I apologize, upon reviewing the document I do not see enough context to accurately summarize it. The document appears to be a mix of text in different languages discussing various AI systems like Watson, Google, and Azure but it is difficult for me to determine the overall topic or compare the different sections. For a proper summary, more context would be needed about the intent and purpose of the document.

1. Watson
AI vs

2. -0.965659
http://www.lr.pi.titech.ac.jp/~takamura/pndic_ja.html

5. 詳細な評価基準は、別な機会に。

6. :
aps-smp.wav
( )
Watson
Google
Microsoft

7. 評価: 音声学に関する学会発表時の音声データを用いた評価
今回：2018/11実施 前回：2017/12実施
G
W A
良質なデータなので、これくらいの精度は出ないとね・・・

8. サービス 正解
誤り 単語認識率 単語正解率
脱落 置換 挿入 (Correct) （Accuracy)
1 Watson 110 1 14 2 88.00 86.40
2 Google 120 0 7 0 94.49 96.00
3 Azure 82 15 25 5 67.21 61.60
サービス 正解
誤り 単語認識率 単語正解率
脱落 置換 挿入 (Correct) （Accuracy)
1 Watson 99 12 14 9 79.2 72.0
2 Google 111 6 8 2 88.8 87.2
3 Azure 86 13 25 3 69.35 66.4
2018/11 2017/12
( )
Watson +8.8 +14.4
Google +5.69 +8.8
Azure -2.14 -4.8
W
G A
:
Watson
精度の変化

9. GAN vs (Watson, Azure, Google, Amazon)

10. GOAL
知るー

11. https://bijutsutecho.com/magazine/news/headline/18719

12. ゙ ゙
GAN DCGAN(Deep Convolutional Generative Adversarial Networks)

13. GAN
Generative Adversal Networks
(Facebook AI )

14. IN OUT

15. ホンモノ
偽札偽造者
警察ニセ札
鮮

17. Generator
( )
Discrimnator
( )
Discrimnator
Generator

18. data x
(Discriminator)
data z
=0
=1
Generator

19. data x
=0.6
=0.4
( 0.6, 0.4)
1
0.4
Discriminator

20. (Generator)
Generator
data z
Generatordata x

21. data z
=0.3
=0.7
Discriminator
( )
Generator Discriminator
Discriminator
data z
本物と認識させるため、その誤差を埋める
パラメータ設定を学習によって獲得する。
Generatorが目指している
Discriminatorの判定
本物画像=1.0
生成画像=0.0

22. http://www.afpbb.com/articles/-/3194763?pid=20640893
min
𝐺
max
𝐷
𝑉(𝐷, 𝐺) = 𝔼 𝑥~𝑝 𝑑𝑎𝑡𝑎(𝑥)
log 𝐷(𝑥) + 𝔼 𝑧~𝑝 𝑧(𝑍)
log(1 − 𝐷 𝐺 𝑧 )
゙ ゙

24. DCGAN(Deep Convolutional Generative Adversarial Networks)
512
256
128
3
100 z
4
4
8
8
16
16
32
32
conv1
conv2
conv3
G(z)
stride 2 stride 2 stride 2 stride 2

25. 10002000300040005000100002500030000

26. START100020005000600070008000900010000

27. Watson giant panda 0.97 giant panda
0.93
giant panda 0.94 giant panda 0.99 giant panda 0.50
Azure animal 0.99
giant panda 0.70
panda 0.36
animal 0.99
giant panda
0.42
panda 0.42
animal 0.99
giant panda 0.42
panda 0.42
animal 0.99
panda 0.56
giant panda 0.09
animal 0.99
giant panda 0.22
panda 0.21
Google Nose 90
Terrestrial animal 74
Green 91
Head 88
SnapShot 83
Terrestrial animal 74
Bear 57
Terrestrial animal 91 Terrestrial animal 94
Amazon Soccer 99.3 giant panda 0.9
8.2
Team Sport 97.6
Soccer 97.6
animal 97.8
giant panda 97.8
Soccer ball 99.9
Watson animal 0.93
giant panda 0.90
Azure animal 0.99
giant panda 0.70
panda 0.36
Google Terrestrial animal 74
Bear 94
Amazon Giant Panda 97.1
本物画像
生成画像の識別

28. START1000200050006000700080009000100002500030000400006000080000100000

29. giant panda 0.94
Azure age 33
Female
age 33
Male
age 32
Female
Google
Joy 96% Joy 87% Joy 62%
Amazon Head 99.4
Art 95.8
Art 84.2 Game 98.2
Chess 98.2
Art 96.2
生成画像の識別
google

30. Watson plant 0.91
barbados cherry 0.80
plant 0.80
vegetable 0.80
plum tomato 0.71
plant 0.84
cranberry bush 0. 60
plum tomato 0.71
nutrition( )
food 0.83
staffed tomato 0.83
Azure flower 0.20
berry 0.16
tomato 0.041
vegetable 0. 78
fruit 0.71
tomato 0.51
fruito,62
Art 0.11
strawberry 0.11
tomato 0.39
food 0.09
vegetable 0.06
Google Food 85
Plant 77
Tomato 55
Natural Foods 95
Food 86
Tomato 71
plant 59 Food 92
Natural Foods 87
Tomato 73
Amazon Produce 99.3 99.2
Food 99.2
plant 99.4
Food 95.3
Produce 95.3
plant 99.4
Food 97.3
Fruit 97.3
plant 99.4
Fruit 91.9
Food 91.9
barbados cherry staffed tomato
生成画像の識別
plum tomato

31. トマトの学習過程

32. animal 0.88
mammal( ) 0.82
marten cat( ) 0.75
Dandie Dinmont dog
1.0
terrier dog 1.0
dod 1.0
guitar 0.6
uke(guiter) 0.60
animal 0.60
animal 0.88
dog 0.71
spaniel dog 0.64
Azure animal 0.80
mammal 0.76
dog 0.64
mammal 0.81
dog 0.68
puppy 0.07
animal 0.40
dog 0.15
animal 0.96
mammal 0.93
dog 0.63
hay 0.57
Google Chow Chow 84
Nose 84
Candiae( ) 82
Canidae( ) 90
Dog 88
Nose 87
Dog Breed 84
Animal 91
Canidae 90
Snout 83
Norfolk Terrier 76
Snout 83
Sporting Group 62
Border Terrier 60
Norfolk Terrier 53
Amazon Animal 87.9
Mammal 81.9
Wildlife( ) 66.8
Lesser Panda 66.8
Bear 66.8
Toy 82.9
Plush( ) 67.3
Mammal 59.8
Dog 59.8
Animal 86
Pet 80.8
Dog 58.7
Mammal 96.2
Animal 96.2
Dog 82.9
spaniel dogDandie Dinmont dogChow Chowmarten cat( )

33. Watson harbor 0.66
shelter 0.66
natural elevation 0.60
nature 0.53
building 0.59
cornice(building) 0.58
ribbonfish 0.57
sky 0.70
building 0.66
ziggurat 0.66
(ancient templr)
animal 0.79
fish 0.72
aquatic vertebrate 0.72
( )
Azure landscape 0.09
sky 0.06
painting 0.05
animal 0.96
mammal 0.65
giraffe 0.10
rock 0.24
shore 0.15
beach 0.15
animal 0.90
winter 0.21
art 0.15
Google Motor Vehicle 90
Mode Of Transport 81
Vehicle 80
Geological Phenomenon 80
( )
Tree 75
Sky 89
Architecture 65
Terrain( ) 60
Tree 89
Wood 65
Rock 54
Amazon Transportation 97.9
Vehicle 97.9
Truck 97.9
Outdoors 90.5
Nature 87.2
Landscape 87.2
Panoramic 87.2
Outdoors 87.2
Building 99.2
Architecture 99.2
temple 91.6
Outdoors 97.7
Landscape 97.7
Panoramic 97.7
Building 93
生成画像の識別
Watson building 0.85
synagogue( ) 0.72
religious residence( ) 0.
Azure sky 0.99
building 0.82
root 0.40
Google Nagoya Castle 83
Amazon Architecture 99,7
Building 99.7
Castle 99.3
本物画像、名古屋城
google

40. THANKS!
Any questions?