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Taking advantageofai july2018

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Presentation of the NATF report on Artificial Intelligence and Machine Learning.

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Taking advantageofai july2018

  1. 1. Artificial Intelligence and Machine Learning – July 2018 1/14 Yves Caseau National Academy of Technologies Michelin CIO Taking Advantage of AI July 11th , 2018 V0.4
  2. 2. Artificial Intelligence and Machine Learning – July 2018 2/14 OutlineOutline Artificial Intelligence and Machine Learning « revolution » A glance at the « toolbox »: methods, protocols and assembly A « how to guide » for corporation How to grow « emergence » ? 1:AI“renewal”1:AI“renewal”2:The« Toolbox »2:The« Toolbox »3:Calltoaction3:Calltoaction
  3. 3. Artificial Intelligence and Machine Learning – July 2018 3/14 AI « renewal » /AI « renewal » / Technology academy workgroupTechnology academy workgroup Spectacular Investment Acceleration  Major players and venture capital  Belief that major benefits are yet to come Spectacular Performance Acceleration  Image, speech recognition, translation, ….  Alpha Go, etc.   Moore’s law does not explain everything Workgroup questions  Revolution or evolution ?  AI Algorithms = commodity ?  « Exponential Organization » ? 1:AI“renewal”1:AI“renewal”
  4. 4. Artificial Intelligence and Machine Learning – July 2018 4/14 Taking advantage of AI availabilityTaking advantage of AI availability Vaccine Manufacturing at Merck  5 Terabytes in a “Datalake”  Batch yield optimization IHG Continental (hotels)  ultra-fine customer segmentation client  Similar approach at Amadeus Ejection fraction analysis (cardiology)  Contest prepared by doctors and cardiologists  DNN to compute a volume through image analysis FAA  Long term delay forecast through a Bayesian network  Taking “avalanche effect” into accounts  5 years of data, 52 millions flights – noisy data 1:AI“renewal”1:AI“renewal”
  5. 5. Artificial Intelligence and Machine Learning – July 2018 5/14 1:AI“renewal”1:AI“renewal” Most AI application are built on top of a feedback loopMost AI application are built on top of a feedback loop Iterative Developement of AI Practice Speed of learning depends on computing power Smart Algorithms Smart Engineering Smart Services Service Usage Growing Large Datasets Distributed Software Engineering Practices Distributed Software Engineering Practices Management Vision & Grit Management Vision & Grit Ease to collectEase to collect Trust & Acceptability Trust & Acceptability A Scientists Open source Startups B- Lack of SW medium sized players C+ Risk-adverse Lack of SW culture B+ Market Size / language B- GDPR CNIL B+ Competitive access to GPU/TPU
  6. 6. Artificial Intelligence and Machine Learning – July 2018 6/14 Today’s Artificial Intelligence (& ML) makes an extended toolboxToday’s Artificial Intelligence (& ML) makes an extended toolbox Open question Question précise Few data Lots of data classical « Data Science » methods Rules OR / NLP Agents Evolutionary Game Theory Deep Learning (CNN) Semantics (e.g., Watson) • Rule-based and constraint- based (e.g., configuration) • Fuzzy boundary with operations research • Most companies “AI use cases” • Einstein / TellMePlus / Da Vinci Labs • Moore’s Law & Big Data • Key role of simulation • Well suited to complex systems • Continuous but slow progress • News articles written by robots • Pattern/situation recognition • This decade’s inflexion point Intelligence Artificielle et Apprentissage Automatique –May 2018 8/16 Quelques éléments clés de la boîte-à-outils l Regression linéaire / logistique l Réseau Bayésien l Régularisation l (K-mean) clustering l Random Forest l Gradient Boosting l Support-Vector Machines l Réseaux Neuronaux l Ontologies l Lexicographie l ARMA, ARIMA, etc. 2:La“boïte-à-outils 2:The« Toolbox »2:The« Toolbox »
  7. 7. Artificial Intelligence and Machine Learning – July 2018 7/14 Some key pieces in the toolboxSome key pieces in the toolbox l Linear / logistic regression l Bayesian networks l Regularization l (K-mean) clustering l Random Forest l Gradient Boosting l Support-Vector Machines l Neural Networks l Ontologies l Lexicographic tools l Rule-bases scripting l ARMA, ARIMA, etc. 2:The« Toolbox »2:The« Toolbox »
  8. 8. Artificial Intelligence and Machine Learning – July 2018 8/14 Meta-Heuristics to mix these componentsMeta-Heuristics to mix these components l Reinforcement learning l Transfer learning l Natural language processing toolbox l Large-scale Intelligent agents communities l Game theory to reason about competition and cooperation • Hybrid AI: to combine different tools and meta-heuristics • Generative approaches 2:The« Toolbox »2:The« Toolbox »
  9. 9. Artificial Intelligence and Machine Learning – July 2018 9/14 Cognitive Systems: Mixing various AI andCognitive Systems: Mixing various AI and Machine Learning TechniquesMachine Learning Techniques  Smart System Components:  Perception / environment  Self-consciousness of goals  Forecast and adjust  Growth through usage  Biomimicry  Develop through reinforcement  Add layered capabilities for resilience  Cognitive computing  “reason from a purpose” – IBM  “systems grow by machine learning, not by programmatic design” EDA Objects & sensors user CEP Reflexes ACT command center state react history THINK Decisions (AI) PLAN Execution Logic goals REFLECT Evolutionary ML ANALYZE Machine Learning ADAPT Reactive LEARN Representation VALUATION emotions services Other systems Systems of Systems FORECAST Anticipation decide insights 2:The« Toolbox »2:The« Toolbox »
  10. 10. Artificial Intelligence and Machine Learning – July 2018 10/14 AI strategy starts with data collectionAI strategy starts with data collection Data collection process Do not forget meta-data ! Build qualified training sets “System thinking” (loop) : collect tomorrow’s data as well as past data Prepare « machine vision » revolution (& perception) through collecting images and video … as well as customer digital traces. 3:Calltoaction3:Calltoaction Data Lots of them, tagged Algorithms most often, open-source Integration & meta- heuristics Training Protocols Time & resourcesSkills / experience
  11. 11. Artificial Intelligence and Machine Learning – July 2018 11/14 Grow the success conditions for your teamsGrow the success conditions for your teams Leverage the « technology wave » Beware of « false positives »  Overfitting, Spurious correlations, .. … and of biases in training data Mindset: distributed and emergent innovationMindset: distributed and emergent innovation Data collection/ training setsData collection/ training sets AI-friendly software environmentsAI-friendly software environments Lab Culture (Data Science)Lab Culture (Data Science) PerseverancePerseverance Constant flow of software It takes time to build skills 3:Calltoaction3:Calltoaction
  12. 12. Artificial Intelligence and Machine Learning – July 2018 12/14 Time to act is nowTime to act is now  Start right now with tools that are easily available  Simple methods work  Take advantage of « integrated/Automated » toolboxes  Einstein, Holmes, TellMePlus, etc. Secure access to large-scale computing power to increase the speed of learning (GPU & TPU) 3:Calltoaction3:Calltoaction Research & Development Digital Manufacturing Deliver Product Supply Chain Assist Customer Pattern detectionPattern detection Customer Interaction (e.g. Chatbots / Smart Assistants)Customer Interaction (e.g. Chatbots / Smart Assistants) Operations Support / Information Systems Digital Traces - IOT Operations Support / Information Systems Digital Traces - IOT FraudFraud Predictive maintenance Quality Assurance Automation Forecast / Optimization Robotic Process AutomationRobotic Process Automation Knowledge EngineeringKnowledge Engineering SearchSearch
  13. 13. Artificial Intelligence and Machine Learning – July 2018 13/14 To develop one’s situation potential (emergence)To develop one’s situation potential (emergence) Artificial Intelligence is not a service that you buy, it is a practical skill that one must grow.  It takes time … Learning curve  To develop the kind of AI that is suited to one’s business  To work within a small team with outside experts (e.g., from academia)  To organize contests with business training sets  To build a continuous improvement process Think Platform  Large scope vision (upstream & downstream value chain)  « Win/win » : learn to share data  Example: Today’s “stupid” chatbots collect data that will be used to train tomorrow’s smart assistants 3:Calltoaction3:Calltoaction
  14. 14. Artificial Intelligence and Machine Learning – July 2018 14/14 Main take-awayMain take-away These are the five domains that anyone should start investigating without delays: 1.Smart Automation: RPA scripting tools, Rule engines 2.Natural Language Processing: Bots & ontologies Sentiment analysis API 3.Pattern recognition : Random Forests, Neural Nets 4.Forecasting : Machine Learning Toolboxes / Prediction API / Bayesian Networks 5.Machine Vision : play with CNN (TensorFlow) ConclusionConclusion

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