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Combine hololens with Machine Learning Matteo Valoriani, Antimo Musone - FifthIngenium


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Tech 6. May 19th 2018. Data Driven Innovation 2018. Engineering Department, University of Roma Tre

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Combine hololens with Machine Learning Matteo Valoriani, Antimo Musone - FifthIngenium

  1. 1. COMBINE HOLOLENS WITH MACHINE LEARNING Matteo Valoriani Antimo Musone
  2. 2. Nice to Meet You Antimo Musone Co-founder of FifthIngenium @AntimoMusone Slideshare: Linkedin: Blog:
  3. 3. Nice to Meet You Matteo Valoriani, PhD CEO of FifthIngenium mvaloriani at @MatteoValoriani Slideshare: Linkedin: Blog: GitHub:
  4. 4. Agenda Computing evolution Industry (0.)4.0? Mixed Reality Maintenance Process Holographic Maintenance Process Machine Learning / Advanced Analytics Predictive Maintenance (cloud) AI Supported Maintenance (At the Edge) Conclusion
  5. 5. Industry (0.)4.0 ?
  6. 6. 1969, Apollo Guidance Computer 2000 Transistor 4k Memory
  7. 7. 2017, 4.5B Transistorn per CPU (mobile) 19B Transistor per CPU (server) 21B Transistor per GPU 50B Transistor per FPGA
  8. 8. 230 → 4.400 → 32.000 → 90.000 (1996) (2003) (2008) (2016)
  9. 9. RECAP
  10. 10. b 𝑑 = 𝑥𝑙 − 𝑥 𝑟 𝑏+𝑥 𝑙 − 𝑥 𝑟 𝑍−𝑓 = 𝑏 𝑍 Z = 𝑏∗𝑓 𝑑 Depth Sensing (Multi camera)
  11. 11. Visual Inertial Odometry (VIO)
  12. 12. HoloLens
  13. 13. What is HoloLens? HoloLens is the first, fully wireless holographic computer that redefine personal computing and empowers people in new ways.
  14. 14. • .
  15. 15. Mantainance
  16. 16. Traditional mantainance process
  17. 17. Holografic Mantainance Support DEMO
  18. 18. Machine Learning / Advanced Analytics Vision Analytics Recommenda-tion engines Advertising analysis Weather forecasting for business planning Social network analysis Legal discovery and document archiving Pricing analysis Fraud detection Churn analysis Equipment monitoring Location-based tracking and services Personalized Insurance Machine learning & predictive analytics are core capabilities that are needed throughout your business
  19. 19. Machine Learning Overview Formal definition: “The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience” - Tom M. Mitchell Another definition: “The goal of machine learning is to program computers to use example data or past experience to solve a given problem.” – Introduction to Machine Learning, 2nd Edition, MIT Press ML often involves two primary techniques: ▪ Supervised Learning: Finding the mapping between inputs and outputs using correct values to “train” a model; ▪ Unsupervised Learning: Finding patterns in the input data (similar to Density Estimates in Statistics).
  20. 20. Machine Learning / Advanced Analytics (ML & AI) techniques enable multiple use cases Fraud detection, credit risk/scoring, customer behaviour (e.g., churn), insurance underwriting AML transaction monitoring, anomaly detection in cyber security Algorithmic trading Algorithmic trading, unstructured data processing (see below) Fraud and market abuse investigation Sales & marketing, risk analysis, scheduling, resource allocation Automation of paper-based processes (e.g., claims processing, accounts payable, trade finance) Authentication, insurance claims analysis Telephone surveillance (insider trading, etc.), voice sentiment analysis Email surveillance, Machine Translation, help systems/chatbots, report writing, contract review Supervised Learning Unsupervised Learning Reinforcement Learning Deep Learning Social Network Analytics Visualisation & Reporting Optical Character Recognition Image Voice Natural Language Processing Predictive Analytics Descriptive & Prescriptive Analytics Unstructured Data Processing Machine Learning & Advanced Analytics ExampleusecasesinFinancialServices
  21. 21. AI Platform Stack Linux ( CentOS, Ubuntu, RedHat, SUSEDebian), Android, Windows, BSD, iOS, MacOS x86, ARM, CUDA, Mali, Adreno CUDA, MPI, OpenMP, TBB, OpenCL, StarPU Languages AI Platforms Parallel programming Basic libraries Compilers Operating System Hardware Microsoft Azure, Google Cloud, Amazon, Watson etc. Tensorflow, Caffe, Torch, Theano, TensorRT, CNTK, OpenCV LLVM,GCC, ICC, Rose, PGI, Lift … cuBLAS, BLAS, MAGMA, ViennaCL, CLBlast, cuDNN, openBLAS, clBLAS, libDNN, tinyDNN, ARM compute lib etc C++, Fortran, Java, Python, Byte code, Assembler
  22. 22. Microsoft Azure ML Platform Azure ML a production environment that simplifies the development and deployment of machine learning models. The platform enables growing community of developers and data scientists to share their analytics solutions. Capabilities : ▪ Machine Learining Service ▪ Bot Service ▪ Bing Web Search API ▪ Text Analytics API ▪ Face API ▪ Large scale machine learning service ▪ Computer Vision API ▪ Custom Vision Service
  23. 23. Predictive Maintenance (cloud)
  24. 24. AI Supported Maintenance (At the Edge)
  25. 25. And more…
  26. 26. Developer Resources Preview Pricing Documentation Client SDKs Example Code Join Our Community Thank you! How Augment your Reality Matteo Valoriani
  27. 27. samsung references