amplifier with 128-pchannel Quik-cap

• Analysis using SPSS and EEGLAB toolbox (http://sccn.ucsd.edu/eegl

Schematic depicting the experimental paradigm(http://sccn.ucsd.edu/eeglab)

[C.-Y. Lee](c) 2005-2012 SNU Biointelligence Laboratory, http://bi.snu.ac.kr/

70

EEG Experimental ResultsEEG Experimental Results Theta activation on the left parietal lobe (C3 and CP1), and gamma activation on the temporal lobes

(T7 and T8) were increased during overall memory formation


(T7 and T8) were increased during overall memory formation. However, activations on the frontal lobes and the occipital lobes were more increased in retrieval

sessions than in encoding sessions.

The grand-average topographical distribution of theta and gamma frequency power during the2 experimental conditions (memory encoding session and retrieval session) [C.-Y. Lee]


71

EEG Experimental ResultsEEG Experimental Results Correlates of neural activation and reaction time (RT) of subjects in query tasks were

b d i th f l i f EEG i l


observed in the frequency analysis of EEG signals. Theta power on the left central cortex and gamma power on the occipital cortex were

increased in tasks with sustained RT in contrast to tasks with instant RT.

[C.-Y. Lee]The grand-average of topography about theta and gamma frequency power values in query tasks considering the subjects’sustained / instant RT and their difference


72

Conclusion:Bio-Inspired Human-Level Machine Learning

MindMindTechnological Construction

BrainBrainCellCell

MindMind

MoleculeMolecule

10101111 CellsCells44

∞ ∞ MemoryMemory

>10>1044 MoleculesMoleculesScientific Understanding

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