Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

IoT, Automation and AI to enrich Human Experience

2,449 views

Published on

IoT, Automation and AI to enrich Human Experience

Published in: Business
  • Be the first to comment

IoT, Automation and AI to enrich Human Experience

  1. 1. IoT, Automation and AI to enrich Human Experience Hassan Sawaf Director of Applied Science & Artificial Intelligence Amazon Web Services
  2. 2. Agenda • My Motivation • Amazon Software Services • Alexa • AWS • Use Cases • Q&A
  3. 3. Agenda • My Motivation
  4. 4. Agenda • My Motivation Personal Background: • Serial Entrepreneur since mid-90s • Speech Recognition, Machine Translation and Computer Vision since 1996 • Daimler Benz, AIXPLAIN AG, AppTek Inc., SAIC, eBay • now Amazon (AWS AI)
  5. 5. Agenda • My Motivation Personal Experiences: • Business ideas often require complex AI services • E.g. “real-time speech translation” • E.g. “personal voice assistant” • Expensive R&D necessary to establish robust AI services • Challenges can me prohibitive for small and large enterprises
  6. 6. Alexa • Goal: Ubiquitous Computing • Alexa Skills Kit • Alexa Voice Services • Alexa Fund • Smart Home
  7. 7. Amazon Web Services • Goal: Ubiquitous Computing • Check it out: https://aws.amazon.com
  8. 8. Amazon Web Services • Goal: Ubiquitous Computing • Check it out: https://aws.amazon.com
  9. 9. Amazon Web Services • Goal: Ubiquitous Computing • Check it out: https://aws.amazon.com
  10. 10. AWS Internet of Things • AWS IoT service since October 2015 • Check it out on: https://aws.amazon.com/iot-platform
  11. 11. AWS Internet of Things • AWS IoT service since November 2016 • Check it out on: https://aws.amazon.com/greengrass
  12. 12. Amazon Web Services • Goal: Ubiquitous Computing • Check it out: https://aws.amazon.com
  13. 13. AWS Machine Learning • AWS ML service since November 2016 • Check it out on: https://aws.amazon.com/machine-learning
  14. 14. AWS Lex • AWS Lex service since November 2016 • Check it out on: https://aws.amazon.com/iot-platform
  15. 15. The Advent Of Conversational Interactions 1st Gen: Machine-oriented interactions
  16. 16. The Advent Of Conversational Interactions 1st Gen: Machine-oriented interactions 2nd Gen: Control-oriented & translated
  17. 17. The Advent Of Conversational Interactions 1st Gen: Machine-oriented interactions 2nd Gen: Control-oriented & translated 3rd Gen: Intent-oriented
  18. 18. AI Services Amazon Rekognition Amazon AI: Democratized Artificial Intelligence
  19. 19. AI Services Amazon Rekognition Amazon Polly Amazon AI: Democratized Artificial Intelligence
  20. 20. AI Services Amazon Rekognition Amazon Polly Amazon Lex Amazon AI: Democratized Artificial Intelligence
  21. 21. AI Services Amazon Rekognition Amazon Polly Amazon Lex More to come in 2017 Amazon AI: Democratized Artificial Intelligence
  22. 22. AI Services AI Platform Amazon Rekognition Amazon Polly Amazon Lex More to come in 2017 Amazon Machine Learning Amazon Elastic MapReduce Spark & SparkML More to come in 2017 Amazon AI: Democratized Artificial Intelligence
  23. 23. AI Services AI Platform AI Engines Amazon Rekognition Amazon Polly Amazon Lex More to come in 2017 Amazon Machine Learning Amazon Elastic MapReduce Spark & SparkML More to come in 2017 Apache MXNet TensorFlow Caffe Theano KerasTorch CNTK Amazon AI: Democratized Artificial Intelligence
  24. 24. AI Services AI Platform AI Engines Amazon Rekognition Amazon Polly Amazon Lex More to come in 2017 Amazon Machine Learning Amazon Elastic MapReduce Spark & SparkML More to come in 2017 Apache MXNet Caffe Theano KerasTorch CNTK Amazon AI: Democratized Artificial Intelligence TensorFlow P2 ECS Lambda GreenGrass FPGAEMR/Spark More to come in 2017 Hardware
  25. 25. Autonomous Driving Systems
  26. 26. Computational Knowledge Engine
  27. 27. Pinterest Visual Search
  28. 28. Pinterest Lens
  29. 29. Recommendations & Ranking At Netflix Personalized ranking, page generation, search, similarity, ratings In 140 new countries, simultaneously
  30. 30. Rekognition: Object & Scene Detection
  31. 31. Rekognition: Facial Detection
  32. 32. Model Training Amazon AI: Building Intelligent Systems
  33. 33. Model Training Amazon AI: Building Intelligent Systems Inference in the Cloud
  34. 34. Model Training Amazon AI: Building Intelligent Systems Inference in the Cloud Inference at the Edge
  35. 35. Apache MXNet Programmable Portable High Performance Near linear scaling across hundreds of GPUs Highly efficient models for mobile and IoT Simple syntax, multiple languages
  36. 36. Why Apache MXNet? Most Open Best On AWS Optimized for deep learning on AWS Accepted into the Apache Incubator (Integration with AWS)
  37. 37. 0 4 8 12 16 1 2 4 8 16 Ideal Inception v3 Resnet Alexnet 91% Efficiency Amazon AI: Scaling With MXNet
  38. 38. 0 64 128 192 256 1 2 4 8 16 32 64 128 256 Amazon AI: Scaling With MXNet
  39. 39. Ideal Inception v3 Resnet Alexnet 88% Efficiency 0 64 128 192 256 1 2 4 8 16 32 64 128 256 Amazon AI: Scaling With MXNet
  40. 40. Apache MXNet Background
  41. 41. MXNet Overview • Founded by: U.Washington, Carnegie Mellon U. (~1.5yrs old) • Recently Accepted to the Apache Incubator • State of the Art Model Support: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) • Ultra-scalable: Near-linear scaling equals fastest time to model • Multi-language: Support for Scala, Python, R, etc.. for legacy code leverage and easy integration with Spark • Ecosystem: Vibrant community from Academia and Industry Open Source Project on Github | Apache-2 Licensed
  42. 42. Collaborations and Community 4th DL Framework in Popularity (Outpacing Torch, CNTK and Theano) 0 27.5 55 82.5 110 137.5 TensorFlow Caffe Keras MXNet Theano Deeplearning4j CNTK Torch7 Popularity Diverse Community (Spans Industry and Academia) 0 15000 30000 45000 60000 Bing Xu (Apple) Tianqi Chen (UW) Mu Li (CMU/AWS) Eric Xie (UW/AWS) Yizhi Liu (Mediav) Chiyuan Zhang (MIT) Tianjun Xiao (Micrsoft) Yutian Li (Face++) Guo Jian (Tusimple) Guosheng Dong (sogou) Yu Zhang (MIT) Depeng Liang (?) Qiang Kou (Indiana U) Xingjian Shi (HKUST) Naiyan Wang (Tusimple) Top Contributors
  43. 43. Roadmap / Areas of Investment • NNVM Migration (complete) • Apache project (Accepted and transitioning to Apache) • Usability • Keras Integration WIP (Expected by Q2) • MinPy being merged (Dynamic Computation graphs, Std Numpy interface) • Documentation (installation, native documents, etc.) • Tutorials, examples • Platform support (Linux, Windows, OS X, mobile …) • Language bindings (Python, C++, R, Scala, Julia, JavaScript …) • Sparse datatypes and LSTM performance improvements • Deploy your model your way: Lambda, EC2/Docker, Raspberry Pi
  44. 44. Application Examples | Python notebooks • https://github.com/dmlc/mxnet-notebooks • Basic concepts • NDArray - multi-dimensional array computation • Symbol - symbolic expression for neural networks • Module - neural network training and inference • Applications • MNIST: recognize handwritten digits • Check out the distributed training results • Predict with pre-trained models • LSTMs for sequence learning • Recommender systems • Train a state of the art Computer Vision model (CNN) • Lots more..
  45. 45. Call to Action MXNet Resources: • MXNet Blog Post | AWS Endorsement • Read up on MXNet and Learn More: mxnet.io • MXNet Github Repo • MXNet Recommender Systems Talk | Leo Dirac Developer Resources: • Deep Learning AMI |Amazon Linux • Deep Learning AMI | Ubuntu – NEW!!! • P2 Instance Information • CloudFormation Template Instructions • Deep Learning Benchmark • MXNet on Lambda • MXNet on ECS/Docker • MXNet on Raspberry Pi | Wine Detector
  46. 46. Thank you! spisakj@amazon.com Joseph Spisak Manager | Product Mgmt AI & Deep Learning hassan@amazon.com Hassan Sawaf Director AI & Applied Sciences

×