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vs , , (2016 )
2011 IBM Watson
Jeopardy 100
Watson Watson
Chef Watson (recipe)
Watson ,
() ( )
581 80% 472 80% A
IBM Watson on Jeopardy Show
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( + Aldebaran Robotics) 200 2015 7 1,000
Pepper, Softbank
-
(classification)
(real-time bidding)
90% (high frequency trading)
UBIC (Paralegal)
UBIC e-Discovery Service
SF ?
() 1,000 .
.2016 () () 4 1 1
()
AI
Google 2013 1 (Geoffrey E. Hinton) DNN 2014 4 (CEO: Demis Hassabis)
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
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Singularity is imminent?
2. ?
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.
.
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.
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.(Turing)
. (Turing machine)
(Marvin Lee Minsky)
(Roger Penrose)
(Hubert Lederer Dreyfus)
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, (), ()
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(Marvin Minsky)
Artificial intelligence is the science of making machines do
things that would require intelligence if done by men
, AI
(, AlphaGo)
AI , , (), (, ), AI
, ( ) (Marvin Minsky) : AI
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(Stuart Russell), ()
1 : , ,
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4 (Deep Learning)
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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AI AI (Strong AI as Artificial General Intelligence)
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(John Rogers Searle) ( )
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AI John Searle AI (John Searl )
. . . . . AI .
- (John Searle) 1984
Axioms 1. = (, Syntactic) 2. = (Semantics) 3. (syntax) (semantics) .
axiom Axiom
1 .
. () . . . , .
Axiom
4. .
2 (causal power) .
3 . . ( 1,2)
4 .
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
3 AI
Watson
1956 1970 1980 20151995 20101 (, )
2 ()
3 (, )
ELIZA SIRI
botCALO
WatsonLOD(Linked Open Data)
DENDRAL
-
,
, AI
1956 : Dartmouth Summer Research Project on Artificial Intelligence : (John McCarthy) 1956 2 10 .
. , , , . .
John McCarthy (1927~2011)
Marvin Minsky1927~
Allan Newell1927~1992
Herbert Simon1916~2001
4 Turing Simon
1 AI : : , : (ex. Depth-first iterative-deepening)
() ()
AA
BB CC
DDEE
FF
GG
HH
II
AA
BB CC
DDEE
FF
GG
HH
II
start
goal goal
start start
goal
start
AAEE
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. : 1883 (douard Lucas)
(dome) . 1 . 64 . , . . . , .
: .
64 18,445,744,073,709,551,616 1 5849 4241 7355
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
Minimax
(maximum loss)
i : / -i : ai : , a-i : vi :
Minimax
.4 1. ( ) 2. 3. 4. 5.
.
( ) Minimax
Minimax search
, 1997 IBM (DeepBlue)
(Garry Kasparov) 2012 ( ) ( )
() () 2016 AlphaGo
.
.
()
Perceptron (Frank Rosenblatt)1957 Mark I Perceptron
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) ( )
Cyc 1984 (Douglas Lenat) (#$isa #$BillClinton #$UnitedStatesPresident) Bill Clinton belongs to the collection of U.S. President (#$genls #$Tree-ThePlant #$Plant) All trees are plants
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%
Watson - , -
He saw a woman in the garden with a telescope. 1. .2. .3. .
2 , . ?
Time flies like an arrow . .
Time flies, Dwarf4r, http://dwarf4r.deviantart.com/art/Time-Flies-332901940
(frame problem)
: (John McCarthy) (Patric Hayes) (1969) (Daniel Dennett)
Mission: Robot1: . . . . . . () .Robot2: . . . . . . . time over. . . Robot3: ? ? .
, , , ,
, , .
Frame problem John McCarthy
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
? . .
? (sensor) ? .
Aaron Sloman, Univ. of Birmingham( )
2 AI
2 AI .
AI
1. 1966 ALPAC
2. 1969 Symbolic reasoning
3. 1974 Lighthill Lighthill AI
4. 70 DARPA AI 1969 mission-orient direct research
5. SUR debacleDARPA CMU speech understanding research ( ) CMU
6. 1987 LISP 7. 90 8. 5
5 8.5
AI ?
5 3 AI
2 AI
AI 1989 Berners-Lee (web ) 1990 1993 1998
/
(Google) - : $100,000 (1998)2015: $517,170,000,000
?
YES/NO (binary classifier)
?
.
.
(rule) , (frequent)
( )
(Nearest neightbor) (Nave Bayes) (Decision tree) (Support vector machine) (Neural networks)
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-)
X+ X- (margin) = 2/||W||
Yi = 1 Yi = -1
(Neural Network)
(sigmoid)
(threshold) on/off
( )
on/off :
, ,
- : MNIST 28x28 pixel (784-pixel) 7
( 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
168 2500
155 7000
183 12000
175 4000
174 1800
163 50000
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=
. .
symbol grounding . . (feature design) .
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(signfi, ) (signifiant, ) ( )
.
.
6. 3 AI
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
=
(, signfi) (signifiant)
= () : , ()
,
Noise
Drop-out
Regularization
.