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A tifi i l I t lli A I t d tiA tifi i l I t lli A I t d tiArtificial Intelligence: An IntroductionArtificial Intelligence: An Introduction

Revised September 2008Revised September 2008

Byoung-Tak Zhang

School of Computer Science and EngineeringSchool of Computer Science and EngineeringGraduate Programs in Cognitive Science, Brain Science,

and BioinformaticsSeoul National UniversitySeoul National University

E-mail: [email protected]://bi snu ac kr/http://bi.snu.ac.kr/

Can Machines Think?Can Machines Think?Can Machines Think?Can Machines Think?

Th T i TThe Turing TestComputing Machinery and Intelligence [Turing, 1950]

(c) 2000-2009 SNU CSE Biointelligence Lab, http://bi.snu.ac.kr/

Chess PlayingChess PlayingChess PlayingChess Playing

G K d D Bl © 1997


Garry Kasparov and Deep Blue. © 1997

Mars Rover Sojourner:Mars Rover Sojourner: M P hfi d Mi iM P hfi d Mi iMars Rover Sojourner: Mars Rover Sojourner: Mars Pathfinder MissionMars Pathfinder Mission


Natural Language ProcessingNatural Language ProcessingNatural Language ProcessingNatural Language Processing

Polysemy4 I keep the money in the bank.4 I walk along the bank of the river4 I walk along the bank of the river.

Ambiguity4Time flies like an arrow4Time flies like an arrow.4 I saw a man with a telescope.

Diversityy4She sold him a book for five dollars.4He bought a book for five dollars from her.

Related Knowledge4Lexical, Grammatical, Situational, Contextual


Expert SystemsExpert SystemsExpert SystemsExpert Systems


Web Information RetrievalWeb Information RetrievalText Data

Classification System

Cl ifi i

Preprocessing and Indexing

Text Classification

Information Extraction

I f ti Filt i S t

DB Template Filling& InformationExtraction System

Information Filtering

Information Filtering System

questionuser profile

LocationDate

DB Record

Extraction System

question

feedback

answerfiltered data


feedbackDB

What Is Artificial Intelligence (AI)?What Is Artificial Intelligence (AI)?What Is Artificial Intelligence (AI)?What Is Artificial Intelligence (AI)?

Branch of computer science that is concerned with the automation of intelligent behavior.Design and study of computer programs that behave intelligentlyes g a d study o co pute p og a s t at be ave te ge t yStudy of how to make computers do things at which, at the moment, people are better.Designing computer programs to make computers smarterDesigning computer programs to make computers smarter.Develop programs that respond flexibly in situations that were not specifically

) h l i beg.) - house-cleaning robots- perceive its surroundings- navigate on the floor- respond to events- decide what to do next- space exploration (Fig. 1.1)p p ( g )

Synonyms of AI: machine intelligence


What is Artificial Intelligence?What is Artificial Intelligence?What is Artificial Intelligence?What is Artificial Intelligence?AI i ll i f h d bl hi h b l d bAI 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.g g g4e. g., understanding spoken natural language, medical diagnosis,

circuit design, learning, self-adaptation, reasoning, chess playing, proving math theories etcproving math theories, etc.

Definition from R & N book: a program that4Acts like human (Turing test)4Thinks like human (human-like patterns of thinking steps)4Acts or thinks rationally (logically, correctly)

Some problems used to be thought of as AI but are nowSome problems used to be thought of as AI but are now considered not4e. g., compiling Fortran in 1955, symbolic mathematics in 1965,


pattern recognition in 1970

Research GoalsResearch GoalsResearch GoalsResearch Goals

Making machines more usefulg

U d t di i t lliUnderstanding intelligence


History of AIHistory of AIHistory of AIHistory of AIEarly enthusiasm (1950’s & 1960’s)4 Turing test (1950)4 1956 Dartmouth conference4 Emphasize on intelligent general problem solvingp g g p g

Emphasis on knowledge (1970’s)4 Domain specific knowledge4 DENDRAL, MYCIN

AI became an industry (late 1970’s & 1980’s)AI became an industry (late 1970 s & 1980 s)4 Knowledge-based systems or expert systems4 Wide applications in various domains

Searching for alternative paradigms (late 1980’s - early 1990’s)44 AI’s Winter: limitations of symbolic/logical approaches4 New paradigms: neural networks, fuzzy logic, genetic algorithms, artificial life

Resurge of AI (mid 1990’s – present)4 Internet Information retrieval data mining bioinformaticsInternet, Information retrieval, data mining, bioinformatics4 Intelligent agents, autonomous robots

Recent trends:4 Probabilistic computation4 Biological basis of intelligence


4 Biological basis of intelligence4 Brain research, cognitive science

Artificial Intelligence (AI)Artificial Intelligence (AI)g ( )g ( )

S b li AISymbolic AI Rule-Based Systems

Connectionist AI Neural Networks

Evolutionary AI Genetic Algorithms

Molecular AI:


Molecular AI: DNA Computing

Research Areas and ApproachesResearch Areas and ApproachesppppLearning AlgorithmsInference Mechanisms

ResearchInference MechanismsKnowledge RepresentationIntelligent System Architecture

Intelligent AgentsInformation RetrievalElectronic Commerce

ArtificialIntelligence Application

Electronic CommerceData MiningBioinformaticsN t l L P

R ti li (L i l)

Natural Language Proc.Expert Systems

Rationalism (Logical)Empiricism (Statistical)Connectionism (Neural)Paradigm


Evolutionary (Genetic)Biological (Molecular)

Intelligent AgentsIntelligent AgentsIntelligent AgentsIntelligent Agents

Autonomous Agents

Biological Agents Robotic Agents Computational Agents

Software Agents Artificial Life gAgents

EntertainmentAgents

Task-specificAgents

Viruses


Applications of Intelligent Agents (1)Applications of Intelligent Agents (1)Applications of Intelligent Agents (1)Applications of Intelligent Agents (1)

E il AE-mail Agents4Beyond Mail, Lotus Notes, Maxims

h d liScheduling Agents4ContactFinder

Desktop Agents4Office 2000 Help, Open Sesame

Web-Browsing Assistants4WebWatcher, Letizia

Information Filtering Agents4Amalthaea, Jester, InfoFinders, Remembrance agent,

PHOAKS Sit S


PHOAKS, SiteSeer

Applications of Intelligent Agents (2)Applications of Intelligent Agents (2)Applications of Intelligent Agents (2)Applications of Intelligent Agents (2)

N i ANews-service Agents4NewsHound, GroupLens, FireFly, Fab, ReferralWeb,

NewTNewTComparison Shopping Agents4Mysimon BargainFinder Bazzar Shopbor Fido4Mysimon, BargainFinder, Bazzar, Shopbor, Fido

Brokering Agents4P lL i B K b h J Y t4PersonalLogic, Barnes, Kasbah, Jango, Yenta

Auction Agents4A ti B t A ti W b4AuctionBot, AuctionWeb

Negotiation Agents4D t D t t T@T


4DataDetector, T@T

Computers Meet BiosciencesComputers Meet BiosciencesComputers Meet BiosciencesComputers Meet Biosciences

BT IT

Bioinformation Technology(BIT)


( )

AI in Life SciencesAI in Life SciencesAI in Life SciencesAI in Life Sciences

S l iS l iSequence analysisSequence analysis4 Sequence alignment4 Structure and function prediction

Structure analysisStructure analysis4 Protein structure comparison

4 Gene finding

p4 Protein structure prediction 4 RNA structure modeling

Expression analysisExpression analysis4 Gene expression analysis4 Gene clustering

Pathway analysisPathway analysis

4 Gene clustering


4Metabolic pathway4 Regulatory networks

Bi l i l A li tiBi l i l A li tiBiological ApplicationBiological Application

&

120 l f

&

120 samples from60 leukemia patients

Gene expression data Class: ALL/AML

Di iTraining with


Diagnosis

[Cheok et al., Nature Genetics, 2003]

6-fold validation

Evolutionary ComputationEvolutionary Computation: : y py pNature as ComputerNature as Computer

“Owing to this struggle for life, any variation, however slight and from whatever cause proceeding, if it be in any degree profitable to an individual of any species, in its infinitely complex relations to other organic beings and to external nature, will tend to the preservation of that individual, and will generally be inherited by its offspring.” p g

Origin of Species “Charles Darwin (1859)”


Genetic AlgorithmsGenetic AlgorithmsGenetic AlgorithmsGenetic Algorithms110010 1010

crossovercrossover

solutions

1100101010

1011101110 110010 1110

101110 1110

encodingchromosomes

solutions0011011001

1100110001

mutationmutation

101110 1010

00110 10011

00110 10010

evaluationevaluationselectionselectionnew population 00110 10010

1100101110

1011101010

0011001001

roulette

solutions

fitness

roulettewheel


fitnesscomputation

Hot Water Flashing Nozzle (1)Hot Water Flashing Nozzle (1)Application Example 1Application Example 1

Hot Water Flashing Nozzle (1)Hot Water Flashing Nozzle (1)Hans-Paul Schwefel

Start

Hot water entering Steam and droplet at exit

performed the original experiments

Hot water entering Steam and droplet at exit

At throat: Mach 1 and onset of flashingg


Concrete Shell RoofConcrete Shell RoofApplication Example 3Application Example 3

Concrete Shell RoofConcrete Shell Roof

under own and outer load (snow and wind)

Optimal shape

Height 1.34m

Spherical shapep p

Half span 5.00m

S i 36% h ll thi ki


Savings : 36% shell thickness

27% armation

max min

Orthogonal bending strength

→ϕm

Cooperating Robots (3)Cooperating Robots (3)Application Example 13Application Example 13

Cooperating Robots (3)Cooperating Robots (3)

Cooperating Autonomous Robots


CC l i S S ftb t (1)l i S S ftb t (1)Application Example 14Application Example 14

CoCo--evolving Soccer Softbots (1)evolving Soccer Softbots (1)CoCo evolvingevolvingCoCo--evolvingevolvingSoccer Softbots Soccer Softbots With Genetic With Genetic P iP i

At R b C th t "l " th " l" b t

ProgrammingProgrammingAt RoboCup there are two "leagues": the "real" robot league and the "virtual" softbot leagueHow do you do this with GP?How do you do this with GP?4GP breeding strategies: homogeneous and heterogeneous4Decision of the basic set of function with which to evolve players


Decision of the basic set of function with which to evolve players4Creation of an evaluation environment for our GP individuals

CoCo evolving Soccer Softbots (2)evolving Soccer Softbots (2)Application Example 14Application Example 14

CoCo--evolving Soccer Softbots (2)evolving Soccer Softbots (2)

Initial Random Population




Kiddie Soccer




Learning to Block the Goal




Becoming Territorial


10001000--Pentium BeowulfPentium Beowulf--Style Style yyCluster Computer for Parallel GPCluster Computer for Parallel GP


Computing Power and Memory Capacity of Computing Power and Memory Capacity of p g y p yp g y p yComputers and Organisms [Moravec, 1988]Computers and Organisms [Moravec, 1988]


Von Neumann’s Von Neumann’s The Computer The Computer ppand the Brain (1958)and the Brain (1958)


John von Neumann (1903-1957)

The Computer and the BrainThe Computer and the BrainThe Computer and the BrainThe Computer and the Brain

- 10 billion neurons(100 trillion synapses)

- Less than 1 million processors (10 –9 sec each, neuron: 10-3 sec) ( y p )

- Distributed processing- Nonlinear processing- Parallel processing

(10 sec each, neuron: 10 sec)- Central processing- Arithmetic operation (linearity)

Sequential processing


- Parallel processing- Carbon-based (wet)

- Sequential processing- Silicon-based (dry)

From Biological Neurons to From Biological Neurons to ggArtificial NeuronsArtificial Neurons


Multilayer Perceptron (MLP)Multilayer Perceptron (MLP)Error Backpropagation

Multilayer Perceptron (MLP)Multilayer Perceptron (MLP)

E∂Output Comparison

Information Propagation ∑ −≡ kkd otwE 2)(1)(i

iiii wEwwww

∂∂

−=ΔΔ+← η ,

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


Application Example:Application Example:pp ppp pAutonomous Land Vehicle (ALV)Autonomous Land Vehicle (ALV)

NN learns to steer an autonomous vehicle.960 input units, 4 hidden units, 30 output units p pDriving at speeds up to 70 miles per hour

ALVINN System

Weight values

Image of aforward -mounted

for one of the hidden units

mountedcamera


Neural Nets for Face RecognitionNeural Nets for Face Recognition

960 x 3 x 4 network is trained on gray-level images of faces to predict


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.

New Directions: New Directions: BiointelligenceBiointelligence


Humans and ComputersHumans and ComputersHumans and ComputersHumans and Computers

The Entire Problem Space

What Kind ofHuman Computers What Kind of Computers?

Human Computers

Current Computers


Humans and MachinesHumans and MachinesHumans and MachinesHumans and Machines

Humans are 4creative

Humans are ♦ imprecise, creative,

4compliant, 4attentive to change

p ,♦ sloppy, ♦ distractable, attentive to change,

4resourceful, and 4multipurpose

,♦ emotional, and ♦ illogical4multipurpose

To achieve human-level intelligence these

♦ illogical

To achieve human-level intelligence theseproperties should be taken into account.


Toward HumanToward Human Level IntelligenceLevel IntelligenceToward HumanToward Human--Level Intelligence Level Intelligence

Human intelligence develops situated in a multimodal environment [Gibbs, 2005].[ , ]The human mind makes use of multiple representations and problem-solving strategies [Fuster, 2003]. ]The brain consists of functional modules which are localized in subcortical areas but work togetheron the whole-brain scale [Grillner et o e w o e b sc e [G e eal., 2006]. Humans can integrate the multiple tasks into a coherent solution [Jones, 2004].2004]. Humans are versatile and come up with many new ideas and solutions to a given problem [Minsky, 2006].


What is What is the information the information i i i li i i l d l id l iprocessing principleprocessing principle underlying underlying

human intelligence?human intelligence?human intelligence?human intelligence?


Mind Brain Cell MoleculeMind Brain Cell MoleculeMind, Brain, Cell, MoleculeMind, Brain, Cell, MoleculeMindMindMind

Brain

C llCell

M l l∞ Molecule

1011 cells

∞ memory

0 ce s

1010 mol.


Molecular Basis of Learning and Memory Molecular Basis of Learning and Memory g yg yin the Brainin the Brain


Principles of Learning: Modern ConceptsPrinciples of Learning: Modern ConceptsPrinciples of Learning: Modern ConceptsPrinciples of Learning: Modern Concepts

Types of learning: Accretion, tuning, restructuring (e grestructuring (e.g., Rumelhart & Norman, 1976)Encoding specificityEncoding specificity principle (Tulving, 1970’s)Cellular and molecular basis of learning andbasis of learning and memory (Kandel et al., 1990’s) Conceptual blend andConceptual blend and chemical scramble (e.g., Feldman, 2006)

© 2008, SNU Biointelligence Lab, http://bi.snu.ac.kr/

45


Principles of Information Processing in thePrinciples of Information Processing in thePrinciples of Information Processing in the Principles of Information Processing in the BrainBrain

The Principle of Uncertainty4Precision vs. predictionp

The Principle of Nonseparability “UN-IBM”4Processor vs. memoryy

The Principle of Infinity4Limited matter vs. unbounded memory

The Principle of “Big Numbers Count”4Hyperinteraction of 1011 neurons (or > 1017 molecules)

The Principle of “Matter Matters”4Material basis of “consciousness” [Zhang, 2005]


Unconventional ComputingUnconventional ComputingUnconventional ComputingUnconventional Computing

Quantum Computing4Atoms4Superposition, quantum entanglements

Chemical ComputingChemical Computing 4Chemicals4Reaction diffusion computing4Reaction-diffusion computing

Molecular Computing4M l l4Molecules4“Self-organizing hardware”


Molecular Computing: Molecular Computing: p gp gBioMolecules as ComputerBioMolecules as Computer


011001101010001 ATGCTCGAAGCT

DNA Molecules for Information DNA Molecules for Information Storage and ProcessingStorage and Processing

Writing: make DNA sequences

DNA

a t c g g t c a t ag c a c t

0 0 0DNAmemory strands

1 0 1

t a g c c c g t g a

t c a t a

t a g c c c g t g a

a t c g g t c a t a

Reading: hybridization and readout


g y

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

Learningx1=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=0

x6=0

x7 =0

x8=1

x9=0

x10=0

x11=1

x12=0

x13=0

x14=0

x15=1

y=1

4

x1x2 x15

x4 x10 y=1x1Round 1Round 2Round 3

x3 x144 10 y1

x4 x12 y=1x1

x10 x12 y=1x4

1

x12

x4 x13x3 x9 y=0x2

x3 x14 y=0x22x12

x5x9 x14 y=0x3

x6 x8 y=1x3

x6 x11x6 x13 y=1x3

x8 x13 y=1x6

3


50x8 x9

x7x10x11 x15 y=0x84


Hypernetwork of DNA Molecules

[Zhang, DNA-2006](c) 2000-2009 SNU CSE Biointelligence Lab, http://bi.snu.ac.kr/

““학습학습 추론”하는추론”하는 DNADNA 컴퓨터컴퓨터““학습학습 추론”하는추론”하는 DNA DNA 컴퓨터컴퓨터

MP4.avi


M l l C Sili CM l l C Sili CMolecular Computers vs. Silicon ComputersMolecular Computers vs. Silicon Computers

Molecular Computers Silicon Computers

Processing Ballistic HardwiredMedium Liquid (wet) or Gaseous (dry) Solid (dry)Communication 3D collision 2D switchingCommunication 3D collision 2D switchingConfiguration Amorphous (asynchronous) Fixed (synchronous)Parallelism Massively parallel Sequentialy p qSpeed Fast (millisec) Ultra-fast (nanosec)

Reliability Low High

Density Ultrahigh Very high

Reproducibility Probabilistic Deterministic


B h k P bl Di it IB h k P bl Di it IBenchmark Problem: Digit ImagesBenchmark Problem: Digit Images

• 8x8=64 bit images (made from 64x64 scanned gray images)• Training set: 3823 images• Test set: 1797 images


• Test set: 1797 images

Pattern Information ProcessingPattern Information ProcessingPattern Information ProcessingPattern Information Processing

0.7

0.8

0.9

1

ate

0 2

0.3

0.4

0.5

0.6

Cla

ssifi

catio

n ra

Order 1Order 2

0

0.1

0.2

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97

epoch


Cognitive Learning and MemoryCognitive Learning and Memory

Toward HumanToward Human--Level Machine Learning: Level Machine Learning: M l i d l M G (MMG)M l i d l M G (MMG)Multimodal Memory Game (MMG)Multimodal Memory Game (MMG)

But, I'm getting married tomorrowBut, I'm getting married tomorrowBut, I'm getting married tomorrowBut, I'm getting married tomorrowBut, I'm getting married tomorrowBut, I'm getting married tomorrowWell, maybe I am...I keep thinking about you.And I'm wondering if we made a mistake giving up so fast.

Well, maybe I am...I keep thinking about you.And I'm wondering if we made a mistake giving up so fast.But, I'm getting married tomorrowW ll b IBut, I'm getting married tomorrowW ll b IBut, I'm getting married tomorrowW ll b IBut, I'm getting married tomorrowW ll b I

Well, maybe I am...I keep thinking about you.And I'm wondering if we made a mistake giving up so fast.



Well, maybe I am...I keep thinking about you.And I'm wondering if we made a mistake giving up so fast.g g g pAre you thinking about me?But if you are, call me tonight.

g g g pAre you thinking about me?But if you are, call me tonight.

Well, maybe I am...I keep thinking about you.And I'm wondering if we made a mistake giving up so fast.A thi ki b t ?








Are you thinking about me?But if you are, call me tonight.Are you thinking about me?But if you are, call me tonight.Are you thinking about me?But if you are, call me tonight.Are you thinking about me?But if you are, call me tonight.

Image Sound Text

Text HintHint Image

(c) 2000-2009 SNU CSE Biointelligence Lab, http://bi.snu.ac.kr/Image-to-Text Generator Text-to-Image GeneratorMachine Learner

Text Generation Game (from Image)Text Generation Game (from Image)Text Generation Game (from Image)Text Generation Game (from Image)

Image SoundSound Text

LearningLearningby ViewingT

I2T T2IGameManager

Text HintT


58

g


Image Generation Game (from Text)Image Generation Game (from Text)Image Generation Game (from Text)Image Generation Game (from Text)

TextImage SoundSound

LearningLearningby ViewingI

I2T T2IGameManager

Hint ImageI


59

g


Three ExperimentsThree ExperimentsThree ExperimentsThree Experiments

S G iSentence Generation4Learn: a linguistic recall memory from a sentence corpus4Given: a partial or corrupt sentencep p4Generate: a complete sentence

Image-to-Text Translation4 i j i d l f i i4Learn: an image-text joint model from an image-text pair corpus4Given: an image (scene)4Generate: a text (dialogue of the scene)( g )

Text-to-Image Translation4Learn: an image-text joint model from an image-text pair corpus4Gi (di l )4Given: a text (dialogue)4Generate: an image (scene of the dialogue)


60

Experiment 1: Learning Linguistic MemoryExperiment 1: Learning Linguistic MemoryExperiment 1: Learning Linguistic MemoryExperiment 1: Learning Linguistic Memory

D i f dDataset: scripts from dramas4Friends4HouseHouse4244Grey Anatomy 44Gilmore Girls 4Sex and the City

Training data: 289 468 sentencesTraining data: 289,468 sentences Test data: 700 sentences with blanksVocabulary size: 34 219 wordsVocabulary size: 34,219 words


61

Sentence Completion ResultsSentence Completion ResultsSentence Completion ResultsSentence Completion Results

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

We ? ? a lot ? giftsWe don't have a lot of gifts

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

? ? don't need your ?if I don't need your help

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

? ? a dream about ? In ?I had a dream about you in Copenhagen

? i t it if ? ll h b ? ?

? ? ? decisionto make a decision

I still can't believe you did this

What ? ? ? hereWhat are you doing here

? ? fi ? f di l h l

I had a dream about you in Copenhagen

? appreciate it if ? call her by ? ? I appreciate it if you call her by the way

I'm standing ? the ? ? ? cafeteria I' t di i th f th f t i

Would you ? to meet ? ? Tuesday ? W ld i t t i T d d

? you ? first ? of medical schoolAre you go first day of medical school

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

? think ? I ? met ? somewhere beforeI think but I am met him somewhere before

I'm standing in the one of the cafeteriaWould you nice to meet you in Tuesday and


62

Experiments 2 & 3: Crossmodal Experiments 2 & 3: Crossmodal ppTranslationTranslation The order (k) of hyperedge

♦ Text: Order 2~4Dataset: scenes and corresponding scripts from two dramas4 Friends

♦ Image: Order 10~340The method of creating hyperedges from training data

4 Prison Break

Training data: 2,808 scenes and scriptsScene (image) size: 80 x 60 = 4800

yp g g♦ Text: Sequential sampling from a

randomly selected position♦ Image: Random sampling in 4,800 Scene (image) size: 80 x 60 = 4800

binary pixelsVocabulary size: 2,579 words

g p gpixel positions

Number of samples from an image-text pair

Where am I giving birth

g p♦ From 150 to 300

Where am I giving birth

I know it's been really hard for you

So when you guys get in there


63

So when you guys get in there

ImageImage--toto--Text Translation ResultsText Translation Results

Matching &

ImageImage toto Text Translation ResultsText Translation Results

AnswerQuery

I don't know

gCompletion

I don't know what happeneddon't know whatknow what happened

There's a kitty in my guitar case

There's aa kitty in

case…in my guitar case

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

Maybe there's somethingthere's something I

… pregnantI get pregnant


TextText--toto--Image Translation ResultsImage Translation Results

Matching &

TextText toto Image Translation ResultsImage Translation Results

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 d t k I t tdo to make sure I get pregnant


Toward HumanToward Human--Level IntelligenceLevel Intelligence


From Mind to Molecules and BackFrom Mind to Molecules and BackFrom Mind to Molecules and BackFrom Mind to Molecules and BackMind

BrainBrain

Cell

Molecule∞ memory

Molecule1011 cells

>103 molecules


67(c) 2000-2009 SNU CSE Biointelligence Lab, http://bi.snu.ac.kr/

P di f C t ti l I t lliP di f C t ti l I t lliParadigms for Computational IntelligenceParadigms for Computational Intelligence

Symbolism Connectionism Dynamicism Hyperinter-actionism

Metaphor symbolt

neuralt dynamical system biomolecular

tp system system y y systemMechanism logical electrical mechanical chemical

Description syntactic functional behavioral relational

Representation localist distributed continuous collective

Organization structural connectionist differential combinatorial

Adaptation substitution tuning rate change self-assembly

Processing sequential parallel dynamical massively parallel

Structure procedure network equation hypergraphStructure procedure network equation hypergraph

Mathematics logic, formallanguage

linear algebra,statistics geometry, calculus graph theory,

probabilistic logicS /ti f l ti l t l ti t l


68

Space/time formal spatial temporal spatiotemporal

[Zhang, IEEE Comp. Intel. Mag., August 2008](c) 2000-2009 SNU CSE Biointelligence Lab, http://bi.snu.ac.kr/

Da Vinci’s Dream of Flying MachinesDa Vinci’s Dream of Flying MachinesDa Vinci s Dream of Flying MachinesDa Vinci s Dream of Flying Machines


Engines of FlightEngines of FlightEngines of FlightEngines of Flight

R k E i(c) 2000-2009 SNU CSE Biointelligence Lab, http://bi.snu.ac.kr/

Piston Engine Jet Engine Rocket Engine

T i ’ D f I t lli t M hiT i ’ D f I t lli t M hiTuring’s Dream of Intelligent MachinesTuring’s Dream of Intelligent Machines

Alan Turing(1912-1954)


Computing Machinery and Intelligence (1950)

C t d I t lliC t d I t lliComputers and IntelligenceComputers and Intelligence


Future Technology EnablersFuture Technology EnablersFuture Technology EnablersFuture Technology Enablers

Bio-electric computers

True neural computing

Quantum computer, molecular electronics

computers1e6-1e7 x lower power for lifetime batteries

Full motion

Smart lab-on-chip, plastic/printed ICs, self-assembly

mobile video/office

Metal gates Pervasive voice

Vertical/3D CMOS, Micro-wireless nets, Integrated optics

yWearable communications, wireless remote medicine, ‘hardware over internet’ !

Metal gates, Hi-k/metal oxides, Lo-k with Cu, SOI

recognition, “smart” transportation

Integrated optics

(c) 2000-2009 SNU CSE Biointelligence Lab, http://bi.snu.ac.kr/Source: Motorola, Inc, 2000

Now +2 +4 +6 +8 +10 +12

Artificial Intelligence (AI)Artificial Intelligence (AI)g ( )g ( )

S b li AISymbolic AI Rule-Based Systems

Connectionist AI Neural Networks

Evolutionary AI Genetic Algorithms

Molecular AI:


Molecular AI: DNA Computing

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