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.
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
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
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
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.
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
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
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.
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
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
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
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
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
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
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
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
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
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
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
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/)