fujitsu laboratories’ r&d targeting intelligent computing

0 Copyright 2015 FUJITSU

Human Centric Innovation

in Action

Fujitsu Forum 2015

18th – 19th November


Fujitsu Laboratories’ R&D Targeting Intelligent Computing

Dr. Hirotaka Hara Director and Heard of Knowledge Information Processing Laboratories

Fujitsu Laboratories Ltd.


1. FUJITSU AI Technology Brand “Zinrai”

2. Systematizing AI technologies and deploying them for products/services

3. Cutting-edge AI technologies developed by Fujitsu Laboratories

Agenda


FUJITSU AI Technology Brand “Zinrai”

1.


Enables customers’ digital transformation Incorporates cutting-edge cloud, mobile, big data, IoT, and AI

technologies

AI: Artificial intelligence IoT: Internet of Things SoE: Systems of Engagement SoR: Systems of Record

FUJITSU Digital Business Platform “MetaArc” Announced in

September 2015

Existing information systems （SoR）

Work efficiencies

Cost reductions

Digital transformation of business （SoE） Transformation of existing business processes

Creation of new products and services

Ecosystem of multiple companies

Digital Business Platform “MetaArc” Cloud

Links between SoE and SoR

IoT Big Data Mobile AI


AI technology development activities

Applied for more than 100 AI-related patents since 2008 Technology

▲Risk analysis prediction software “QRMining”（2011/12）

～2013 2015

▲SCM model predictive control（2014/3）

▲Analysis scenario recommendation（2012/8） ▲Predictive model generation from massive datasets (2015/9）

▲Jointly sets up center of excellence in Singapore（2014/10）

▲Establishes joint research unit on mathematical techniques with Kyushu University (2014/9）

2014

▲Field trial at Fukuoka Airport（2015/9）

▲Participates in Artificial Brain Project (2012/9）

▲Voice search software “VoiceTracking/KeywordFinder”（2009/2）

▲Synthesis of various voices（2014/3） ▲Chinese handwriting recognition

exceeds human levels（2015/9）

▲3-D gesture analysis（2013/5） ▲Human flow recognition（2015/3）

▲Gaze tracking “EyeExpert”

（2015/10）

▲Revolutionize access to Open Data （2013/4）

▲Assisted independent living project in Ireland “KIDUKU” (2013/6） ▲Early detection of motor function anomaly（2015/3）

▲Professional baseball game image search service

（2015/4）

▲Spotting applicable areas in material from voice (2015/4）

▲High-speed malware detection（2014/4）

▲Identifying users vulnerable to cyber attack based on behavioral and psychological characteristics（2015/1）

▲Medical image diagnosis（2015/2）

▲Automatic generation of image analysis program（2014/9） Technology

development

Collaboration

▲Consumer behavior analysis service “Do-Cube” (2014/3）

▲Small-enough gaze tracking（2012/10）

▲Automatic tagging of scenes（2010/11）

▲Phone scam detection（2012/3）


PoC: Proofs of Concept PoB: Proofs of Business

Customer analysis /Marketing About 60 cases

Traffic/Disaster

About 40 cases

Top 8 fields of PoC/PoB

Product traceability About 30 cases

Visualizing factories About 30 cases

Watching over the elderly and children About 30 cases

Equipment surveillance /maintenance About 20 cases

Flow analysis of store customers About 20 cases

Advanced agriculture /livestock farming About 30 cases

About 300 PoC/PoB are in action

Collaborations with customers toward digital innovation


What AI Fujitsu aims to provide

AI that is in collaboration with

people and human centric

AI that grows continuously

AI that can be incorporated in products/services and provided


Meaning Being agile and intense

Idea behind the brand name

Realizing innovations in business and society dynamically by supporting people’s decisions and actions speedily

FUJITSU AI Technology Brand


Systematizing AI technologies and deploying them for products/services

2.


People/Businesses/Society

Systematizing AI technologies of Fujitsu

Sensing Actuation

Sensing and Recognition

Image recognition

Voice recognition

Emotion/state recognition

Knowledge processing

Natural-language processing Knowledge processing & discovery

Pattern discovery

Decision and support

Inference & planning Prediction & optimization Interactivity & recommendation

Social receptivity Simulation Neuroscience Advanced research

Machine learning Reinforcement learning Deep Learning Learning





Strengths of Zinrai

Learning technology Supports continuous growth of AI by drawing useful knowledge and patterns through daily learning

Chinese handwriting recognition

Detecting cyber attacks

Preventing resignations Stock optimization

Affective media processing technology


Mathematical technology

Uses five senses like people and processes people’s feelings, recognition, and consideration

Understanding human emotions

Phone scam detection

Creates knowledge that not only people can understand but also computers can process

Medical decision -making support

Improvement of banking supervision

Finds solutions to public or business problems using a supercomputer Can a robot pass

the Univ. of Tokyo entrance exam?

Tsunami inundation prediction Gaze tracking

Finger-operated interface Call centers

Visualizing regional features

Restoration planning

Mitigating congestions


Provides Zinrai as a service on top of Digital Business Platform “MetaArc”

Deployment for “MetaArc”

IoT: Internet of Things SoE: Systems of Engagement SoR: Systems of Record

Existing information systems （SoR）

Work efficiencies

Cost reductions

Digital transformation of business （SoE） Transformation of existing business processes

Creation of new products and services

Ecosystem of multiple companies

Digital Business Platform “MetaArc” Cloud

Links between SoE and SoR

IoT Big Data Mobile Zinrai


Deployment for products/services

Provides products and services with Zinrai technologies incorporated

Industry/Business Applications

Security

Manufacturing

Healthcare

Marketing

City/Traffic

Finance

Products/ Services


Application of anomaly detection Fault prognosis of plant facility using huge sensor data

Prevent sudden halt of production line and improve productive efficiency

Apply machine learning to generate 'normal state model' from sensor data

Detect ‘anomaly’ (unusual state) based on deviance from normal state model

Big Data Solution for Anomaly Detection powered by Zinrai

Machine Learning Modeling

Apply machine learning technology to multiple (tens of thousands) series of sensor data to model normal state of machinery/facility

Accumulated Sensor Data

Real-time Anomaly Detection Operation

Normal State Model Anomaly Detection Apply real-time sensor data to ‘normal

state model’ and detect precursor of failure

Real-time

Sensor Data Alert


New service-based solutions: AI Application Consulting Availability date: December 2015

Customer Finance

Public/Government

University/ Research institution

Manufacturing

Distribution

Medical etc.

Fujitsu

AI Application Consulting Department Formed on November 1, 2015

Researcher Engineer Curator

Total of 200 people

Creation of new products/services

Reform of existing business

AI-specialist consultants co-create innovation together with the customer

“Co-creation”


Cutting-edge AI technologies developed by Fujitsu Laboratories

3.


Why AI now?

1950 1970 1980 1990 2000 2010 1960

★”Artificial Intelligence” （Dartmouth Conf.）【’56】

★Big AI projects （Japan ICOT ’82, US MCC ’83, UK Alvey ’84）

★AI failure【’73】（Lighthill report)

★DEC R1 【’82】

★Victory in chess 【’97】

★ELIZA 【’64】

★Turing test【’50】

★Bayesian network【’88】 ★Lisp【’58】 ★Prolog【’72】

★Back propagation【’86】 ★Perceptron【’62】

Projects

★Deep Learning in limelight【’12】

★Brain Science projects （BRAIN Initiative, Human Brain）

★Japanese version of “Siri”【’12】

1st AI era

Search and reasoning Knowledge

Systems

★MYCIN 【’73】

Paradigms

2nd AI era 3rd AI era

Machine learning

Could not solve practical problems Ended in 1st AI winter

Technologies are advancing rapidly (Big data × Computer power × Machine learning). Practical applications are now possible.

Could not teach experts’ knowledge Ended in 2nd AI winter





Strengths of Zinrai

Learning technology Supports continuous growth of AI by drawing useful knowledge and patterns through daily learning

Chinese handwriting recognition

Detecting cyber attacks

Preventing resignations Stock optimization

Affective media processing technology


Mathematical technology

Uses five senses like people and processes people’s feelings, recognition, and consideration

Understanding human emotions

Phone scam detection

Creates knowledge that not only people can understand but also computers can process

Medical decision -making support

Improvement of banking supervision

Finds solutions to public or business problems using a supercomputer Can a robot pass

the Univ. of Tokyo entrance exam?

Tsunami inundation prediction Gaze tracking

Finger-operated interface Call centers

Visualizing regional features

Restoration planning

Mitigating congestions


Deep Learning

Leaning Technology

Modeling

Nerve Cells (Neurons)

Neuron Synapse

Neural Network

A Mobile Robot SATORU-Kun (In 1988, Three-Layer NN)

29

232

Object Recognition in Image (In 2015, Seven-Layer NN)

1.1M

730M

# of Neurons

# of Synapses

38,000 times

3,150,000 times

The latest technology of neural network (NN)


Deep Learning: Object Recognition in Image

Leaning Technology

1.1 M neurons 730 M synapses

Data：1.3M images 1,000 categories

Higher Accuracy than Conventional Machine Learning Automatic Acquisition of Object Features for Recognition

Projection Only

Demo


Chinese Handwriting Recognition

Technology

Effect

Efficiency of processing of Chinese handwritten document

“Handwriting recognition” using Fujitsu original Deep Leaning

Achieved 96.7% of recognition accuracy that exceeds the human performance

Visualization of the features that are learned among nerve cells in the process of character recognition

Learned character sample generation based on 3D-Distortion

Purpose

Leaning Technology (Use Case)


Application of Deep Learning

Learning Technology

Deep Learning

Marketing

Manufacturing

CRM

Finance

Medical Care

Urban transport

Image recognition/Video recognition/Voice recognition/Text recognition/Diagnostic imaging

Security

…

Future prediction/Automatic operation/Anomaly detection/Optimal control/・・・


Machine Learning

Detecting Cyber Attacks Enterprise are continuously exposed to various cyber-attacks.

Sophisticated attacks, are operated behind major attacks.

It is difficult to detect novel sophisticated attacks with manually analyzing IDS log.

Enterprise System IDS

DoS (major)

Virus (major)

Scanning vulnerability(major)

Attackers …

Foretaste of attack

Sophisticated attack

Novel-type attack

IDS log (Big-data)

Various attacks are mixed

Security operation center


Detecting Cyber Attacks: "Outlier clustering"

11 1 11 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 3 1 1 3 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 3 1 1 1 1 2 1 1 1 3 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

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1 1 1 1 1 1 1 1 2 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

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2 1 1 2 1 1 1 2 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1

3 2 3 1 1 11 1

11 1 1

1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 2 21 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 3 2 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 4 4 1 1 1 1 1 3 2 1 1 1 31 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 14 1 1 1 1 1 1 1 1 1

1 1 1 1 1 11 1 1 1 1 1

1 11 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 11 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1

1 1 11 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 11 1

11 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 11 1 1

Distribution of original data* Previous method

Our method

1 11 1 1 1 1 1 1

1 1 1 1 1 1 1 11 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1

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1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1

1 11

1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1

1 1 11 1 1

Evaluating low-frequency data sets as “outlier clusters”

Simplifying and grouping frequently-appearing data

Attackers’ IP address

Port

num

ber

*Actually n-dimension data

Detecting low-frequently attacks with “outlier clustering” technique.

Machine Learning


Detecting Cyber Attacks: Adopting for FUJITSU cloud

Machine Learning (Use Case)

Rare shape Same attack

Our method has extracted novel-type attack at short times.

“A novel type of distributed brute-force attacks” manually

extracted in three months last year.

Perceiving the contexts of “Outlier Clusters” with visualization.

Applying to security monitoring for FUJITSU cloud services.

Result of manually extraction Visualization of “Outlier Clusters ”


Machine Learning for Massive Datasets

Machine Learning

Parallel Data Processing (Apache Spark)

Quickly Generating Accurate Predictive Models Latest technology delivers rapid highly accurate results

Learning from datasets of 50 million records in a few hours

Selecting learning algorithms and configuration combinations for the most accurate results in an automatic manner.

Analysis

Result Big

Data

Machine

Learning

Select machine config.

Select

algorithm

Select

operating

condition Assess

accuracy

2. Selection and Tuning

1. Estimation during execution

Performance

knowledge end time, accuracy

Combi-

nations Tune

sampling rate

model Sampling Training set

Most accurate

Test set

model

size

4000

Learning time

size

1000

size

2000

size

4000

size

8000

size

1000

size

2000

size

1000

size

2000

size

4000

size

8000

size

16000 size

32000

Now

Eliminate from

Candidate

Accura

cy

Current Best

Demonstrated

at L19


Machine Learning for Massive Datasets

Machine Learning (Use Case)

Optimization

Energy Management

Weather

Power

Consumption the correlation

patterns

automatically learns and Data Sources produces predictive models

Humidity RPM

Voltage Temperaｔure

sensors for components in

a factory

Predictive

Model

Predictive

Model

Failure

Prediction

Manufacturing bxwxwxwy nn 2211

sensor data

weight


Affective Media Processing Technologies

Technologies that Sense and Understand You

A detailed user monitoring scheme realized by new visual sensing technologies and analysis algorithms.

Laser Sensing/Analysis 3D-sensing of user behavior from a distance

Gaze Tracking/Analysis Understanding users’ interests from a distance

Equip existing ICT systems with human visual systems


Gaze Tracking and Analysis


Long-range sensor for gaze tracking at a distance

Low cost, compact sensor for wide use in various applications

Gaze Database

By zooming in to track gaze, interests of customers browsing at a distance can also be understood

Multiple sensors are installed in-store to analyze which items are seen and compared.

Details ●●●●●●● ●●●●

Sensor Sensor

Sensor

Zoom in to track from several meters away

Info about item seen by customer

Use gaze data to analyze customer behavior

Store Display Sensor

1. Face parts detection

2. Eye region analysis & gaze calculation Pupil Corneal Reflection

Demo


Affective Media Processing Technology (Use Case)

Understanding users’ feelings

Objective

Technology

Future development

Customer service improvement and automation Combines data from multiple sensing technologies to infer users’ mental state (intent, emotions)

Service release planned for FY2016

Results Provide fine grained services adapted to users’ characteristics and conditions

Compact Gaze Tracking Sensor

Behavior sensing

Gaze tracking

Projection display

Apply

Accumulate

Timely display of relevant information when the user seems to be confused

Technology to provide feedback to the user

Technology to understand the user’s mental state

User Characteristics Database

Smart guidance

Demo



Affective Speech Processing

Speaker’s intent and emotion can be understood with cutting-edge speech analytics / acoustic sensing technologies from speech signal.

Scam Detection -- Detect emotional instability

Speech Analytics -- Understand speaker’s feelings

“Pleasure”

“Sorrow”

“Haste”

…

“Anger”

“Laughter”

“Excitement

”

…

speech signal

Ability to read between the lines is added to

conventional ICT.


Phone scam detection technology

Object

Technology

Effect

Prevents phone scams, a serious social problem in Japan

Detects phone scams by integrating results of keyword detection and emotional instability analysis by the false alarm rate of less than 1%

In the field trial, the number of cognitive case of phone scams was reduced by

half(Deterrence effect)

Detection system

FUJITSU original conversation analysis technology

(Processing spontaneous conversation)

Emotional instability Analysis

Keyword detection

Combined Decision

“Come here to explain

about the compensation.”

Notifying victim’s family

or relevant parties

Fraudster

Detecting remittance-solicitation phone scams

Conversation speech

Contact and other support

“Well, even

if you say that…”

Victim

Affective Media Processing Technologies (Use Case)


LOD（Linked Open Data）

Knowledge Processing

Network of data, published in a standard format on Web

Data can link to, and be linked from other data

LOD consists of More than

1,000 datasets

60 billion triples

500 million links

From various domains ,

not only governmental data

Machine Readable Knowledge Representation

Media

Geographic Life science

Publications Cross-domain

Government

LOD cloud net


LOD4ALL：LOD Utilization PF

Knowledge Processing (Use Case)

Renewal on July, 27, 2015： http://lod4all.net/

http://lod4all.net/

http://lod4all.net/


Evaluation of Local Cities based on LOD

Purpose

Technology

Result Support to plan city policies by comparing with similar cities

Fast graph-database engine for billions of LOD (LOD4ALL)

Evaluate dozens of indices from 1,000 kinds of statistical data

(EvaCva)

Evaluate local cities from economy, society and environment

＜EvaCva＞＜LOD＞



Improvement of Banking Supervision

Purpose

Technology

Result

Grasp of invisible malicious illegal activities such as hidden people network surrounding insider trading

Discovery of the insider trading that has been overlooked

Knowledge Processing（Use Case）

Detect hidden network of people relates to insider trading.

Heterogeneous Data Federation Framework using LOD

Dynamic Graph Network Analysis to Detect Similarity and Anomaly

Demonstrated

at J4


Collaborative Research with San Carlos Hospital

Purpose: Establishment of new treatments for mental disease

Technology:

Knowledge discovery by a fusion of medical records and public open data

Anonymization for keeping patients’ privacy

Result: Discovery of triggers of the pathogeny

E.g.: Relation between the moon’s wax and wane and the visit to the hospital


FUJITSU Spain

Angeles Delgado

San Carlos Hospital

Julio Mayol

FUJITSU Laboratories

Hideyuki Saso

Demonstrated

at M7


"Can a Robot Pass the University of Tokyo Entrance Exam?"

Mathematical Technology (Use Cae)

Target

Technology

Effect

Pass the University of Tokyo (Todai) entrance exam

Automatic natural language math problem solving base on our unique “QE* inference tech”

Deviation value: about 65 at a prep school practice exam [ improvement: about 15 points compared to 2014 ]

*QE: Quantifier Elimination

entrance exam

解答

input Output Answer Todai robot QE inference technology

automatic problem-solving Participation as the "math team"

for project

Math

Todai robot project

http://www.nii.ac.jp/en/


「東ロボ」技術詳細説明 10

Inference by computer algebra

Answer r = 2

circle(C) ∧ area(C) = πr2 radius(C) = r

Math knowledge base

word meaning

(x is) circle circle(x)

area (of x) area(x)

radius (of x) radius(x)

Database Math problems

Learning

Ex：Formula for a area of a circle

Find(r)[∀s. πs2 = 4π ∧ s > 0 → s = r ]

equivalent conversion of logical formulas

Language Understanding

Logical form

Problem “Find the radius of a circle with area 4π.”

Find(r)[ ∀C. circle(C)∧ area(C) = 4π → radius(C) = r ]

QE inference technology

Todai robot technologies for math problems

dictionary

Mathematical Technology (Use Case)

Demo


＊Cooperative research : “Fujitsu Social Mathematics Joint Research Unit” in Kyusyu university

Mathematical science, Applied Math

modeling Control

Analytics/Prediction Optimization

Social issues

human behavior and psychology

Solution

Social system design （Game theory/matching）

Social system evaluation （Agent-based social Simulation）

Social system modeling （Operational models that consider human’s mind and behavior）

Refine policies

Mathematical Technology (case example)

Raising Passenger Satisfaction at Fukuoka Airport

Target

Technology

Effect

Raising Passenger Satisfaction in terms of multiple aspects: congestion, security and staff assignment etc.

Social system design framework based on math technologies

Aiming at deploying the field trial results at Fukuoka airport to other airports


Tsunami Inundation Prediction


Target

Technology

Effect

Improvement of tsunami early warning systems

High-efficiency parallel computing of High-resolution tsunami inundation simulation

Tsunami’s inundation is predictable within 2 min after tsunami source is estimated.

Provided by IRIDeS, Tohoku University

Demo


Real-time Tsunami Hazard Map

Inundation

analysis

Inundation analysis

Assumptions

of possible

earthquakes

Earthquake

Historical

earthquakes

hazard maps

of local authorities

Real-time

hazard map

Execution time：

Days on a PC

10 sec - minutes

This technology

Execution time：

2 min on supercomputer

More than 5 min

Observation

Instant tsunami

source analysis



Brain Science

Pursuing human’s processing mechanisms

Board pattern recognition

Next-move decision

“Neural Basis of Intuitive Best Next-Move Generation in Board Game Experts”, Science 2011 Collaborative research between RIKEN BSI and Fujitsu

Big projects of brain science will reveal functions of neural circuits in the next decade.

Research with neuroscientists would bear a computer having ‘intuition’ required for complex problem solving.

Striatum

Precuneus

Differences between professionals, amateur experts, novices

Highly activated areas of professional board game players (Japanese ‘Shogi’ players)


People/Businesses/Society

Systematizing AI technologies of Fujitsu

Sensing Actuation


Image recognition

Voice recognition

Emotion/state recognition


Natural-language processing Knowledge processing & discovery

Pattern discovery


Inference & planning Prediction & optimization Interactivity & recommendation

Social receptivity Simulation Neuroscience Advanced research

Machine learning Reinforcement learning Deep Learning Learning