Transcript PowerPoint 프레젠테이션 - 뇌-마음

Brain, Mind, and Computation
Part II: Brain-Inspired Computation
Brain-Mind-Behavior Seminar
May 26, 2010
Byoung-Tak Zhang
Biointelligence Laboratory
Computer Science and Engineering &
Brain Science, Cognitive Science, and Bioinformatics Programs &
Brain-Mind-Behavior Concentration Program
Seoul National University
http://bi.snu.ac.kr/
Lecture Overview

Part I: Computational Brain
 How the brain encodes and processes information?

Part II: Brain-Inspired Computation
 How to build intelligent machines inspired by brain processes?

Part III: Cognitive Brain Networks
 How the brain networks perform cognitive processing?
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(c) 2009 SNU Biointelligence Laboratory, http://bi.snu.ac.kr/
Artificial Intelligence
Can Machines Think?
Alan Turing
(1912-1954)
Computing Machinery and Intelligence (1950)
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Chess Playing
Deep Blue beat G. Kasparov in 1997
Garry Kasparov and Deep Blue. © 1997
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DARPA Grand Challenge
기계학습 기반 무인자동차 운전 기술
[Sebastian Thrun, Stanley & Junior, Stanford Univ.]
Stanford 팀은 무인자동차의 자동운전 기술에 기계학습 기법을 활용하여 2005년도 Grand
Challenge에서 우승(상금 2백만 달러), 2007년도 Urban Challenge에서 준우승을 차지하였다.
2005년도 미션: 사막지역 175마일을
자동운전만으로 10시간 이내에 주파
2007년도 미션: 도시환경에서 96km를
자동운전만으로 6시간 이내에 주파
지형 파악 및 진행경로 계획
Video
DARPA Grand Challenge:
Final Part 1
레이저를 이용한 지형 파악
사람의 운전 패턴을 학습
사람
확률적모델링
자동차
© 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/
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Natural Language Processing
Videos: ALICE Artificial Intelligence and Nicole
Talking Robot: Android Video

Polysemy
 I keep the money in the bank.
 I walk along the bank of the river.

Ambiguity
 Time flies like an arrow.
 I saw a man with a telescope.

Diversity
 She sold him a book for five dollars.
 He bought a book for five dollars from her.

Related Knowledge
 Lexical, Grammatical, Situational, Contextual
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(c) 2000-2007 SNU CSE Biointelligence Lab, http://bi.snu.ac.kr/
What is Artificial Intelligence?

AI is a collection of hard problems which can be solved by
humans and other living things, but for which we don’t have
good algorithms for solving.
 e. g., understanding spoken natural language, medical diagnosis,
circuit design, learning, self-adaptation, reasoning, chess playing,
proving math theories, etc.

Definition from R & N book: a program that
 Acts like human (Turing test)
 Thinks like human (human-like patterns of thinking steps)
 Acts or thinks rationally (logically, correctly)

Some problems used to be thought of as AI but are now
considered not
 e. g., compiling Fortran in 1955, symbolic mathematics in 1965,
pattern recognition in 1970
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(c) 2000-2007 SNU CSE Biointelligence Lab, http://bi.snu.ac.kr/
History of AI

Early enthusiasm (1950’s & 1960’s)
 Turing test (1950)
 1956 Dartmouth conference
 Emphasize on intelligent general problem solving

Emphasis on knowledge (1970’s)
 Domain specific knowledge
 DENDRAL, MYCIN

AI became an industry (late 1970’s & 1980’s)
 Knowledge-based systems or expert systems
 Wide applications in various domains

Searching for alternative paradigms (late 1980’s - early 1990’s)
 AI’s Winter: limitations of symbolic/logical approaches
 New paradigms: neural networks, fuzzy logic, genetic algorithms, artificial life

Resurge of AI (mid 1990’s – present)
 Internet, Information retrieval, data mining, bioinformatics
 Intelligent agents, autonomous robots

Recent trends:
 Probabilistic computation
 Biological basis of intelligence
 Brain research, cognitive science
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(c) 2000-2007 SNU CSE Biointelligence Lab, http://bi.snu.ac.kr/
Artificial Intelligence (AI)
Symbolic AI
Rule-Based Systems
Connectionist AI
Neural Networks
Evolutionary AI
Genetic Algorithms
Molecular AI:
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DNA Computing
(c) 2000-2007 SNU CSE Biointelligence Lab, http://bi.snu.ac.kr/
Research Areas and Approaches
Research
Artificial
Intelligence
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Learning Algorithms
Inference Mechanisms
Knowledge Representation
Intelligent System Architecture
Application
Intelligent Agents
Information Retrieval
Electronic Commerce
Data Mining
Bioinformatics
Natural Language Proc.
Expert Systems
Paradigm
Rationalism (Logical)
Empiricism (Statistical)
Connectionism (Neural)
Evolutionary (Genetic)
Biological (Molecular)
(c) 2000-2007 SNU CSE Biointelligence Lab, http://bi.snu.ac.kr/
바이오지능: “분자”에서 “인지”까지
인지(Cognition)
Mind
뇌(Brain)
세포(Cell)
 memory
분자
(Molecule)
1011 cells
1010 mol.
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© 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/
바이오지능: 네가지 컴퓨터 모델

Neural Computing
 전기적 신호전달
 “뉴런망”을 닮은 컴퓨터

Genetic Computing
 유전정보의 재결합
 “진화”하는 컴퓨터

Molecular Computing
 화학적 자기조립
 “분자망/시냅스망”을 닮은
컴퓨터

Cortical Computing
 전기화학적 신호전달
 “셀어셈블리망” 컴퓨터
© 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/
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Neural Computing
What Is a Neural Network?

A new form of computing, inspired by
biological (brain) models.
 A mathematical model composed of a
large number of simple, highly
interconnected processing elements.
 A computational model for studying
learning and intelligence.
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From Biological Neuron to
Artificial Neuron
Dendrite Cell Body Axon
From Biology to
Artificial Neural Networks
Properties of Artificial Neural Networks

A network of artificial neurons
l
Characteristics
t
t
t
t
t
<Multilayer Perceptron Network>
Nonlinear I/O mapping
Adaptivity
Generalization ability
Fault-tolerance (graceful
degradation)
Biological analogy
Integrate-and-Fire Neuron
Membrane potential, u
 Membrane time constant,  m
 Input current, I (t )
 Synaptic efficiency, w j
 Firing time of presynaptic neuron
of synapse j, t jf
 Firing time of the postsynaptic
neuron, u (t f )
 Firing threshold, 
 Reset membrane potential, ures

 Absolute refractory time by holding
this value
du (t )
 u (t )  RI (t ) (leaky itegrator)
dt
I (t )   w j (t  t jf )
m
j
t jf
α  function : f ( x)  x exp(  x)
u (t f )  
lim u (t f   )  ures
 0
Fig. 3.1 Schematic illustration of a leaky integrate-and-fire
neuron. This neuron model integrates(sums) the external
input, with each channel weighted with a corresponding
synaptic weighting factors wi, and produces an output spike
if the membrane potential reaches a firing threshold
Activation Functions
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Associative Networks
Associative node and network architecture. (A) A
simplified neuron that receives a large number of inputs
riin. The synaptic efficiency is denoted by wi. the output of
the neuron, rout depends on the particular input stimulus.
(B) A network of associative nodes. Each component of the
input vector, riin, is distributed to each neuron in the
network. However, the effect of the input can be different
for each neuron as each individual synapse can have
different efficiency values wij, where j labels the neuron in
the network.
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Auto-associative node and network architecture. (A)
Schematic illustration of an auto-associative node that is
distinguished from the associative node as illustrated in
Fig. 7.1A in that it has, in addition, a recurrent feedback
connection. (B) An auto-associative network that consist
of associative nodes that not only receive external input
from other neural layers but, in addition, have many
recurrent collateral connections between the nodes in the
neural layer.
Multilayer Feedforward Networks
Error Backpropagation
wi  wi  wi ,
wi  
Information Propagation
Weights
Input x1
x
Input x2
E
wi
Output Comparison
 1
Ed ( w) 
(t k  ok ) 2

2 koutputs
Output
o  f (x)
Input x3
Input Layer
Scaling Function
Hidden Layer
Activation Function
Output Layer
Activation Function
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(c) 2008 SNU Biointelligence Laboratory, http://bi.snu.ac.kr/
Application Example:
Autonomous Land Vehicle (ALV)



NN learns to steer an autonomous vehicle.
960 input units, 4 hidden units, 30 output units
Driving at speeds up to 70 miles per hour
ALVINN System
Image of a
forward mounted
camera
Weight values
for one of the
hidden units
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(c) 2008 SNU Biointelligence Laboratory, http://bi.snu.ac.kr/
Multilayer Networks and its
Decision Boundaries
* Decision regions of a multilayer feedforward network.
* The network was trained to recognize 1 of 10 vowel sounds occurring
in the context “h_d”
* The network input consists of two parameter, F1 and F2, obtained
from a spectral analysis of the sound.
* The 10 network outputs correspond to the 10 possible vowel sounds.
Neural Nets for Face Recognition
960 x 3 x 4 network is trained on gray-level images of faces to predict
whether a person is looking to their left, right, ahead, or up.
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Other Types of Neural Networks
Self-Organizing Maps (SOM)

Output nodes build a
2D lattice

Winner-take-all
Competitive learning




Dimension reduction
Clustering
Visualization
© 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/
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Self-Organizing Map of Documents
Over million documents from 83 Usenet newsgroups. http://websom.hut.fi/websom
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Bayesian Networks
 BN = (S, P) consists of a network structure S and a set of local
probability distributions P
n
p(x)   p( x | pa )
i 1
i
i
<BN for detecting credit card
fraud>
• Structure can be found by relying on the prior knowledge of
© 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/
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Properties of Neural Networks
Hidden Layer Representation for Identity
Function
Hidden Layer Representation for Identity
Function
* The evolving sum of squared errors for each of the eight
output units as the number of training iterations (epochs)
increase
Hidden Layer Representation for Identity
Function
* The evolving hidden layer representation for the
input string “01000000”
Hidden Layer Representation for Identity
Function
* The evolving weights for one of the three hidden units
Generalization and Overfitting

Continuing training until the training error falls
below some predetermined threshold is a poor
strategy since BP is susceptible to overfitting.
 Need to measure the generalization accuracy over a
validation set (distinct from the training set).

Two different types of overffiting
 Generalization error first decreases, then increases, even
the training error continues to decrease.
 Generalization error decreases, then increases, then
decreases again, while the training error continues to
decreases.
Two Kinds of Overfitting Phenomena
Applications of Neural Networks and
Machine Learning
기계학습: 종류 및 모델
학습 방법
학습 문제의 예
감독 학습
인식, 분류, 진단, 예측, 회귀분석
무감독 학습
군집화, 밀도추정, 차원 축소, 특징추출
강화 학습
시행착오, 보상 함수, 동적 프로그래밍
모델 구조
표 현
기계학습 모델 예
논리식
명제 논리, 술어논리,
Prolog 프로그램
Version Space,
귀납적 논리 프로그래밍(ILP)
규칙
If-Then 규칙, 결정규칙
AQ
함수
Sigmoid, 다항식, 커널
신경망, RBF 망,
SVM, 커널 머신
트리
유전자 프로그램,
Lisp 프로그램
결정 트리, 유전자
프로그래밍, 뉴럴트리
그래프
방향성/무방향성 그래프,
네트워크
확률그래프 모델, 베이지안망, HMM
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Machine Learning: Three Tasks

Supervised Learning





Unsupervised Learning





Estimate an unknown mapping from known input and target output pairs
Learn fw from training set D={(x,y)} s.t.
f w (x)  y  f (x)
Classification: y is discrete
Regression: y is continuous
Only input values are provided
Learn fw from D={(x)} s.t.
f w ( x)  x
Compression
Clustering
Reinforcement Learning




Not target, but rewards (critiques) are provided
Learn a heuristic function hw from D={(x, a, c)} s.t.
Action selection
Policy learning
© 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/
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hw (x, a, c)
[Nature Reviews Neuroscience, 2006]
뇌 활동 신호 분석: Mind Reader
Support Vector Machine 기반의 기계학습 기술을 이용하여 뇌의 활동 신호를
분석함으로써
사람의 마음 상태, 생각, 지각을 예측한다.


시 자극이 눈을 통해 들어올 때 뇌의 활동 신호(multivariable
neuroimaging) 측정
신호를 SVM(Support Vector Machine)을 통해 분석
 얼굴 장면이 들어 올 경우 뇌의 FFA영역이 활성화됨.

응용 사례: 거짓말 탐지기 (Lie Detector)
© 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/
학습 기반 이미지 검색 기술:
Probabilistic
Latent Semantic Analysis
 P(w|d) = ∑P(w|z)P(z|d)
A. Bosch, A. Zisserman and X. Munoz
ECCV 2006
d: data, z: topic, w: visual word
(c) 2000-2007 SNU CSE Biointelligence Lab, http://bi.snu.ac.kr
© 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/
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대출고객 신용평가
방대하고 복잡한 데이터에서 찾아 내기 힘든 Rule이나 Factor를 자동 추출
금융권에서 통계에 기반한 통한 합리적 의사 결정에 Decision Tree 사용
신용등급 판별을
통한 고객성향 파악
Decision Tree
예
소득이 5000이상인가?
아니오
소득이
3000이상인가?
대기업
정규직인가?
40세 이하인가?
S-1 등급
S-2 등급
© 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/
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전문직인가?
V 등급
교회담임목사인가?
V 등급
D 등급
[ThunderBird 프로그램]
스팸메일 필터링: ThunderBird
Bayesian Filtering에 기반한 기계학습을 통해서 메일을 Junk과 Not Junk로 분류해 준다.
© 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/
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[Thore Graepel, MS Research Cambridge]
레이싱 게임 학습: Microsoft XBOX 360
MS XBOX 360의 레이싱 게임인 Forza 2의 플레이어 운전 패턴을 기계학습을 통해
모델링 한 후 확률적으로 운전을 모방함으로써 인간 수준의 플레이 실현
Microsoft Research in Cambridge, UK
http://research.microsoft.com/mlp/apg/behavior.aspx
게임 플레이 상에서의 운전 패턴
도로상
위치
주행차선
코스별
속력
브레이크
/엑셀
모든 경로 세그먼트화 
게이머가 선택하는 최적경로 학습
(Imitation Approach)
운전자 운전 패턴 확률기반 모델링
확률적
모델링으로 인해 동일 수준의 무한한 운전 형태를
© 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/
생성
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[Baluja, WWW2006]
모바일 웹 브라우징: Google
Decision Tree 기반 학습기술을 이용하여 휴대폰의 작은 화면상에서 웹페이지 화면을 보기
편리하도록 화면을 자동으로 분할해 줌.
Decision Tree를
이용
화면을 균등하게
분할
1
4
7
1
4
7
1
4
75
2
5
8
2
5
8
2
5
8
→ 내용기준 구역 분할
3
3
6
9
6
9
3
6
9
내용을 이해하기 어려움
문장이나 그림이 같은 구역 안에 위치
© 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/
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산업적 응용 사례: 기타


Robotics



 DARPA Grand Challenge
 헬리콥터 자동 비행
 가정/사무실 도우미 로봇


Mobile 기기


 Spam filtering
 아마존 추천서비스
 블로그/뉴스 이용 여론조사

Computer Vision

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공통관심사 분석
범죄 예방

© 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/
뇌 신호 분석
Connectonomics–뉴런구조 규명
소셜 네트워크

 인물사진 검색 및 매칭
 천체사진 자동 분석
 고고학 유뮬 자동 매칭
Drivatar – 레이싱 게임의 AI
Neuroscience



대출고객 신용평가
신용카드 평가
컴퓨터게임

Web 응용서비스
게놈 구조 예측
바이오 네트워크 분석
HIV 백신 설계
금융


 Web browsing
 행동인식 폰

Bioinformatics 및 의약
뉴욕경찰청 실시간 치안센터