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次世代 への挑戦
と 時代の次へ
Mar. 22nd , 2019
森 正弥 Masaya Mori
楽天株式会社 執行役員
楽天技術研究所 代表
2
雑談
• ElMo のような大量データに基づく成果も確かに続いてい
るが、Deep Learning と BigData だけの世界は終わり、次
世代AI への胎動が始まっている
• DQN
• Generative Adverasiral...
3
Trend of Deep Learning and Bigdata is coming to an end.
New paradigm is emerging.
4
• ~ Dec. 2017:
• Google
• ~ Aug. 2018:
• DeepL.com (they didn’t use Bigdata)
• ~ Now:
• Facebook AI Research
5
スコア
出展:Google AI Blog https://ai.googleblog.com/2017/08/transformer-novel-neural-
network.html (アクセス日 2019/03/26)
Transf...
6
Deep
learning
Small Dataset
Big Dataset
Great AI
Deep
learning
So so AI
Other Dataset
2nd Deep
Learning
Other so so AI
C...
7
• GAN, Adversarial Network
• AlphaGo Zero (Deep RL)
• AICO (ad banner generator & CTR predictor)
• Predictive Learning
R...
8
A Variety of
Dataset
Just bigdata
9
Under one Vision
10
Other projects will be organized under one vision.
Pitari
AIris
RCP
Creative
AI
Projects for the platform
Fraud
Detecti...
11
Core Algorithms
12
Machine translation
Voice recognition
Automatic Speech Recognition for Product Voice Search
Product Data science
Sentim...
13
Machine translation
Automatic Speech Recognition for Product Voice Search
Product Data science
Sentiment Analysis
+
Cat...
14
RIT Machine Translation matches Human Translation
Rated by bi-lingual speakers on
a 5-point scale for adequacy
and flue...
15
Face recognition
Contents Generation (Creative AI)
Gender, Age, Emotion Recognition
Product Recognition
16
Face recognition Gender, Age, Emotion Recognition
Product Recognition
AI understanding Face AI understanding User visua...
17
SNEAKER SALE Up to 30% off
Generation Prediction
30
Sneaker Sale
Up to
OFF
%
Sneaker
up to 30% off
Sale
Sneaker
up to 3...
18
Mature-Level
At leveraging Deep Learning
Vision
Voice
(ASR)
Language
Voice
(TTS)
Big Gap, but bridgingSome Gap
The stra...
19
More Advanced Technologies
20
Data Augmentation
Transfer Learning
21
Artificially increase the volume of the training dataset to improve accuracy. It is good for when data is
insufficient,...
22
Data augmentation example :
• Shifting vertical/horizontally
• Invert vertically/horizontally
• Enlarging/Minimizing
• ...
23
Data augmentation example :
• Mixup is combination of two
training data
Method Sample Image
*C. Summers et al., "Improv...
24
A model developed for a task is reused as the starting point for a model on a second task. By
transferring, we can get ...
25
With a pretrained model with Japan`s Ichiba data, transfer learning can help extract prospective
customer, product reco...
26
Ensemble Learning
Multi-modal Learning
Reinforcement Learning
Meta Learning
27
Ensemble learning method is techniques that create multiple models and then combine them to improve
prediction accuracy...
28
Predict USD/JPY, NK225 and JGB on daily or weekly basis from past data by using machine learning.
Ensemble learning is ...
29
Classify product catalog by using machine learning.
Ensemble learning is used as a method, and accuracy is improved.
Pr...
30
Detect merchants which can repay money from EC data with machine learning.
Ensemble learning is used as a method, and a...
31
Multi-modal learning is a model to learn from multiple data source(text, image, voice, etc.).
It is expected to high ac...
32
Item Genre Classification : with Multi-modal learning
Classifier based on
CNN/RNN
Final Result
Text Data
• Item Title
•...
33
Reinforcement Learning is machine learning on how software agents to take action in environment to maximize
some notion...
34
100%
50%
0%
Time
Trials
100%
50%
0%
Time
Trials
A
B
C
A wins!
A
B
C
Automatically
A / B Test BANDIT
100%
50%
0%
Time
Tr...
35
Example (Human case)
Skill of riding bicycle
= Stand up + Ascend or descend a staircase etc.
Meta learning is approach ...
楽天技術研究所の次世代AI 技術への挑戦
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楽天技術研究所の次世代AI 技術への挑戦

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In this study session, we raised a topic about new trends of AI technologies following a combination of deep learning and big data.
It would call for new AI architecture and require new challenge we should do to keep up with front runners.

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楽天技術研究所の次世代AI 技術への挑戦

  1. 1. 次世代 への挑戦 と 時代の次へ Mar. 22nd , 2019 森 正弥 Masaya Mori 楽天株式会社 執行役員 楽天技術研究所 代表
  2. 2. 2 雑談 • ElMo のような大量データに基づく成果も確かに続いてい るが、Deep Learning と BigData だけの世界は終わり、次 世代AI への胎動が始まっている • DQN • Generative Adverasiral Network • Transfer Learning、Data Augmentation • BERT、BackTranslation
  3. 3. 3 Trend of Deep Learning and Bigdata is coming to an end. New paradigm is emerging.
  4. 4. 4 • ~ Dec. 2017: • Google • ~ Aug. 2018: • DeepL.com (they didn’t use Bigdata) • ~ Now: • Facebook AI Research
  5. 5. 5 スコア 出展:Google AI Blog https://ai.googleblog.com/2017/08/transformer-novel-neural- network.html (アクセス日 2019/03/26) Transformer の高スコア、しかし、Back Translation Model はこれをや すやすとこえた。
  6. 6. 6 Deep learning Small Dataset Big Dataset Great AI Deep learning So so AI Other Dataset 2nd Deep Learning Other so so AI Connect & Loop Learning Super AI Interactive Loop
  7. 7. 7 • GAN, Adversarial Network • AlphaGo Zero (Deep RL) • AICO (ad banner generator & CTR predictor) • Predictive Learning Real Example Generated Example Generator Noise Source Discriminator GAN Real Fake
  8. 8. 8 A Variety of Dataset Just bigdata
  9. 9. 9 Under one Vision
  10. 10. 10 Other projects will be organized under one vision. Pitari AIris RCP Creative AI Projects for the platform Fraud Detection Data-based Trading Delivery Optimization Language Learning Next Candidates
  11. 11. 11 Core Algorithms
  12. 12. 12 Machine translation Voice recognition Automatic Speech Recognition for Product Voice Search Product Data science Sentiment Analysis + Category Grocery & food Subcategory Wine 我们真的很有诚意 了。 你说我一个老总都 亲自跑了好几趟了。 Machine translation
  13. 13. 13 Machine translation Automatic Speech Recognition for Product Voice Search Product Data science Sentiment Analysis + Category Grocery & food Subcategory Wine 我们真的很有诚意 了。 你说我一个老总都 亲自跑了好几趟了。 Machine translationAI organizing Chaotic Data AI understanding multi-languages AI understanding speechAI understanding Voice of Customers Voice recognition
  14. 14. 14 RIT Machine Translation matches Human Translation Rated by bi-lingual speakers on a 5-point scale for adequacy and fluency RIT Human Google for English to Spanish / Portuguese / French / Polish / German / Italian And, we‘re starting English to Japanese.
  15. 15. 15 Face recognition Contents Generation (Creative AI) Gender, Age, Emotion Recognition Product Recognition
  16. 16. 16 Face recognition Gender, Age, Emotion Recognition Product Recognition AI understanding Face AI understanding User visually AI understanding Object visuallyAI generating Digital Contents Contents Generation (Creative AI)
  17. 17. 17 SNEAKER SALE Up to 30% off Generation Prediction 30 Sneaker Sale Up to OFF % Sneaker up to 30% off Sale Sneaker up to 30% off Sale 30 Sneaker Sale Up to OFF % Image Segmentation Images Text Styles Assisting Graphic Design Process
  18. 18. 18 Mature-Level At leveraging Deep Learning Vision Voice (ASR) Language Voice (TTS) Big Gap, but bridgingSome Gap The strategies of each Program Management are different.
  19. 19. 19 More Advanced Technologies
  20. 20. 20 Data Augmentation Transfer Learning
  21. 21. 21 Artificially increase the volume of the training dataset to improve accuracy. It is good for when data is insufficient, quality is low, or data is imbalanced to a specific category. Small Dataset Small Dataset Big Dataset Data Augmentation Data is enough Accuracy is increased Data is insufficient
  22. 22. 22 Data augmentation example : • Shifting vertical/horizontally • Invert vertically/horizontally • Enlarging/Minimizing • Rotating • Tilting vertically/horizontally • Cropping • Changing contrast Method Sample Image *Google Website (Machine Learning Crash Course)
  23. 23. 23 Data augmentation example : • Mixup is combination of two training data Method Sample Image *C. Summers et al., "Improved Mixed-Example Data Augmentation", 2018
  24. 24. 24 A model developed for a task is reused as the starting point for a model on a second task. By transferring, we can get improved result with small dataset. Concept Use Cases Big Dataset Small Dataset Pre-Training Re-Training Output Transfer Autonomous cars  Realization of automatic cars by deep learning *Preferred Research (https://research.preferred.jp/2016/01/ces2016/) 43cm 20 cm (ex. Flower image) (ex. Animal image) TOYOTA / Preferred Networks
  25. 25. 25 With a pretrained model with Japan`s Ichiba data, transfer learning can help extract prospective customer, product recommendation and purchase prediction in US market . Japan EC data US EC data Pre-Training Transfer ・User purchase history ・Browsing history ・Review ・Advertisement click count ・Product search history… Prediction (in US market) ・Prospective customer extraction ・Product recommendation ・Purchase prediction … Re-Training
  26. 26. 26 Ensemble Learning Multi-modal Learning Reinforcement Learning Meta Learning
  27. 27. 27 Ensemble learning method is techniques that create multiple models and then combine them to improve prediction accuracy. Concept Use Cases  Predict demand forecast with high accuracy by using multiple learning model. Manufacturer *FUJITSU website “FUJITSU Business Application Operational Data Management & Analytics” Prediction accuracyModel B Output Model C Model A Ensemble Normal Output Model FUJITSU Predict demand forecast
  28. 28. 28 Predict USD/JPY, NK225 and JGB on daily or weekly basis from past data by using machine learning. Ensemble learning is used as a method, and accuracy is improved. Index ・・・ Model B Model C Model A Past Data Future Prediction Accuracy Ensemble learning Nikkei 225 Bond Currency Index ・・・ Nikkei 225 Bond Currency Input Machine Learning Output Predict price
  29. 29. 29 Classify product catalog by using machine learning. Ensemble learning is used as a method, and accuracy is improved. Product catalog data Classification (Taxonomy) Input Output ・Title ・Product description etc. Model B Model C Model A Accuracy Ensemble learning (XGBoost) Machine Learning
  30. 30. 30 Detect merchants which can repay money from EC data with machine learning. Ensemble learning is used as a method, and accuracy is improved. EC data Credibility Score Input Output Tons of inputs • Can repay • Cannot repay Judge MerchantsModel B Model C Model A Accuracy Ensemble learning Machine Learning 30 Sneaker Sale Up to OFF %
  31. 31. 31 Multi-modal learning is a model to learn from multiple data source(text, image, voice, etc.). It is expected to high accuracy than model which learn from single source Concept Text Voice Image Multi-modal learning  Increase accuracy of fraud item detection by using multimodal model : image, product name, description and price. EC Robotics  Develop ASVR(Audio-Visual Speech Recognition), which has high noise-robust with combination of sound and video signals, Use Cases *Waseda University, Ogata tetsuya (https://pdf.gakkai-web.net/gakkai/ieice/icd/html/2017/view/I_01_02.pdf) *Mercari, Engineering Blog “https://tech.mercari.com/entry/2018/04/24/164919”, Text Multi- modal source Single source Voice Image (Video)+ Honda Research Institute Mercari Image Text (Product name, Description etc. ) +
  32. 32. 32 Item Genre Classification : with Multi-modal learning Classifier based on CNN/RNN Final Result Text Data • Item Title • Item Description Image Data LSTM CNN
  33. 33. 33 Reinforcement Learning is machine learning on how software agents to take action in environment to maximize some notion of reward. Agents find optimal action model through trial and error. Software Agents User etc. Action Feedback Concept Use Cases Find optimal action model  Alibaba has adopted reinforcement learning to improve commodity search EC Tech  DeepMind’s AlphaGo beat champion in Go game. *Sigmoidal (https://sigmoidal.io/alphago-how-it-uses-reinforcement-learning-to-beat-go-masters/) *Analytics India Magazine (https://www.analyticsindiamag.com/how-alibaba-is-applying-virtual-taobao-to-simulate-e-commerce-environment/) Google Alibaba
  34. 34. 34 100% 50% 0% Time Trials 100% 50% 0% Time Trials A B C A wins! A B C Automatically A / B Test BANDIT 100% 50% 0% Time Trials A wins A B C Automatically+Dynamic Dynamic BANDIT B wins A wins! Static Environment change Evolution!
  35. 35. 35 Example (Human case) Skill of riding bicycle = Stand up + Ascend or descend a staircase etc. Meta learning is approach of learning to learn. It learn a variety of tasks from small amounts of data by utilizing past learning. • Learn task quickly from small amounts of data by utilizing past learning • Meta learning is deep learning algorithm close to human *Nikkei X TECH (https://tech.nikkeibp.co.jp/dm/atcl/mag/15/00189/00003/)

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