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  • Artificial Intelligence. What machines can learn and how to implement Machine Learning

    project in you organization?

    Antons Mislēvičs

    antonsm@microsoft.com

    antons.mislevics@hotmail.com

  • 1. Over 9 years @ Microsoft  Over 12 years of SharePoint experience

    2. Projects:  Portals, ECM, Social, Search, BPMS – Architect, Lead Developer

     Architecture Design Sessions – Subject Matter Expert on SharePoint

     Cloud Solutions: Office 365, SharePoint Online, Windows Azure, SharePoint / Office 365 Dev Patterns & Practices (PnP) Core Team member

    3. Countries:  Latvia, Lithuania, Estonia, Ukraine, Kazakhstan, Malta, Cyprus, Switzerland,

    UK, Germany, France, Russia, Brunei, Azerbaijan, Georgia, Sweden, USA

    4. PhD in Engineering of Computer Systems:  Thesis: “Mobile Agents for Business Process Management Support in Cloud

    Computing Environments”

    5. Lead Researcher at Riga Technical University  Department of Artificial Intelligence and Systems Engineering

    Antons Mislēvičs

    Senior Consultant

    Microsoft Services EMEA

    antonsm@microsoft.com

  • Agenda

    1. What machines can do today?

    2. How Machine Learning works?

    3. How to implement Machine Learning in your project?

    4. How the future looks like?

  • What machines can do today?

  • Chess: IBM Deep Blue 3 ½ - Gary Kasparov 2 ½ (1997)

    Kasparov vs Deep Blue the rematch, 1997

    https://www.research.ibm.com/deepblue/

    http://www.sciencemag.org/news/2016/03/update-why-week-s-man-versus-machine-go-match-doesn-t-matter-and-what-does

  • Jeopardy: IBM Watson beats champions (2011)

    Final Jeopardy! and the Future of Watson

    http://www-03.ibm.com/marketing/br/watson/what-is-watson/the-future-of-watson.html

    http://www-03.ibm.com/marketing/br/watson/what-is-watson/the-future-of-watson.html

  • Go: Google AlphaGo 4 – Lee Sedol 1 (2016)

    AlphaGo

    https://deepmind.com/alpha-go

    https://deepmind.com/alpha-go

  • Poker: Libratus and DeepStack beat top pros (2017)

    Carnegie Mellon Artificial Intelligence Beats Top Poker Pros: https://www.cmu.edu/news/stories/archives/2017/january/AI-beats-poker-pros.html

    Brains Vs. AI Rematch: Why Poker?: https://www.youtube.com/watch?v=JtyA2aUj4WI

    Tough poker player: Brains Vs. AI update: https://www.youtube.com/watch?v=CRiH8yCskAE

    Safe and Nested Endgame Solving for Imperfect-Information Games, N. Brown, T. Sandholm, 2017: http://www.cs.cmu.edu/~sandholm/safeAndNested.aaa17WS.pdf

    DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker, 2017: https://arxiv.org/abs/1701.01724

    https://www.cmu.edu/news/stories/archives/2017/january/AI-beats-poker-pros.html https://www.youtube.com/watch?v=JtyA2aUj4WI https://www.youtube.com/watch?v=CRiH8yCskAE http://www.cs.cmu.edu/~sandholm/safeAndNested.aaa17WS.pdf https://arxiv.org/abs/1701.01724

  • Image Recognition Error rates – human vs machine

    1. Traffic Sign Recognition (IJCNN 2011):

    – Human: 1.16%

    – Machine: 0.54%

    2. Handwritten Digits (MNIST):

    – Human: approx. 0.2%

    – Machine: 0.23% (2012)

    The German Traffic Sign Recognition Benchmark: http://benchmark.ini.rub.de/?section=gtsrb&subsection=results

    THE MNIST DATABASE of handwritten digits: http://yann.lecun.com/exdb/mnist/

    http://benchmark.ini.rub.de/?section=gtsrb&subsection=results http://yann.lecun.com/exdb/mnist/

  • Large Scale Visual Recognition Challenge (ILSVRC)

    2015 challenge:

    – Object detection - 200 categories

    – Object recognition – 1000 categories

    – Object detection from video – 30 categories

    – Scene classification – 401 categories

    Large Scale Visual Recognition Challenge 2015 (ILSVRC2015)

    http://image-net.org/challenges/LSVRC/2015/index#maincomp

    http://image-net.org/challenges/LSVRC/2015/index#maincomp

  • Large Scale Visual Recognition Challenge 2015 – Results: http://image-net.org/challenges/LSVRC/2015/results

    Microsoft Researchers’ Algorithm Sets ImageNet Challenge Milestone, 2015: https://www.microsoft.com/en-us/research/microsoft-researchers-algorithm-sets-imagenet-challenge-milestone/

    Microsoft Research Team:

    “To our knowledge, our result is the first to surpass human-

    level performance…on this visual recognition challenge”

    http://image-net.org/challenges/LSVRC/2015/results https://www.microsoft.com/en-us/research/microsoft-researchers-algorithm-sets-imagenet-challenge-milestone/

  • Machines can understand the meaning…

    Show and Tell: A Neural Image Caption Generator, O. Vinyals, A. Toshev, S. Bengio, D. Erhan, 2015: http://arxiv.org/abs/1411.4555v2

    https://arxiv.org/abs/1411.4555v2

  • DenseCap: Fully Convolutional Localization Networks for Dense Captioning, J. Johnson, A. Karpathy, L. Fei-Fei, 2015: http://arxiv.org/abs/1511.07571

    http://image-net.org/challenges/LSVRC/2015/index#maincomp

  • Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models, R. Kiros, R. Salakhutdinov, R. S. Zemel, 2014: http://arxiv.org/abs/1411.2539

    https://arxiv.org/abs/1411.2539

  • Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models, R. Kiros, R. Salakhutdinov, R. S. Zemel, 2014: http://arxiv.org/abs/1411.2539

    https://arxiv.org/abs/1411.2539

  • https://clarifai.com/demo

    https://clarifai.com/demo

  • Self-Driving Cars

    Google Self-Driving Car Project - How it drives: https://www.google.com/selfdrivingcar/how/

    Autopilot Full Self-Driving Hardware (Neighborhood Long), Tesla Motors: https://vimeo.com/192179727

    https://www.google.com/selfdrivingcar/how/ https://vimeo.com/192179727

  • 2. The Next Rembrandt [4]

    1. A Neural Algorithm of Artistic Style, 2015: https://arxiv.org/abs/1508.06576

    2. Supercharging Style Transfer, 2016: https://research.googleblog.com/2016/10/supercharging-style-transfer.html

    3. Neural Doodle:, 2016: https://github.com/alexjc/neural-doodle

    4. The Next Rembrandt: https://www.nextrembrandt.com/

    5. Image Completion with Deep Learning in TensorFlow, 2016: https://bamos.github.io/2016/08/09/deep-completion/

    6. Neural Enhance, 2016: https://github.com/alexjc/neural-enhance

    Machines get creative… 1. Reproduce artistic style [1, 2, 3]

    4. Enhance images [6]

    3. Complete images [5]

    https://arxiv.org/abs/1508.06576 https://research.googleblog.com/2016/10/supercharging-style-transfer.html https://github.com/alexjc/neural-doodle https://www.nextrembrandt.com/ https://bamos.github.io/2016/08/09/deep-completion/ https://github.com/alexjc/neural-enhance

  • 1. WaveNet: A Generative Model for Raw Audio: https://deepmind.com/blog/wavenet-generative-model-raw-audio/

    2. WaveNet: A Generative Model for Raw Audio, 2016: https://arxiv.org/abs/1609.03499

    3. Historic Achievement: Microsoft researchers reach human parity in conversational speech recognition: https://blogs.microsoft.com/next/2016/10/18/historic-achievement-microsoft-researchers-reach-human-parity-conversational-speech-recognition/

    4. Achieving Human Parity in Conversational Speech Recognition, 2016: http://arxiv.org/abs/1610.05256

    Text to speech and voice recognition…

    2. Recognize voice [3, 4]

    1. Talk - text to speech (WaveNet) [1, 2]

    https://deepmind.com/blog/wavenet-generative-model-raw-audio/ https://arxiv.org/abs/1609.03499 https://blogs.microsoft.com/next/2016/10/18/historic-achievement-microsoft-researchers-reach-human-parity-conversational-speech-recognition/ http://arxiv.org/abs/1610.05256

  • 2. Generate handwriting [2, 3]

    3. Translate texts [4, 5]

    1. Composing Music With Recurrent Neural Networks: http://www.hexahedria.com/2015/08/03/composing-music-with-recurrent-neural-networks/

    2. Generating Sequences With Recurrent Neural Networks, A. Graves, 2014: http://arxiv.org/abs/1308.0850

    3. Alex Graves’s RNN handwriting generation demo: http://www.cs.toronto.edu/~graves/handwriting.html

    4. University of Montreal, Lisa Lab, Neural Machine Translation demo: http://lisa.iro.umontreal.ca/mt-demo

    5. Fully Character-Level Neural Machine Translation without Explicit Segmentation, J.Lee, K. Cho, T. Hofmann, 2016: http://arxiv.org/abs/1610.03017

    What else machines can do?

    1. Compose music [1]

    http://www.hexahedria.com/2015/08/03/composing-music-with-recurrent-neural-networks/ http://arxiv.org/abs/1308.0850 http://www.cs.toronto.edu/~graves/handwriting.html http://lisa.iro.umontreal.ca/mt-demo http://arxiv.org/abs/1610.03017

  • Machine Learning is transforming businesses…

    – Different industries…

    – Happens in Latvia as well…

  • How Machine Learning works?

  • Machine Learning process

    Introducing Azure Machine Learning, D. Chappell, 2015:

    http://www.davidchappell.com/writing/white_papers/Introducing-Azure-ML-v1.0--Chappell.pdf

    http://www.davidchappell.com/writing/white_papers/Introducing-Azure-ML-v1.0--Chappell.pdf

  • Machine Learning questions

    1. How much / how many? Regression

    2. Which category? Classification

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