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Exploring the development and exploitation of cutting edge AI technology

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There have been significant technological breakthroughs in artificial intelligence (AI) recently, with the use of AI expanding rapidly in various fields. Fujitsu Laboratories has been developing AI technologies for more than 30 years, extending from basic technology development through to practical deployments. In this session, we will share the latest Fujitsu initiatives around cutting edge AI technology development and examples of practical implementations in society.

Publié dans : Technologie
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Exploring the development and exploitation of cutting edge AI technology

  1. 1. 0 © Copyright 2017 FUJITSU Fujitsu Forum 2017 #FujitsuForum
  2. 2. 1 © Copyright 2017 FUJITSU Exploring the development and exploitation of cutting-edge AI technology Shoji Suzuki Member of the Board Head of Artificial Intelligence Laboratory Fujitsu Laboratories Ltd.
  3. 3. 2 © Copyright 2017 FUJITSU Agenda 1. Current status of AI and Fujitsu Lab. research strategy 2. Implementation in society of AI technologies 3. Cutting-edge AI technology 「Explainable AI」
  4. 4. 3 © Copyright 2017 FUJITSU Current status of AI and Fujitsu Lab. research strategy 1.
  5. 5. 4 © Copyright 2017 FUJITSU History of Artificial Intelligence 1st AI Boom(Search/Inference) 2nd AI Boom(Knowledge) 3rd AI Boom(Machine Learning) (1956-1960s) (1980s) 1st AI Winter(‘74-’80) 2nd AI Winter(‘87-’11) ■The birth of AI【‘56】 (Dartmouth Conference) ■The rush of big AI projects (Expert System) ■Games with clear goals can be solved but not practical Rule Base Lisp Prolog (2012-) ■Far from the knowledge level of human experts ■Deep learning surpassed conventional methods in image recognition【‘12】
  6. 6. 5 © Copyright 2017 FUJITSU Background of the 3rd AI Boom Evolution of computing Data driven Breakthrough algorithm The latest GPU: 120TFLOPS ・・・ Realizes 1000-smartphone computing by one chip ■100 GB/1 person Sequence data of whole human genome ■220 GB/1 minute Videos uploaded to Youtube around the world ■2 TB/1 driving Data generated by various sensors in an automated vehicle ■200 TB/1 fright Data generated by various sensors in an airplane flight Data is the new oil of the 21st century Brilliantly overcame the prejudice that useless Gains visual and speech recognition functions that can exceed human for the first time with deep learning 201020001990 Worderrorrate 100% 5% 10% Using DL Source : Microsoft Evolution of speech recognition
  7. 7. 6 © Copyright 2017 FUJITSU Expected AI Related Market  AI related market reaches 59.8 billion dollars worldwide in 2025  30% for enterprises, 70% for consumers CAGR : 50% or more EnterprisesConsumers , partially edited by Fujitsu Labs.
  8. 8. 7 © Copyright 2017 FUJITSU Solving Practical Social Issues Through AI  Current AI applications are still in the element technology level  Needs to realize the AI effect and growth through social implementations Current Applications Games Speech recognition / translation Image recognition Applications required by real world Mobility System Healthcare System HR System Social Infrastructure System Financial System
  9. 9. 8 © Copyright 2017 FUJITSU Strategy to Grow AI Market ① Clearly shows the effect of AI in enterprise market ② Enhance AI market by understanding AI’s decision Shows the effect of AI Understands AI’s decision Collects, classifies and organizes events Understands events and predicts future Executes by human or AI and feedback its results Makes decisions and plans actions Growing Cycle of AI Visualization Execution & Feedback Analysis & Prediction Decision Making By repeating this cycle, AI’s performance is demonstrated AI collaborates smoothly with people and coexists with society
  10. 10. 9 © Copyright 2017 FUJITSU Implementation in society of AI technologies 2.
  11. 11. 10 © Copyright 2017 FUJITSU Fujitsu AI Technology Brand Agile & intense
  12. 12. 11 © Copyright 2017 FUJITSU Fujitsu’s AI Goals To create AI that collaborates with people and is human centric To create AI that continuously evolves To provide AI that can be incorporated in our products and services then deployed
  13. 13. 12 © Copyright 2017 FUJITSU AI Development Overview AI platform AI core technologies Implementation in society Applications Software Acceleration of AI programs as data preprocessing, parallel processing distributed processing and so on Sensing and Recognition Understanding of user's intention, emotion, environment and situation Knowledge Processing Acquisition of knowledge, and inference and problem solving Learning Advancement of machine learning in order to expand application range AGI Artificial General Intelligence Service and Embedded Incorporating AI into equipment. Development & verification for applying social infrastructure systems. Governance and Social acceptability Privacy and security protection of intellectual property rights. Social acceptability. Eco-system Ecosystem for business, technology and human resource development. Hardware Hardware for high-speed AI computing such as HPC, DLU and DAU Social infra- structure Mobility Customer centerLogistics Main- tenance Manu- facturing Fintech Digital marketing Food / agriculture Health care Office / living
  14. 14. 13 © Copyright 2017 FUJITSU AI Development Overview AI platform AI core technologies Implementation in society Applications Software Acceleration of AI programs as data preprocessing, parallel processing distributed processing and so on Sensing and Recognition Understanding of user's intention, emotion, environment and situation Knowledge Processing Acquisition of knowledge, and inference and problem solving Learning Advancement of machine learning in order to expand application range AGI Artificial General Intelligence Service and Embedded Incorporating AI into equipment. Development & verification for applying social infrastructure systems. Governance and Social acceptability Privacy and security protection of intellectual property rights. Social acceptability. Eco-system Ecosystem for business, technology and human resource development. Hardware Hardware for high-speed AI computing such as HPC, DLU and DAU Social infra- structure Mobility Customer centerLogistics Main- tenance Manu- facturing Fintech Digital marketing Food / agriculture Health care Office / living Explainable AI Applications to solve social problems in various fields
  15. 15. 14 © Copyright 2017 FUJITSU Extending the Scope of 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 dataImage 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 Learning Technology
  16. 16. 15 © Copyright 2017 FUJITSU 1 Blade Inspection Image Data Purpose Technology Outcome Develop “Non-destructive testing” to assist human operators in identifying potential defects Detection with a combination of Deep Learning, Image and Signal processing  Blades can now be checked 75% faster, taking just 1.5 hours  To be put in production in Denmark and UK starting Autumn 2017 demonstration
  17. 17. 16 © Copyright 2017 FUJITSU 2 Bridge Inspection Automatic detection of internal damage for bridges Topological data analysis (TDA) for time-series data from sensor Contribution to the optimization of bridge maintenance and management tasks Vibration dataExternal sensor Converting vibration data into figures Converting figures into numerical values degree of abnormality The degree of abnormality and the degree of change degree of change TDA Chaos theory DL intact damaged wheel load concrete slab external sensor Purpose Technology Outcome Time-series Data demonstration
  18. 18. 17 © Copyright 2017 FUJITSU Deep Tensor FUJITSU’s proprietary deep learning technology that can learn graph data representing relationships in the real world Various relationships SNS (personal relations) Compound (element combination) Business connections Existing error back propagation method Extended error back propagation method Tensor representation Graph data Deep Tensor Business growth forecast Fintech Virtual screening IT drug development Malware invasion detection Cyber attack … Neural network Learning both neural network and tensor transformation Communicati on relations
  19. 19. 18 © Copyright 2017 FUJITSU 3 Security(Malware Attack Detection)  Shorten the detection time, several days by experts to a few minutes  Detect the behavior of a malware, which is difficult for the expert to find out the example of graph data that only the new method using Deep Tensor can detect the attack The number of nodes: 223 The number of nodes: 249 The number of nodes: 343 通信パケット長 起動コマンド種類,属性 To shorten the malware attack detection time  Detection of several patterns of attacks by multiple tensor technology  Conversion of various types log data to graph data and multi tensor form DeepTensor Learn the multiple patterns of attacks Network log SNS Cheical compound Trading histories Graph Data Tensor form + Tensor form + Tensor form ・ ・ MultpleTensorTech Deep Learning Multi Tensor ConvertGraphData Graph Data demonstration Source IP address Destination IP address Source port No. Destination port No. Packet length Command attribute Purpose Technology Outcome
  20. 20. 19 © Copyright 2017 FUJITSU Modeling, Inference, Optimization, Matching ・・・ Knowledge Processing and Decision & Support Modeling and Inference Modeling and Optimization Matching and Modeling Heat Stress Level Estimation Ship Fuel Reduction Daycare Center Matching ①Accumulated heat stress status ②Heat stress level decision model Specialists’ knowledge Work/environmental states data Heat stress level Machine Learning Heat stress accumulation status decision algorithm Ship operational data Voyage data Engine log climate Performance model at sea Learning a statistical model cargo weight wind direction wave direction wave height wind speed Ship Performance current speed current direction aging ship design High-dimensional statistical analysis Accurate Mathematical Optimization Prediction Optimization Fuel Efficient Routing Maintenance Timing Day care centers & Applicant matching under applicants’ requirements Fair & efficient Mathematical model of the relationships of complex requirements Game theoretical Algorithm best matching Risk
  21. 21. 20 © Copyright 2017 FUJITSU 4 Heat Stress Level Estimation Safety management of worker's heat stress level Learning based on the specialist's judgment using ambient temperature, humidity, the pulse rate and the momentum  Realize the safety management of workers under hot weather.  The accuracy of heat stress risk level detection is more than 94%.  Started the business from this summer The heat stress level is estimated and the alarm is notified. Pulse rate in working Pulse rate in a rest Body heat environmental index Vital sign sensing band Specialists’ findings International standard and index related to heat stress Heat stress accumulation level dicision algorithm ①Accumulated heat stress Momentum ①Accumulated heat stress based on work and environmental states ②Presumption of heat stress level based on specialists’ findings ②dicision model Heat stress level Risk is high Risk is middle Risk is low Safety Modeling and Inference Purpose Technology Outcome
  22. 22. 21 © Copyright 2017 FUJITSU 5 Ship Fuel Reduction Solve the big issue of ship fuel consumption cost reaches 3 billion dollars per major shipping company  Generate statistical model with every factor learnt from operational data  Visualize actual performance at see and select fuel efficient routing for each ship Fuel consumption reduction is about 5% and estimation error is within 2% Time [sec.] Shipspeed[knot] EstimatedMeasured Estimating ship performance accurately Generating statistical model Collecting operational data Selecting fuel efficient routing for each ship cargo weight wind direction wave direction wave height wind speed Ship performance current speed current direction aging ship design High-dimensional statisticalanalysis Maintenance Timing Optimization Performance degradation estimation Fuel efficiency Optimum maintenance timing age ※ VDR=Voyage Data Recorder demonstration VDR Data※ Engine Log data Modeling and Optimization Purpose Technology Outcome
  23. 23. 22 © Copyright 2017 FUJITSU Fair and fast matching under applicants’ complex requirements Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling  Time for admission screening for about 8,000 children is reduced from several days to seconds  To be put in production as an optional service for “MICJET MISALIO Child-Rearing Support”, during fiscal 2017 6 Daycare Center Matching Matching and Modeling Capacity:2 Capacity:2 Capacity:2 1 4 2 5 6 3 DaycareApplicant Child 1 Priority 1 Child 2 Priority 2 Child 3 Priority 3 Child 4 Priority 4 Meets the Rule? Assignment 1 Daycare A Daycare A Daycare B Daycare B ✗ Assignment 2 Daycare A Daycare B Daycare A Daycare B ✗ Assignment 3 Daycare A Daycare B Daycare B Daycare A ✓ Assignment 4 Daycare B Daycare A Daycare A Daycare B ✓ Assignment 5 Daycare B Daycare A Daycare B Daycare A ✗ Assignment 6 Daycare B Daycare B Daycare A Daycare A ✗ Sibling Sibling Purpose Technology Outcome
  24. 24. 23 © Copyright 2017 FUJITSU Other Applications Chatbot for call center Voice automatic translation Business support Labor-saving in call centers by 24/7 service by auto-response Supporting of communication with foreigners Automatic classification AI Business instruction Meeting offer Claim Just information Automatic detection of intention of mail sender and summary of contents Cloud Audio signal Microphone Speaker Model enhance Automatic model adjustment according to noise environment Edge Technology for structural semantics extraction by relations of words and high accuracy FAQ search Learning Voice recognition Translation Voice synthesis user ・・・・・・・ ・・・・・・・ ・・・・・・・ Automatic Dialog through messenger 24/7 Service Digital Agent for Call Center Natural Dialog Knowhow Chatbot FAQ Search Zinrai Platform Agents Supporting office workers for troublesome work such as schedule input
  25. 25. 24 © Copyright 2017 FUJITSU Cutting-edge AI technology 「Explainable AI」 3.
  26. 26. 25 © Copyright 2017 FUJITSU Outline  Why Explainable AI ?  “Black box” issue must be solved in order to expand social applications of AI.  Key technologies to realize Explainable AI  Deep Tensor and knowledge graphs  Technological points  Explaining “reasons” and “basis” for conclusions are identified  Specific example  Genomic medicine field  Future works New technology “Explainable AI” Development of brand new AI that can explain the reasons and basis for AI-generated conclusions
  27. 27. 26 © Copyright 2017 FUJITSU Why Explainable AI ?  Deep learning is a breakthrough technology for AI.  To enhance the field which utilize deep learning, we solved it’s “black box problem” Explainable AI 1.Rely, 2.Understand 3.Control Can make new findings Can fulfill accountability Can improve AI Human 1 2 3 Empower
  28. 28. 27 © Copyright 2017 FUJITSU Key Technologies to Realize Explainable AI Deep learning technology that can learn graph data Deep Tensor Graph data representing relationships in the real world Knowledge Graph LOD Web Tensor representation (standardized expression) Graph data Neural network Existing error back propagation method Extended error back propagation method Malware detection Cyber attack Learning both neural network and tensor transformation Virtual screening IT drug development Gene Mutation PTPN11 Compound Gene Systematic knowledge (utilized for a role to play) Onset Disease Part of Protein Expression Drug class Target Effec t Disease LEOPARD syndrome Activatio n Part of Mutation NM_002834.4:c.174C>G Public DBArticles
  29. 29. 28 © Copyright 2017 FUJITSU Technological Points Inference factorInput Output Basis formation Output both findings and reasons (inference factors) Findings Knowledge Graph Inference factor identification Knowledge graph forms basis from input to findings 1. Explaining the reasons for findings Deep Tensor 2. Explaining the basis for findings b da c e f g Deep Tensor + knowledge graph -> Reasons and basis of AI judgments
  30. 30. 29 © Copyright 2017 FUJITSU  By understanding the cause of the disease, largely reduce the time for analysis, diagnosis, treatment orientation by doctors Application to Genomic Medicine A diagnosis (drawing blood) Gene analysis (next-generation sequencer) The presentation of the therapeutic drug to personality (genetic abnormality) Analyzing and reporting Ref. Hokkaido Univ. Hospital(http://www.huhp.hokudai.ac.jp/hotnews/detail/00001144.html) Identification of the cause gene, medicine, investigational search, judgment of the treatment Web Medical articles Bio DB … Learning from 180,000 cases of disease mutation data Deep Tensor Inference of disease PubMed Medical articles 17million Bio DB 3million records b da c e f More than 10 billion knowledge built from 17 million medical articles, etc. Causative gene Gene Ontology Disease Ontology … Forming medically-proven basis from mutation to disease Gene mutation Gene mutation demonstration
  31. 31. 30 © Copyright 2017 FUJITSU Example of Basis Paths  Composing a path to be the basis from input “mutation” to output “disease” Input node (gene mutation) Output node (disease) Inference factor of Deep Tensor (partial graph having impact on output) Node complemented by knowledge graph Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia).
  32. 32. 31 © Copyright 2017 FUJITSU Future Works Aim for collaboration of humans and AI in various fields Utilization of national projects and industry-academia collaboration Health care Co-creation with financial institutions Finance Implementation within a company Corporate Inferring disease from gene mutation Providing medically reasoned explanations Forecasting corporate growth from business performance and economic indicators Explaining strengths and weaknesses of companies Detecting employees’ health change from their activity data Explaining factors for health maintenance in work environment
  33. 33. 32 © Copyright 2017 FUJITSU A safer, more prosperous and sustainable world Human Centric Intelligent Society
  34. 34. 33 © Copyright 2017 FUJITSU Fujitsu Sans Light – abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789 ¬!”£$%^&*()_+-=[]{};’#:@~,./<>?| ©¨~¡¢¤¥¦§¨ª«»¬- ®¯°±²³µ¶·¸¹º¼½¾¿ÀÁÂÃÄÅÇÈÆÉÊËÌÍÎÏÐÑÒÓÔÕÖ×ØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõö÷øùúûüýþ ÿĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝·-‒–—―‘’‚“”„†‡•…‰‹›‾⁄⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉€™Ω→∂∆∏∑−√∞∫≈≠≤≥⋅■◊fifl Fujitsu Sans – abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789 ¬!”£$%^&*()_+-=[]{};’#:@~,./<>?| ©¨~¡¢¤¥¦§¨ª«»¬- ®¯°±²³µ¶·¸¹º¼½¾¿ÀÁÂÃÄÅÇÈÆÉÊËÌÍÎÏÐÑÒÓÔÕÖ×ØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõö÷øùúûüý þÿĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝·-‒–—―‘’‚“”„†‡•…‰‹›‾⁄⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉€™Ω→∂∆∏∑−√∞∫≈≠≤≥⋅■◊fifl Fujitsu Sans Medium – abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789 ¬!”£$%^&*()_+-=[]{};’#:@~,./<>?| ©¨~¡¢¤¥¦§¨ª«»¬- ®¯°±²³µ¶·¸¹º¼½¾¿ÀÁÂÃÄÅÇÈÆÉÊËÌÍÎÏÐÑÒÓÔÕÖ×ØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõö÷øùúû üýþÿĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝·-‒–—―‘’‚“”„†‡•…‰‹›‾⁄⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉€™Ω→∂∆∏∑−√∞∫≈≠≤≥⋅■◊fifl

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