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Fujitsu Laboratories’ R&D Targeting Intelligent Computing

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The rapid advent and growth in leveraging Big Data and Machine Learning technologies have spurred acceleration of R&D to drive realization of Artificial Intelligence (AI), in which computers conduct intelligent tasks similar to humans. This session highlights our proposed directionality for advancement of Intelligent Computing, in addition to introducing latest R&D in Deep Learning, Q&A systems, and Emotion Cognition that Fujitsu Laboratories is engaging in.

Speaker:
Hirotaka Hara

Published in: Technology
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Fujitsu Laboratories’ R&D Targeting Intelligent Computing

  1. 1. 0 Copyright 2015 FUJITSU Human Centric Innovation in Action Fujitsu Forum 2015 18th – 19th November
  2. 2. 1 Copyright 2015 FUJITSU Fujitsu Laboratories’ R&D Targeting Intelligent Computing Dr. Hirotaka Hara Director and Heard of Knowledge Information Processing Laboratories Fujitsu Laboratories Ltd.
  3. 3. 2 Copyright 2015 FUJITSU 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
  4. 4. 3 Copyright 2015 FUJITSU FUJITSU AI Technology Brand “Zinrai” 1.
  5. 5. 4 Copyright 2015 FUJITSU  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 IoTBig DataMobile AI
  6. 6. 5 Copyright 2015 FUJITSU 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)
  7. 7. 6 Copyright 2015 FUJITSU 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
  8. 8. 7 Copyright 2015 FUJITSU 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
  9. 9. 8 Copyright 2015 FUJITSU  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
  10. 10. 9 Copyright 2015 FUJITSU Systematizing AI technologies and deploying them for products/services 2.
  11. 11. 10 Copyright 2015 FUJITSU 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 SimulationNeuroscience Advanced research Machine learning Reinforcement learningDeep LearningLearning
  12. 12. 11 Copyright 2015 FUJITSU Knowledge processing Sensing and Recognition Decision and support 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 Knowledge processing 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 bankingsupervision Finds solutions to public or business problems using a supercomputer Can a robot pass the Univ. of Tokyo entrance exam? Tsunami inundation predictionGaze tracking Finger-operated interface Call centers Visualizing regionalfeatures Restoration planning Mitigating congestions
  13. 13. 12 Copyright 2015 FUJITSU  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 IoTBig DataMobile Zinrai
  14. 14. 13 Copyright 2015 FUJITSU Deployment for products/services  Provides products and services with Zinrai technologies incorporated Industry/Business Applications Security Manufacturing Healthcare Marketing City/Traffic Finance Products/ Services
  15. 15. 14 Copyright 2015 FUJITSU 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 LearningModeling 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 DetectionOperation Normal State Model Anomaly Detection Apply real-time sensor data to ‘normal state model’ and detect precursor of failure Real-time Sensor Data Alert
  16. 16. 15 Copyright 2015 FUJITSU 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”
  17. 17. 16 Copyright 2015 FUJITSU Cutting-edge AI technologies developed by Fujitsu Laboratories 3.
  18. 18. 17 Copyright 2015 FUJITSU Why AI now? 1950 1970 1980 1990 2000 20101960 ★”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
  19. 19. 18 Copyright 2015 FUJITSU Knowledge processing Sensing and Recognition Decision and support 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 Knowledge processing 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 bankingsupervision Finds solutions to public or business problems using a supercomputer Can a robot pass the Univ. of Tokyo entrance exam? Tsunami inundation predictionGaze tracking Finger-operated interface Call centers Visualizing regionalfeatures Restoration planning Mitigating congestions
  20. 20. 19 Copyright 2015 FUJITSU Deep Learning Leaning Technology Modeling Nerve Cells (Neurons) NeuronSynapse 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)
  21. 21. 20 Copyright 2015 FUJITSU 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 Demo
  22. 22. 21 Copyright 2015 FUJITSU
  23. 23. 22 Copyright 2015 FUJITSU 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 Demo
  24. 24. 23 Copyright 2015 FUJITSU 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)
  25. 25. 24 Copyright 2015 FUJITSU 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/・・・
  26. 26. 25 Copyright 2015 FUJITSU 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 SystemIDS 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
  27. 27. 26 Copyright 2015 FUJITSU Detecting Cyber Attacks: "Outlier clustering" 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 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 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 1 1 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 1 1 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 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 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 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 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 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 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 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 2 1 1 2 1 1 1 2 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 3 2 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 1 2 2 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 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 3 2 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 1 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 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 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 1 1 1 1 1 4 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 1 1 Distribution of original data* Previous method Our method 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 1 1 1 1 1 1 1 1 Evaluating low- frequency data sets as “outlier clusters” Simplifying and grouping frequently- appearing data Attackers’ IP address Portnumber *Actually n-dimension data  Detecting low-frequently attacks with “outlier clustering” technique. Machine Learning
  28. 28. 27 Copyright 2015 FUJITSU 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 extractionVisualization of “Outlier Clusters ”
  29. 29. 28 Copyright 2015 FUJITSU Machine Learning for Massive Datasets Machine Learning  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- nationsTune sampling rate modelSampling 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 Accuracy Current Best Demonstrated at L19
  30. 30. 29 Copyright 2015 FUJITSU Machine Learning for Massive Datasets Machine Learning (Use Case) Optimization Energy Management Weather Power Consumption the correlation patterns automatically learns andData 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
  31. 31. 30 Copyright 2015 FUJITSU 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
  32. 32. 31 Copyright 2015 FUJITSU Gaze Tracking and Analysis Affective Media Processing Technologies 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 ●●●●●●● ●●●● SensorSensor Sensor Zoom in to track from several meters away Info about item seen by customer Use gaze data to analyze customer behavior Store DisplaySensor 1. Face parts detection 2. Eye region analysis & gaze calculation Pupil Corneal Reflection Demo
  33. 33. 32 Copyright 2015 FUJITSU
  34. 34. 33 Copyright 2015 FUJITSU Gaze Tracking and Analysis Affective Media Processing Technologies 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 ●●●●●●● ●●●● SensorSensor Sensor Zoom in to track from several meters away Info about item seen by customer Use gaze data to analyze customer behavior Store DisplaySensor 1. Face parts detection 2. Eye region analysis & gaze calculation Pupil Corneal Reflection Demo
  35. 35. 34 Copyright 2015 FUJITSU 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
  36. 36. 35 Copyright 2015 FUJITSU
  37. 37. 36 Copyright 2015 FUJITSU 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
  38. 38. 37 Copyright 2015 FUJITSU Affective Media Processing Technologies 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.
  39. 39. 38 Copyright 2015 FUJITSU 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)
  40. 40. 39 Copyright 2015 FUJITSU 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
  41. 41. 40 Copyright 2015 FUJITSU LOD4ALL:LOD Utilization PF Knowledge Processing (Use Case)  Renewal on July, 27, 2015: http://lod4all.net/
  42. 42. 41 Copyright 2015 FUJITSU 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> Knowledge Processing (Use Case)
  43. 43. 42 Copyright 2015 FUJITSU 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
  44. 44. 43 Copyright 2015 FUJITSU 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 Knowledge Processing (Use Case) FUJITSU Spain Angeles Delgado San Carlos Hospital Julio Mayol FUJITSU Laboratories Hideyuki Saso Demonstrated at M7
  45. 45. 44 Copyright 2015 FUJITSU "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 AnswerTodai robot QE inference technology automatic problem-solving Participation as the "math team" for project Math Todai robot project
  46. 46. 45 Copyright 2015 FUJITSU 「東ロボ」技術詳細説明10 Inference by computer algebra Answerr = 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
  47. 47. 46 Copyright 2015 FUJITSU
  48. 48. 47 Copyright 2015 FUJITSU 「東ロボ」技術詳細説明10 Inference by computer algebra Answerr = 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
  49. 49. 48 Copyright 2015 FUJITSU *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
  50. 50. 49 Copyright 2015 FUJITSU Tsunami Inundation Prediction Mathematical Technology (case example) 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
  51. 51. 50 Copyright 2015 FUJITSU
  52. 52. 51 Copyright 2015 FUJITSU Tsunami Inundation Prediction Mathematical Technology (case example) 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
  53. 53. 52 Copyright 2015 FUJITSU 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 Mathematical Technology (case example)
  54. 54. 53 Copyright 2015 FUJITSU 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)
  55. 55. 54 Copyright 2015 FUJITSU 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 SimulationNeuroscience Advanced research Machine learning Reinforcement learningDeep LearningLearning
  56. 56. 55 Copyright 2015 FUJITSU

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