ࣝܦࡁ
KNOWLEDGE ECONOMY
ۀܦࡁ
INDUSTRIAL ECONOMY
ۀԽ | Industrialization
18C
Πϯλʔωοτ | Internet
20C
ڙڅଆ | SUPPLY
धཁଆ | DEMAND େ ྔ / গ छ ྨ খ ྔ / ଟ छ ྨ
ਓؒܦࡁ
HUMAN ECONOMY
ਓೳ | A.I.
Now
খ ྔ / ଟ छ ྨ
؍1ɿधڅͷࡉԽ
ग़యɿࣝܦࡁ - Michael HardtɼThe Common in Communism
ਓؒܦࡁ - From Knowledge Economy to Human Economyɼ
Slide 8
Slide 8 text
1 . 0
ใ ͷ Φ ϯ ϥ Π ϯ
3 . 0
ͷ Φ ϯ ϥ Π ϯ
2 . 0
ؔ ͷ Φ ϯ ϥ Π ϯ
4 . 0
ε Ω ϧ ͷ Φ ϯ ϥ Π ϯ
?
x . 0
৺
&
ͷ
Φ
ϯ
ϥ
Π
ϯ
ਓ
ೳ
/
A . I .
ʮ σ ʔ λ ʯ ͷ Π ϯ λ ϑ Σ ʔε Λ
౷ Ұ
ʮ ε Ω ϧ ʯ ͷ Π ϯ λ ϑ Σ ʔε Λ
౷ Ұ
ʮ ೳ ʯ ͷ Π ϯ λ ϑ Σ ʔε Λ
౷ Ұ
ఱూ්ɿ$10ԯΛͱػցͷΠϯλϥΫ
γϣϯͷݚڀʹد
Initiative kicked off with $115 million gift from philanthropists
Tianqiao Chen and Chrissy Luo to establish a new institute
and provide continuous funds for neuroscience research.
Caltech to construct $200 million biosciences complex.
؍2ɿΦϯϥΠϯ / ίωΫτ / ΠϯλϥΫγϣϯ
ΦϯϥΠϯɿσʔλۀ
ͷຊ࣭Λมֵ͠ɺίϯϐϡ
ςʔγϣϯܦࡁͷະདྷΛ
࠶ߏங͢Δ
ԦࡔɿΞϦόόάϧʔϓٕज़ҕһ
ձҕһ
Slide 9
Slide 9 text
ग़యɿThomas Malone @ MIT Center for Collective Intelligence, The Age of Hyperspecialization
ϋΠύʔεϖγϟϥΠζυεΩϧ
Hyperspecialized Skill
ฏۉεΩϧ
Average Skill
Average
is Over ʂ
“
”
ඇ࣭ੜ࢈ऀ / ࣝੜ࢈ऀͷՁɺ͋ΔฏۉϨϕϧͷεΩϧʹୡ͢Δ͜ͱͰͳ͘ɺସෆՄೳͳϋΠύʔ
εϖγϟϥΠζυεΩϧΛ࣋ͭ͜ͱͩɻฏຌͳ࣌͏ऴΘΓɺϋΠύʔεϖγϟϥΠζυ͕࣌དྷͨɻ
؍3ɿϋΠύʔεϖγϟϥΠζυͳ
ݸମͷग़ݱ
Slide 10
Slide 10 text
ۀҕୗ
On Demand
ਖ਼ࣾһ
Employee
֎෦ઐՈ
External Expert
SOURCEɿMcKinsey Quarterly, Organizing for the Future
ະདྷͷ৫λεΫΛ֎෦ਓࡐɺ෦ਓࡐͱػցʹॊೈʹ͢ΔɻશମͷϫʔΫϑϩʔΛ·ͱΊͨΈ
ʹԠ͡ɺλεΫʮίϛϡχέʔγϣϯίετ͕࠷͍ʯྲྀΕʹԊͬͯɺεϜʔζʹͰ͖Δɻ
ࣗಈԽ
Automation
⾃
動
化
/
A u to m
at i o n
؍4ɿਓؒ/ػցͷΠϯλϥΫγϣϯͷ৽ͨͳ৫ߏ
ϛΫϩɿσβΠϯʹ͓͚Δ3ͭͷ࣍ݩ
֬ఆੑ / ෆ֬ఆੑ
σβΠϯͱɺՄೳੑͱࣗ༝Λഉআ
͢Δ͜ͱͰ͋Δɻ
Design is about eliminating possibilities and
degrees of freedom.
ܗࣜ / ༰
σβΠϯܗࣜͱ༰Λซଘͤ͞Δํ
๏Ͱ͋Δɻ
Design is the method of putting form and content
together.
“
“
ࢲ͕ܳज़ͷΛࢼΈͨͱ͖ɺͦΕΛ
Ͱ͖Δ͔Ͳ͏͔ɺࢲͰͳ͘ਆʹҕ
ͶΒΕ͍ͯΔɻ
I try to create art, whether I make it or not is not up
to me, it's up to God.
MILTON GLASER
“
PAUL RAND
PAUL RAND
Slide 17
Slide 17 text
ϛΫϩɿͲ͏ͬͯػցʹʮʯΛཧղͤ͞Δʁ
組合せによる創造 | COMBINATIONAL
CREATIVITY
2つ以上のコンセプトを組み合わせる:現存内容を新たな形
式で表すプロセスでよく⽤いられる。
例:Pringles はポテトチップスを新たな形式で包装するこ
と。似たような考え⽅で、複数の領域を組み合わせると「映
画レンタル市場」と「図書館の貸出システム」が⽣まれる。
“Creativity is just connecting things. ”
STEVE JOBS
探索による創造 | EXPLORATORY CREATIVITY
既存規則(概念空間)に発⽣:ものを探索する規則(探索空
間)が変わらない。たとえば、既存のものを改善や最適化する
こと。
例:より良い材料で、路⾯を修繕すること、また、バイアスタ
イヤからラジアルタイヤに進化することなど。
転換による創造 | TRANSFORMATIONAL
CREATIVITY
既存概念を突破、規則を破る:この創造⽅法は既存の規則を破
る。
例:ピカソの「Tete de Femme」。既存アートの芸術表現⼿法
を徹底的に突破した。
BODEN.M.A
(2009). Computer models of creativity. A.I. Magazine, 30(3), 23.
創造⼒の結果として「斬新かつ役⽴つ」ものができる:
• この結果は個⼈また社会にとって「斬新かつ役⽴つ」
• この結果により以前の結果を否定した
• この結果は創造者の強烈な動機と継続の意志から⽣まれた
• この結果は曖昧な問題をはっきり説明する
NEWELL, SHAW & SIMON
(1963), The process of creative thinking, H. E. Gruber, G. Terrell and M. Wertheimer
(Eds.), Contemporary Approaches to Creative Thinking, New York: Atherton
規則 | RULE 統計 | DATA
上から下に:伝統的な⼈⼯知能研究概念 現在の⼈⼯知能研究概念:下から上に
オットー・リリエンタール:⿃のような⾶⾏機
ϛΫϩɿͲ͏ͬͯʮػցʯʹʮσβΠϯʯͤ͞Δʁ
نଇ | RULE ౷ܭ | DATA
্͔ΒԼʹɿ౷తͳਓೳݚڀ֓೦ ݱࡏͷਓೳݚڀ֓೦ɿԼ͔Β্ʹ
ΦοτʔɾϦϦΤϯλʔϧɿௗͷΑ͏ͳඈߦػ
I think we have crossed a very important threshold. Until fairly
recently, most people in A.I. were doing a kind of A.I. that was
inspired by logic. The paradigm for intelligence was logical
reasoning and the idea of what an internal representation would look
like was it would be some kind of symbolic structure. That has
completely changed with these big neural nets. We now think of
internal representation as great big vectors and we do not think
of logic as the paradigm for how to get things to work. We just
think you can have these great big neural nets that learn, and so,
instead of programming, you are just going to get them to learn
everything. For many, many years, people in A.I. thought that was just
fantasy.
GEOFFREY HINTON
“
σʔλԽ
datafication
ϞσϦϯά
modeling
ίϯϐϡςʔγϣϯ
computation
ΤόϦϡΤʔγϣϯ
evaluation
σβΠϯ
design
Slide 21
Slide 21 text
C R E AT I V E S U P P LY
Ϋ Ϧ Τ Π ς Ο ϒ ڙ څ ଆ
D E M A N D | ध ཁ ଆ
S U P P LY | ڙ څ ଆ
“ݫີ͕͞ະདྷΛΓ։͘”
—— િ ໐ɹAlibaba CSO
ҙٛɿਓೳσβΠϯͱڙڅଆͷվֵ
C O N S U M E R
ফ අ ऀ
σʔλೳ
+
ωοτϫʔΫʹΑΔ
ڠಇ
ใ
Ϛ ʔ έ ς Ο ϯ ά
α ʔ Ϗ ε
ϓ ϩ μΫ τ
“ΦϯϥΠϯԽɺࣗಈԽɺσʔλԽ͕ͳ͚ΕɺೳԽͷͳ͍ ”
—— િ ໐ɹAlibaba CSO
Slide 22
Slide 22 text
σʔλೳ
+
ωοτϫʔΫʹΑΔ
ڠಇ
C R E AT I V E S U P P LY
Ϋ Ϧ Τ Π ς Ο ϒ ڙ څ ଆ
A . I .
A . I .
ҙٛɿਓೳσβΠϯͱڙڅଆͷվֵ
C O N S U M E R
ফ අ ऀ
ใ
Ϛ ʔ έ ς Ο ϯ ά
α ʔ Ϗ ε
ϓ ϩ μΫ τ
S U P P LY | ڙ څ ଆ
“ΦϯϥΠϯԽɺࣗಈԽɺσʔλԽ͕ͳ͚ΕɺೳԽͷͳ͍ ”
—— િ ໐ɹAlibaba CSO
D E M A N D | ध ཁ ଆ
“ݫີ͕͞ະདྷΛΓ։͘”
—— િ ໐ɹAlibaba CSO
ػցɿέʔεελσΟαϯϓϧ
A d o b e S e n s e i
A n y c l i p
A p r o p o s e
A r k i e
A r t i s t A g e n t
A r t i s t o
A s s i s t e d
D r u m m i n g w i t h
R o b o t i c A r m
A s s i s t e d E t h i c a l
D e c i s i o n M a k i n g ,
w i t h a f a n
A s s i s t e d H a i r
D e s i g n f r o m
P h o t o s
A s s i s t e d H a i r
D e s i g n P r o j e c t
A u t o d e s k
D r e a m c a t c h e r
B a n n e r s n a c k
B r a i n F M
C a n v a
C a p s u l e . f m
C i n d e r m e d u s a e
C l a r i f a i
C o d e d
C o u t u r e
C r i t e o
D e e p V i s u a l
A n a l o g y - M a k i n g
D e e p B a c h
D e s i g n M o r p h i n e
D e t e c t i o n
D o u b l e C l i c k
D u d a
E d i t e d
E n h a n c e m e n t
E y e Q u a n t
F a c e R i g L i v e 2 D
F l i p b o a r d
F l o r i a n S c h u l z
G C H Q
G e n e r a t i v e C a r
C h a i s e D e s i g n
G e n e r a t i v e D a t a -
D r i v e n S h o e
M i d s o l e D e s i g n
G e n e r a t i v e F o n t
D e s i g n w i t h
N e u r a l N e t w o r k s
G e n e r a t i v e
J e w e l l e r y d e s i g n
G e n e r a t i v e m a s s
c u s t o m i z e d T- s h i r t
a n d b a g s
G e n e r a t i v e M o t i o n
A r t
G e n e r a t i v e
M o t o r c y c l e
s w i n g a r m D e s i g n
G l i t c h e
G o o g l e A d s e n s e
G o o g l e
A r t s & C u l t u r e
G o o g l e A u t o D r a w
G o o g l e D r e a m
G o o g l e W e b
D e s i g n e r
H a c k R o d
H a i r M o d e l i n g
J u k e d e c k
K i n e m a t i c s D r e s s
L e a r n i n g
P e r c e p t u a l S h a p e
S t y l e S i m i l a r i t y
l o g o . s q u a r e s p a c e
L o g o j o y
M a g e n t a
M a g i s t o
M a n d e l b u l b
M a r k M a k e r
M a r v e l a p p
M o l e c u l e - s h o e s
M o o
N e u r a l I m a g e
A n a l o g i e s
N e u r a l D o o d l e
N e u r a l P a t c h
N o u n p r o j e c t
O u t s y s t e m s
P a r s i n g S e w i n g
P a t t e r n s i n t o 3 D
G a r m e n t s
P r i s m a
P r o c e s s i n g
F o u n d a t i o n
P r o j e c t M U S E
P r o j e c t : L e a r n i n g
V i s u a l C l o t h i n g
S t y l e
P r o j e c t ɿ T h e N e x t
R e m b r a n d t
R e c o n g n i t i o n
R e w r i t e E W R I T E
R o b o t i c P a i n t e r
S e l P h
S E N S Y
S h a d o w D r a w
S T Y L U M I A
Ta i l o r B r a n d s
Ta s t e A n a l y t i c s
T h e G r i d
T h e N e s t
R e m b r a n d t
T h r e a d
V i s u a l C l o t h i n g
S t y l e L e a r n i n g
S y s t e m
Vo x M e d i a
W e b c a m
W i b b i t z
W i x
Ya n d e x . L a u n c h e r
Yu r y Ve t r o v
Z o o m o r p h i c
D e s i g n
Ѩ ཬ ਗ਼ ൝
Ո + 1
䲳 ࢠ Պ ٕ
ى ٦ ်
ਂ
ၜ ඌ ࡗ L B C
100+
ೳϓϩμΫτ
A. I.
ػց
Apropose
σʔλυϦϒϯΣ
ϒධՁπʔϧ
Edited
ϑΝογϣϯσʔλ
SaaSαʔϏε
Wix
A.I.αΠτ࡞
πʔϧ
Wibbitz
ೳతʹ
ಈըΛσβΠϯ͢Δ
πʔϧ
Google AutoDraw
མॻ͖ೝࣝπʔϧ
Canva
άϥϑΟοΫσβ
Πϯ͔Βࣗಈίʔ
υͷੜπʔϧ
Alibaba Ruban
ࣗಈόφʔ
ੜπʔϧ
ཧ ŞžŚŘũŎŲƄ ŠŶœŬşŖŢŔƃ ඇॏෳମྗ࿑ಇ ૉࡐऩू ใॲཧ ॏෳମྗ࿑ಇ
Bannersnack
όφʔͷσβΠϯධ
Ձπʔϧ
Google Adsense
ࠂλʔήοτ
ϓϥοτϑΥʔϜ
EyeEm
ը૾ͷඒֶͷධՁΤ
ϯδϯ
Logojoy
άϥϑΟοΫʹΑΔ
ϩΰσβΠϯπʔϧ
Adobe Sensei
ࣗಈը૾मਖ਼
Τϯδϯ
Flipboard Duplo
ࣗಈϨΠΞτੜ
Τϯδϯ
Thread
ίʔσΟωʔτ
αʔϏε
Slide 29
Slide 29 text
A d o b e S e n s e i
A n y c l i p
A p r o p o s e
A r k i e
A r t i s t A g e n t
A r t i s t o
A s s i s t e d
D r u m m i n g w i t h
R o b o t i c A r m
A s s i s t e d E t h i c a l
D e c i s i o n M a k i n g ,
w i t h a f a n
A s s i s t e d H a i r
D e s i g n f r o m
P h o t o s
A s s i s t e d H a i r
D e s i g n P r o j e c t
A u t o d e s k
D r e a m c a t c h e r
B a n n e r s n a c k
B r a i n F M
C a n v a
C a p s u l e . f m
C i n d e r m e d u s a e
C l a r i f a i
C o d e d
C o u t u r e
C r i t e o
D e e p V i s u a l
A n a l o g y - M a k i n g
D e e p B a c h
D e s i g n M o r p h i n e
D e t e c t i o n
D o u b l e C l i c k
D u d a
E d i t e d
E n h a n c e m e n t
E y e Q u a n t
F a c e R i g L i v e 2 D
F l i p b o a r d
F l o r i a n S c h u l z
G C H Q
G e n e r a t i v e C a r
C h a i s e D e s i g n
G e n e r a t i v e D a t a -
D r i v e n S h o e
M i d s o l e D e s i g n
G e n e r a t i v e F o n t
D e s i g n w i t h
N e u r a l N e t w o r k s
G e n e r a t i v e
J e w e l l e r y d e s i g n
G e n e r a t i v e m a s s
c u s t o m i z e d T- s h i r t
a n d b a g s
G e n e r a t i v e M o t i o n
A r t
G e n e r a t i v e
M o t o r c y c l e
s w i n g a r m D e s i g n
G l i t c h e
G o o g l e A d s e n s e
G o o g l e
A r t s & C u l t u r e
G o o g l e A u t o D r a w
G o o g l e D r e a m
G o o g l e W e b
D e s i g n e r
H a c k R o d
H a i r M o d e l i n g
J u k e d e c k
K i n e m a t i c s D r e s s
L e a r n i n g
P e r c e p t u a l S h a p e
S t y l e S i m i l a r i t y
l o g o . s q u a r e s p a c e
L o g o j o y
M a g e n t a
M a g i s t o
M a n d e l b u l b
M a r k M a k e r
M a r v e l a p p
M o l e c u l e - s h o e s
M o o
N e u r a l I m a g e
A n a l o g i e s
N e u r a l D o o d l e
N e u r a l P a t c h
N o u n p r o j e c t
O u t s y s t e m s
P a r s i n g S e w i n g
P a t t e r n s i n t o 3 D
G a r m e n t s
P r i s m a
P r o c e s s i n g
F o u n d a t i o n
P r o j e c t M U S E
P r o j e c t : L e a r n i n g
V i s u a l C l o t h i n g
S t y l e
P r o j e c t ɿ T h e N e x t
R e m b r a n d t
R e c o n g n i t i o n
R e w r i t e E W R I T E
R o b o t i c P a i n t e r
S e l P h
S E N S Y
S h a d o w D r a w
S T Y L U M I A
Ta i l o r B r a n d s
Ta s t e A n a l y t i c s
T h e G r i d
T h e N e s t
R e m b r a n d t
T h r e a d
V i s u a l C l o t h i n g
S t y l e L e a r n i n g
S y s t e m
Vo x M e d i a
W e b c a m
W i b b i t z
W i x
Ya n d e x . L a u n c h e r
Yu r y Ve t r o v
Z o o m o r p h i c
D e s i g n
Ѩ ཬ ਗ਼ ൝
Ո + 1
䲳 ࢠ Պ ٕ
ى ٦ ်
ਂ
ၜ ඌ ࡗ L B C
100+
ೳϓϩμΫτ
ػցɿ֤λεΫͷೳԽ͞ΕΔՄೳੑʢ%ʣ
ཧ ŞžŚŘũŎŲƄ ŠŶœŬşŖŢŔƃ ඇॏෳମྗ࿑ಇ ૉࡐऩू ใॲཧ ॏෳମྗ࿑ಇ
9 18 20 25 64 69 78
A. I.
ػց