Fujitsu World Tour 2017 shaping tomorrow with you AI Technologies to Real... · Meaning Being agile and intense ... Human Centric AI Zinrai Sensing and Recognition Knowledge Processing

0 Copyright 2017 FUJITSU

Intel Inside®. Powerful Productivity Outside. Intel Inside®. Powerful Productivity Outside.

shaping tomorrow with you

Fujitsu World Tour 2017 Fujitsu North America Technology Forum 2017


Intel Inside®. Powerful Productivity Outside.

Connecting AI Technologies to Real-World Needs

Dr. Fumihiro Maruyama

Senior Expert

Artificial Intelligence Research Center

FUJITSU LABORATORIES LTD.

2 Copyright 2017 FUJITSU LABORATORIES LTD.

Agenda

1. FUJITSU AI Technology Brand “Zinrai”

2. Cutting-edge AI technologies being developed

by Fujitsu Laboratories

3. Connecting AI Technologies to Real-World Needs


Fujitsu’s AI goals

To create AI that collaborates with

people and is human centric

To create AI that continuously evolves

To create AI that can be incorporated in products and services then deployed


Meaning Being agile and intense

Concept behind the brand name

Dynamically realizing innovation in business and society by rapidly supporting decision making and actions

FUJITSU AI Technology Brand


A comprehensive framework for AI

ee p l e a rn i n g

Neuroscience

Machine learning

Social receptivity Simulation

- Image recognition

- Voice recognition

- Emotion/state

recognition

- Natural-language

processing

- Knowledge processing

& discovery

- Pattern discovery

- Inference &

Planning

- Prediction &

optimization

- Interactivity &

recommendation

Human Centric AI Zinrai

Sensing and Recognition Knowledge Processing Decision and support

Learning

Advanced research

Deep learning Machine learning Reinforcement learning

Neuroscience Social receptivity Simulation

People / Businesses / Society Actuation Sensing

- Image recognition

- Voice recognition

- Emotion/state

recognition

- Natural-language processing - Knowledge processing & discovery - Pattern discovery

- Inference & Planning - Prediction & optimization - Interactivity & recommendation


Cutting-edge AI technologies developed by Fujitsu Laboratories


We extend the scope of Deep Learning

Deep Learning

Breakthrough technology of machine learning that extracts knowledge from data

Automates feature extraction, a fundamental issue of machine learning

Time-series data Graph data Image Voice Text

Vehicle Recognition

Company A: White male

Age: 30

Cloth: Gray

Backpack

: Pink

Person Recognition

Handwriting Recognition

Practical application

Fujitsu Laboratories’ scope Conventional scope

Original (2016/02)

Leveraged by topological data analysis

Original (2016/10)

Based on tensor expression


Classification of Time-Series Data

It is difficult to accurately classify time-series data with extreme oscillations, such as that received from IoT devices.

Fujitsu has developed deep learning technology that uses advanced chaos theory and topology to automatically and accurately classify erratic time-series data.

Time[s] 0 20 40 60 80 100 120

G y r o d a t a

-2

-1.5

-1

-0.5

0

0.5

1

エレベーター内での移動 A エレベーター内での移動 B ランニングマシーン A ランニングマシーン B ステッパーマシーン A ステッパーマシーン B

半径0 100 200 300 400 500 600

Betti数

0

10

20

30

40

50

60

70

エレベーター内での移動Aエレベーター内での移動BランニングマシーンAランニングマシーンBステッパーマシーンAステッパーマシーンB

・・・

Classification

Step 1: Graphically represents time-series data using chaos theory (Attractor)

Movement A within elevator Movement B within elevator

Treadmill A Treadmill B

Stair-stepper A Stair-stepper B

Step 3: Learning and classification using a convolutional neural network

Accuracy: 85% (25% better than conventional methods)

Step 2: Quantifies diagrams using topology (Topological Data Analysis)

*UC Irvine Machine Learning

Repository benchmark test

To be incorporated into an IoT product in FY2016. Other time-series data includes log data from servers and sensor data obtained during sleep.

Gyro sensor data


(デモビデオ)

Classification of Time-Series Data Demo


Deep Learning with graph-structured data

To elicit new insights from graph-structured data that expresses connections between people and things

Technology that allows existing deep learning to be applied to graph-structured data New tensor factorization technology converts graph-structured data to a

uniform expression

Optimizing uniform expressions and neural network learning simultaneously

Conventional backpropagation

Extended backpropagation

グラフ全体構造を含んだ統一的表現（テンソル表現）に変換

統一的表現

の最適化人手の設計(赤)より多様な特徴量を自動生成

Tensor-based uniform expression including whole structure

Tensor factorization

Wider variety of features than handmade (red)

Technology

Purpose

Class A Class B


Graph data appear in various business domains

Healthcare/Drug Discovery Biology Graph data that expresses relationships between people and things

Social Network Security

Telecom/Mobile Supply Chain Finance

Internet


IT drug discovery: virtual screening Demo

Predicting a bond between a protein and chemical compounds


Outcomes in various domains

Discovered new insights and greatly improved learning accuracy

Account transaction Credit risk prediction

Detect high-risk

loans

False positive rate:

About 20% reduction

【Benchmark】 Open Data Institute UK P2P lending market dataset

IT drug discovery Virtual screening

Discovered about 200 new protein-compound bond features

Activity prediction accuracy: About 10% increase

【Benchmark】 PubChem BioAssay data set

Communication log Intrusion detection

Detect intrusions via

unexpected paths

False positive rate:

About 30% reduction

【Benchmark】 DARPA Intrusion Detection Evaluation Data Set

Fintech IoT Medical/Drug

Outcome


Linked Open Data (LOD）

LOD

Large-scale Graph DB

Store

Open to the public since January 2014

Store and search LODs on the web

RDF Store Attach links

Co

nve

rsio

n

Convert and integrate data into linked data

http://lod4all.net

Platform for the contest

New applications utilizing disparate data

Text

Open to the public since December 2014

http://evacva.net

Visualizing characteristics of the region

Large-scale data storing and searching technology for LOD

Developed and published LOD4ALL service based on fast graph database engine in collaboration with INSIGHT

Collaboration with INSIGHT

EvaCva

http://lod4all.net/

http://evacva.net/

http://evacva.net/


Corporate analysis (HighStreet)

Purpose

Technology

Outcome

Cross-sectional analysis of various aspects of corporate activities from financial statements, stock prices, products and reputations

Discovery of new relationships beyond performance comparison and business transactions

Integrating various data including financial and social network information

Dynamic graph network analysis to detect similarities and anomalies

Discover discrepancies by integrating multiple reports

Visualize relations between enterprises via people by integrating executive lists


AMED: Integrated Database for Clinical Genome

Variant DB

PubMed

Drug DB

drugBank

Inference Engine based on Machine Learning

LOD Knowledge Base Original DB

Interpretations Inference

Evidence Candidate

Treatments

ClinVar

Academic Paper

LOD Search Engine

AI Curation@11 hospital Aggregate several databases

Dementia

Cancer

Infection etc.

Refractory disease

Clinical Info. Genome Info.

Integrated Database for Clinical Genome

Integration

To achieve Genome Medicine

Gene

Protein Function

Disease

Drug

Pathogenic

Fujitsu and Kyoto University Start R&D for AI that Infers Clinical Interpretations of Genome and Gene Polymorphism

Building a knowledge base that aggregates academic paper and public databases in the medical field, using LOD utilization platform

Inferring clinical interpretations of Gene Variant using the unique machine learning technology


Collaborative Research with San Carlos Hospital

Purpose

Technology

Outcome

New healthcare solution designed to improve clinical decision-making

HIKARI gives clinicians more time and better information to help the patient

HIKARI Advanced Clinical Research Information System Knowledge discovery by combining medical records and public open data

Anonymization of integrated data gathered from both clinical and non-clinical data sources for ensuring patient privacy

HIKARI

Compare results

30 patients

Clinical history

36,000 patients

Non-clinical data

Less than 5 seconds

5 senior clinicians (18-25 years of experience)

~ 20 minutes per patient


Interactive Q&A

Semantic Structure

Analysis

Intention Detection

Generating answer candidates with relevant information

Speech Understanding

Sister

Hokkaido

Friend

Go Lives

Hawaii

Go

Wish

Place

Actor

Companion

Not destination

Knowledge-based Dialog Generation

Interaction Data

Machine Learning

LOD

Interaction History

Selecting an answer Selection Rule

Knowledge Incorporate

I want to go to

Hawaii.

I will go to Hokkaido, where my sister lives,

with a friend.

Hokkaido.

It is still cold there.

Machine Learning

Going to Hawaii is a wish. The real destination is Hokkaido. The companion is not a sister but a friend

Purpose

Technology

Extract necessary information through natural interaction with the user

Accurate information extraction by analyzing relations between words

Realizing natural interaction by utilizing LOD

The customer highly appreciated the merit of the technology based on a trial Looking at deployment in customer-facing business and sales

Outcome


Interactive Q&A Demo


Connecting AI Technologies to Real-World Needs


Network

Automated Operation Fault Recovery

SDN Optimization

AI

IoT Business

Self-Driving

Congestion Mitigation

Scene Support

Video Recognition

Services

AI is relevant to every business

Cloud/ Middleware

Automated Operation

Fault Recovery

Monozukuri

Advanced Industrial Robot Image-based Decision

Fault Diagnosis

Healthcare

Image Diagnosis

Social Infrastructure

Automated Operation

Dialogue Assistance

Diagnosis

Retail

Automatic Ordering

Recommendation

Behavior Analysis

Product Line Automation Symptom Inspection

Contamination Test

Manufacturing Finance

Fraud Detection

Proxy Dialogue

Corporate Analysis

AI AI AI AI AI AI

AI

Function Installation

AI AI

AI Function Installation

Device Embedding

Server/ Data center

Automated Operation

Fault Recovery

Car Drone Wearable

AI

State Estimation

Drug Design

Device Embedding


Issues and Approaches

Issues

Bridging gaps between (general-purpose) AI technologies and real-world needs

Streamlining and accelerating of application processes

Ongoing approaches

PoCs/PoBs with customers

Sharing knowledge about customers inside Fujitsu

Providing application-oriented APIs in addition to function-oriented ones


30 APIs will be available (some are from April 2017) Function-oriented APIs for basic use and application-oriented APIs for rapid development Proven through more than 300 AI-related business projects and field trials

Zinrai Platform Service

Image processing

Speech processing

Emotion and state recognition

Natural- language processing

Knowledge processing and discovery

Pattern discovery

Inference and planning

Prediction & optimization

Conversations & recommen -dations

Function-

oriented

APIs

Knowledge Processing Sensing & Recognition Decision & Support

Application- oriented APIs

Traffic image recognition

Domain specific semantic search

FAQ search Company information searching

Credit scoring

Demand prediction

Communi -cations bot

Delivery planning

Production and logistics network

Image recognition

Speech to text

Emotion recognition

Text analysis

Knowledge structuring Classification Inference Prediction

Dialog generation

Handwritten-

text recognition

Speech synthesis

Line-of-sight recognition

Speech comprehension

Knowledge searching Discrimination Optimization

Question- answering

Video recognition

Behavior recognition Matching

State recognition


Fujitsu’s Advanced Technologies Applied to APIs

Domain Specific Semantic Search

Image processing

Speech processing

Emotion and state recognition

Natural- language processing

Knowledge processing and discovery

Pattern discovery

Inference and planning

Prediction & optimization

Conversations & recommen -dations

Function-

oriented

APIs

Knowledge Processing Sensing & Recognition

Application- oriented APIs

Traffic image recognition FAQ Search

Company information searching

Credit scoring

Demand prediction


Delivery planning

Production and logistics network

Image recognition

Speech to text

Emotion recognition

Text analysis

Knowledge structuring Classification Inference Prediction Dialog

generation

Handwritten- text recognition

Speech synthesis

Line-of-sight recognition


Knowledge searching Discrimination Optimization Question-

answering

Video recognition

Behavior recognition Matching

State recognition

・Image processing with the

world's fastest class of

deep learning processing

Traffic image recognition

Image recognition

Handwritten- text recognition

Video recognition

State recognition

Domain specific semantic search

・The world's largest scale LOD search

・Data linking technology with high

accuracy

Text analysis

Knowledge structuring

Knowledge searching

Company information searching

・High-precision technology for context

comprehension

・Automatic, natural dialog generation


Dialog generation


Question- answering

FAQ search

Decision & Support


Intel Inside®. Powerful Productivity Outside.

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Fujitsu World Tour 2017 shaping tomorrow with you AI Technologies to Real... · Meaning Being agile and intense ... Human Centric AI Zinrai Sensing and Recognition Knowledge Processing