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Watson Watson

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Watson ,

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IBM Watson on Jeopardy Show

?

( + Aldebaran Robotics) 200 2015 7 1,000

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-

(classification)

(real-time bidding)

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SF ?

() 1,000 .

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()

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AlphaGoFacebook 2013 (: Yann LeCun )

() 2014 Institute of Deep Learning() 3 , : (Andrew Ng) ( )

IBM Watson

10 (1) 2,000 1 Watson

Dwango (Dial-up Wide-Area Network Game Operation) 2014 : ()

PFI 2014 PFI(Preferred Infrastructure) Preferred Networks

Deep-learning , IoT NTT 2

Preferred Networks

( )

? 2014 (Deloitte) 35% 20

IT 20 50%

2014 (Transcendence) 2014 (Her) , ( )2015 (Imitation game) (Alan Turing) 1968 (Stanley Kubrick) 2001 (2001: Space Odyssey) HAL 9000

1984 (Terminator)

(Technological Singularity)

?(singularity) (Ray Kurzweil) 2045

(Elon Musk) . .

(Bill Gates)

Google DeepMind

2014

?

Singularity is imminent?

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.

.

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.(Turing)

. (Turing machine)

(Marvin Lee Minsky)

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(Hubert Lederer Dreyfus)

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Artificial intelligence is the science of making machines do

things that would require intelligence if done by men

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(Stuart Russell), ()

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Stuart Russell

A definition of intelligence needs to be formala property of the systems input, structure, and outputso that it can support analysis and synthesis. The Turing test does not meet this requirement

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John Rogers Searle

3. 1 AI

AI

1 1950 1960

1970 (toy problem)

2 1980

. 1990

3 (Watson, , AlphaGo)

1960 1970 1980 1990 2000 2010

1 AI 2 AI

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1956 1970 1980 20151995 20101 (, )

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botCALO

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DENDRAL

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1956 : Dartmouth Summer Research Project on Artificial Intelligence : (John McCarthy) 1956 2 10 .

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Marvin Minsky1927~

Allan Newell1927~1992

Herbert Simon1916~2001

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Planning (condition)

(action) (result)

(condition) (action) (result)

STRIPS (Stanford Research Institute Problem Solver) 1971 (Richard Fikes) (Nils Nilsson)

SHRDLU (Terry Winograd) Planner

(Sergey Brin), (Larry Page)

Terry Winograd

, (brute-force search, exhaustive search)

: 1060 / : 10120 / : 10220 / : 10360

1080

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(maximum loss)

i : / -i : ai : , a-i : vi :

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.

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, 1997 IBM (DeepBlue)

(Garry Kasparov) 2012 ( ) ( )

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.

.

()

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IBM 704

20x20 400 Mark I Perceptron

1

1960

, , =

(Marvin Minsky) Perceptron Perceptron XOR XOR ?

1970 1

(Papert) , Perceptrons: an introduction to computational geometry

Marvin Minsky and Seymour Papert, 1969.

1 (Bronx High School of Science)

.

XOR

(symbolic)

: XOR , . / Boolean McCulloch-Pitt

3 feed-forward

connectedness

4. 2 AI

1980

2 AI =

1 AI 1964 ELIZA

1970 (MYCIN)

1960 (Edward Albert Feigenbaum) DENDRAL

Dendritic Algorithm Heuristic-DENDRAL: performance Meta-DENDRAL: learning

MYCIN, MOLGEN, MACSYMA, PROSPECTOR, XCON DENDRAL

Edward FeigenbaumThe Father of Expert System

DENDRAL , 1994. Turing

(description)

(domain)

? 4 ? 10 ?

(semantic network) (node) ( )

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Commonsense Reasoning and Commonsense Knowledge in Artificial Intelligence, Ernest Davis and Gary Marcus, Communications of the ACM, 58(9): 92-103, 2015

(ontology)

(ontology) () Towards Ontology Engineering, Technical Report AI-TR-96-1, I.S.I.R., Osaka University Theory of Existence An explicit representation of conceptualization / A Theory of vocabulary/concepts used in building artificial system

(, transitive law) Is-a

is-a ^ is-a is-a

Part-of part-of ^ part-of

part-of

part-of ^ part-of part-of

(JAIST)

Ontology example

(Ontology)

Linked Data Applications: There is no One-Size-Fits-All Formula,Asuncion Gomez-Perez,The 9th Summer School on Ontology Engineering and Semantic Web, 2012

(heavyweight ontology)

(lightweight ontology) ,

Thesaurus vs ontology in Why an ontological approach?, Oreste Signore, http://www.weblab.isti.cnr.it/talks/2009/iccu/slides.html#(1)

http://www.ai-one.com/tag/lightweight-ontology/

IBM Watson

2011 Jeopardy -

: 1871 :

/

? O O O X X

() ? X O X O O

1871 ? X O X O X

1300 500 200 150 10

2% 92% 20% 6% 0%

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He saw a woman in the garden with a telescope. 1. .2. .3. .

2 , . ?

Time flies like an arrow . .

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(frame problem)

: (John McCarthy) (Patric Hayes) (1969) (Daniel Dennett)

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open / on open(t), on(t)

, !open(0)!on(0)True open(1) : : !locked(0) open(1) : for all t, execute(t) ^ true open(t+1)

3 !open(0), !on(0) open(1), !on(1) !open(0), !on(0) open(1), on(1)

?Frame axiom: nothing else changes

Frame axiom

(Symbol grounding) (Stevan Harnard) 1990 (referent)

Tony Blair The prime minister of the UK during the year 2004 Cherie Blairs husband

? . .

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Aaron Sloman, Univ. of Birmingham( )

2 AI

2 AI .

AI

1. 1966 ALPAC

2. 1969 Symbolic reasoning

3. 1974 Lighthill Lighthill AI

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6. 1987 LISP 7. 90 8. 5

5 8.5

AI ?

5 3 AI

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/

(Google) - : $100,000 (1998)2015: $517,170,000,000

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( )

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Nave

Nave Bayes .

: = 1:10 : = 100:1

log(10/1) log(1/1000)

How To Build a Naive Bayes Classifierhttps://bionicspirit.com/blog/2012/02/09/howto-build-naive-bayes-classifier.html

Y xi

, https://ko.wikipedia.org/wiki/_

(SVM, Support Vector Machine)

(maximum-margin hyperplane)

SVM (x y ) (w ) (X+,X-)

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Yi = 1 Yi = -1

(Neural Network)

(sigmoid)

(threshold) on/off

( )

on/off :

, ,

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( 100) 784x100 + 100x10 = 79,400 ()

Backpropagation

()

MNIST data

0 1 2 3 4 5 6 7 8 90.05 0.05 0.07 0.40 0.05 0.10 0.06 0.03 0.14 0.02

feature() feature

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2012 ILSVRC(ImageNet Large Scale Visual Recognition Challege) (SuperVision)

Team name Filename Error (5 guesses) Description

SuperVision test-preds-141-146.2009-131-137-145-146.2011-145f. 0.15315 Using extra training data from ImageNet Fall 2011 release

SuperVision test-preds-131-137-145-135-145f.txt 0.16422 Using only supplied training data

ISI pred_FVs_wLACs_weighted.txt 0.26172Weighted sum of scores from each classifier with SIFT+FV, LBP+FV, GIST+FV, and CSIFT+FV, respectively.

ISI pred_FVs_weighted.txt 0.26602 Weighted sum of scores from classifiers using each FV.

ISI pred_FVs_summed.txt 0.26646 Naive sum of scores from classifiers using each FV.

ISI pred_FVs_wLACs_summed.txt 0.26952Naive sum of scores from each classifier with SIFT+FV, LBP+FV, GIST+FV, and CSIFT+FV, respectively.

Task 1()

?

SuperVision : -

SuperVision:

Deep Learning

Geoffrey Hinton

(Deep learning)

minor change

(auto-encoder)

Code =

(code) .

: 10 . A B 5 . B .

10

9

8

11

12

15

3

17

16

13

A

10

9

8

11

12

B

1 . B

10

12

15

3

17

2 1

?

9.5

12

15

3

15.3

?

(auto-encoder) A B

: 28x28 (784) 100

(representation)

= (representation learning) (Geoffrey Hinton) - ()

Auto-encoder

- - -

() End-to-end learning

1: 100 2: 1 100 2 20

20 (code) (represent)

784 nodes

784 nodes

100 nodes

100 nodes

100 nodes

20 nodes

2 = 1

3 = 2

4 = 3

=

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= () : , ()

,

Noise

Drop-out

Regularization

.