Machines are Learning: Bringing Powerful Artificial Intelligence to All Developers

Machines are Learning: Bringing Powerful Artificial Intelligence to All Developers

Codemotion, Milan, November 10th, 2017

Have you always wanted to add predictive capabilities, image or voice recognition to your application, but haven’t been able to find the time or the right technology to get started? Everybody wants to build smart apps, but only a few are Data Scientists. This session will help you understand machine learning terminology & challenges, what deep learning is and the possible use cases, how to build a machine learning model that works, and how to use developer-ready APIs for high-quality, high-accuracy AI capabilities that are scalable and cost-effective.

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Danilo Poccia

November 10, 2017
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  1. Machines are Learning Danilo Poccia, AWS Technical Evangelist CODEMOTION MILAN

    - SPECIAL EDITION 10 – 11 NOVEMBER 2017
  2. Machines are Learning Bringing Powerful Artificial Intelligence to All Developers

    Danilo Poccia AWS Technical Evangelist @danilop danilop@amazon.com danilop
  3. Credit: Gerry Cranham/Fox Photos/Getty Images http://www.telegraph.co.uk/travel/destinations/europe/united-kingdom/england/london/galleries/The-history-of-the-Tube-in-pictures-150-years-of-London-Underground/1939-ticket-examin/

  4. 1939 London Underground Credit: Gerry Cranham/Fox Photos/Getty Images http://www.telegraph.co.uk/travel/destinations/europe/united-kingdom/england/london/galleries/The-history-of-the-Tube-in-pictures-150-years-of-London-Underground/1939-ticket-examin/

  5. Data Predictions

  6. Data Model Predictions

  7. Model

  8. http://www.thehudsonvalley.com/articles/60-years-ago-today-local-technology-demonstrated-artificial-intelligence-for-the-first-time 1959 Arthur Samuel

  9. Machine Learning

  10. Machine Learning Supervised Learning Inferring a model from labeled training

    data
  11. Machine Learning Supervised Learning Unsupervised Learning Inferring a model from

    labeled training data Inferring a model to describe hidden structure from unlabeled data
  12. Reinforcement Learning Perform a certain goal in a dynamic environment

    Machine Learning Supervised Learning Unsupervised Learning
  13. Driving a vehicle Playing a game against an opponent

  14. Clustering

  15. Clustering

  16. Tip: Try topic modeling with your own emails ;-) Topic

    Modeling Discovering abstract “topics” that occur in a collection of documents For example, looking for “infrequent” words that are used more often in a document
  17. Regression “How many bikes will be rented tomorrow?” Happy, Sad,

    Angry, Confused, Disgusted, Surprised, Calm, Unknown Binary Classification Multi-Class Classification “Is this email spam?” “What is the sentiment of this tweet, or of this social media comment?” 1, 0, 100K Yes / No True / False %
  18. Training the Model Minimizing the Error of using the Model

    on the Labeled Data
  19. Validation How well is this Model working on New Data?

  20. Be Careful of Overfitting

  21. Be Careful of Overfitting

  22. Be Careful of Overfitting

  23. Better Fitting

  24. Better Fitting

  25. Different Models ⇒ Different Predictions

  26. Labeled Data

  27. Labeled Data 70% 30% Training Validation

  28. Neural Networks

  29. 1943 Warren McCulloch, Walter Pitts Threshold Logic Units

  30. 1962 Frank Rosenblatt Perceptron

  31. ∑ w1 w2 w3 wn w0 = output weights (parameters)

    activation function input
  32. f(∑) w1 w2 w3 wn w0 = weights (parameters) activation

    function output input
  33. f(∑) input output

  34. 1969 Marvin Minsky, Seymour Papert Perceptrons: An Introduction to Computational

    Geometry A perceptron can only solve linearly separable functions (e.g. no XOR)
  35. f(∑) f(∑) f(∑) f(∑) f(∑) f(∑) f(∑) f(∑) f(∑) input

    layer hidden layer output layer input output Multiple Layers Lots of Parameters Backpropagation
  36. Microprocessor Transistor Counts 1971-2011 Intel Xeon CPU 28 cores NVIDIA

    V100 GPU 5,120 CUDA Cores 640 Tensor Cores https://en.wikipedia.org/wiki/Moore's_law
  37. LeCun, Gradient-Based Learning Applied to Document Recognition,1998 Hinton, A Fast

    Learning Algorithm for Deep Belief Nets, 2006 Bengio, Learning Deep Architectures for AI, 2009 Advances in Research 1998-2009
  38. “Stacks of differentiable non-linear functions with lots of parameters solve

    nearly any predictive modeling problem” —Jeremy Howard, fast.ai
  39. Image Processing

  40. f(∑) f(∑) f(∑) f(∑) f(∑) f(∑) f(∑) f(∑) f(∑) output

    How to give images in input to a Neural Network? Photo by David Iliff. License: CC-BY-SA 3.0 https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg
  41. Convolution Matrix 0 0 0 0 1 0 0 0

    0 Identity Photo by David Iliff. License: CC-BY-SA 3.0 https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg
  42. Convolution Matrix 1 0 -1 2 0 -2 1 0

    -1 Left Edges Photo by David Iliff. License: CC-BY-SA 3.0 https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg
  43. Convolution Matrix -1 0 1 -2 0 2 -1 0

    1 Right Edges Photo by David Iliff. License: CC-BY-SA 3.0 https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg
  44. Convolution Matrix 1 2 1 0 0 0 -1 -2

    -1 Top Edges Photo by David Iliff. License: CC-BY-SA 3.0 https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg
  45. Convolution Matrix -1 -2 -1 0 0 0 1 2

    1 Bottom Edges Photo by David Iliff. License: CC-BY-SA 3.0 https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg
  46. Convolution Matrix 0.6 -0.6 1.2 -1.4 1.2 -1.6 0.8 -1.4

    1.6 Random Values Photo by David Iliff. License: CC-BY-SA 3.0 https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg
  47. Convolutional Neural Networks (CNNs) https://en.wikipedia.org/wiki/Convolutional_neural_network

  48. ImageNet Classification Error Over Time 0 5 10 15 20

    25 30 2010 2011 2012 2013 2014 2015 2016 2017 Classification Error CNNs
  49. 2012 ImageNet Classification with Deep Convolutional Neural Networks

  50. SuperVision: 8 layers, 60M parameters 0

  51. 2013 Visualizing and Understanding Convolutional Networks

  52. None
  53. None
  54. How Do Neural Networks Learn? ? More generic and can

    be reused as feature extractor for other visual tasks Specific to task Cat Dog 0
  55. The Challenge For Machine Learning: Scale Aggressive migration New data

    created on AWS PBs of existing data Data
  56. The Challenge For Machine Learning: Scale Tons of GPUs Elastic

    capacity Pre-built images Aggressive migration New data created on AWS PBs of existing data Data Training
  57. The Challenge For Machine Learning: Scale Tons of GPUs and

    CPUs Serverless At the edge, on IoT Devices Tons of GPUs Elastic capacity Pre-built images Aggressive migration New data created on AWS PBs of existing data Data Training Prediction
  58. Natural Language Processing Experiment: Topic Modeling EC2 Spot Instances 1.1

    Million vCPUs
  59. Fulfilment & logistics Search & discovery Existing products New products

    Thousands Of Amazon Engineers Focused On Machine Learning
  60. None
  61. None
  62. Machine Learning On AWS Today

  63. Artificial Intelligence In The Hands Of Every Developer S E

    R V I C E S P L A T F O R M S E N G I N E S I N F R A S T R U C T U R E GPU CPU IoT Mobile Apache MXNet Caffe 2 Theano PyTorch CNTK TensorFlow
  64. Early Detection Of Diabetic Complications

  65. FDA-approved Medical Imaging

  66. Sports Analytics

  67. Autonomous Driving Systems

  68. Real Time, Per Pixel Object Segmentation

  69. Centimeter-accurate Positioning

  70. Computation Knowledge Engine

  71. None
  72. S E R V I C E S P L

    A T F O R M S E N G I N E S I N F R A S T R U C T U R E Amazon ML Spark & EMR Kinesis Batch ECS GPU CPU IoT Mobile Apache MXNet Caffe 2 Theano PyTorch CNTK TensorFlow Artificial Intelligence In The Hands Of Every Developer
  73. S E R V I C E S P L

    A T F O R M S Vision Amazon Rekognition E N G I N E S I N F R A S T R U C T U R E Amazon ML Spark & EMR Kinesis Batch ECS GPU CPU IoT Mobile Apache MXNet Caffe 2 Theano PyTorch CNTK TensorFlow Artificial Intelligence In The Hands Of Every Developer
  74. Mona Lisa (Leonardo da Vinci)

  75. None
  76. None
  77. None
  78. Mona Lisa (Prado's version)

  79. Portrait of Maddalena Doni (Raphael)

  80. Bynder allows you to easily create, find and use content

    for branding automation and marketing solutions. With our new AI capabilities, Bynder’s software… now allows users to save hours of admin labor when uploading and organizing their files, adding exponentially more value. Chris Hall CEO, Bynder ” “ With Rekognition, Bynder revolutionizes marketing admin tasks with AI capabilities
  81. S E R V I C E S P L

    A T F O R M S Speech Amazon Polly Vision Amazon Rekognition E N G I N E S I N F R A S T R U C T U R E Amazon ML Spark & EMR Kinesis Batch ECS GPU CPU IoT Mobile Apache MXNet Caffe 2 Theano PyTorch CNTK TensorFlow Artificial Intelligence In The Hands Of Every Developer
  82. Generate Lifelike Speech With Amazon Polly 24 languages “The temperature

    in Milanis 16 degrees Celsius” “The temperature in Milan is 16˚C” Amazon Polly 50 voices
  83. aws polly synthesize-speech --text "It was nice to live such

    a wonderful live show." --output-format mp3 --voice-id Joanna --text-type text output.mp3
  84. “Nel mezzo del cammin di nostra vita mi ritrovai per

    una selva oscura ché la diritta via era smarrita.” https://commons.wikimedia.org/wiki/File:Portrait_de_Dante.jpg
  85. Duolingo voices its language learning service Using Polly Duolingo is

    a free language learning service where users help translate the web and rate translations. With Amazon Polly our users benefit from the most lifelike Text-to-Speech voices available on the market. Severin Hacker CTO, Duolingo ” “ • Spoken language crucial for language learning • Accurate pronunciation matters • Faster iteration thanks to TTS • As good as natural human speech
  86. ” “ Royal National Institute of Blind People creates and

    distributes accessible information in the form of synthesized content Amazon Polly delivers incredibly lifelike voices which captivate and engage our readers. John Worsfold Solutions Implementation Manager, RNIB • RNIB delivers largest library of audiobooks in the UK for nearly 2 million people with sight loss • Naturalness of generated speech is critical to captivate and engage readers • No restrictions on speech redistributions enables RNIB to create and distribute accessible information in a form of synthesized content RNIB provides the largest library in the UK for people with sight loss
  87. S E R V I C E S P L

    A T F O R M S Chat Amazon Lex Speech Amazon Polly Vision Amazon Rekognition E N G I N E S I N F R A S T R U C T U R E Amazon ML Spark & EMR Kinesis Batch ECS GPU CPU IoT Mobile Apache MXNet Caffe 2 Theano PyTorch CNTK TensorFlow Artificial Intelligence In The Hands Of Every Developer
  88. Amazon Lex Speech recognition and natural language understanding Automatic speech

    recognition Natural language understanding “What’s the weather forecast?” Weather forecast Amazon Lex
  89. Amazon Lex Speech recognition and natural language understanding “It will

    be sunny and 16C” Automatic speech recognition Natural language understanding “What’s the weather forecast?” Weather forecast Amazon Lex
  90. “It will be sunny and 16 degrees Celsius” Amazon Polly

    Amazon Lex “It will be sunny and 16C” Automatic speech recognition Natural language understanding “What’s the weather forecast?” Weather forecast Speech recognition and natural language understanding Amazon Lex
  91. ” “ Finding missing persons: ~100,000 active missing persons cases

    in the U.S. at any given time ~60% are adults, ~40% are children • Motorola Solutions applies Amazon Rekognition, Amazon Polly and Amazon Lex • Image analytics and facial recognition can continually monitor for missing persons • Tools that understand natural language can enable officers to keep eyes up and hands free Motorola Solutions is using AI to help finding missing persons Motorola Solutions keeps utility workers connected and visible to each other with real-time voice and data communication across the smart grid.
  92. S E R V I C E S P L

    A T F O R M S Chat Amazon Lex Speech Amazon Polly Vision Amazon Rekognition E N G I N E S I N F R A S T R U C T U R E Amazon ML Spark & EMR Kinesis Batch ECS GPU CPU IoT Mobile Apache MXNet Caffe 2 Theano PyTorch CNTK TensorFlow
  93. There’s Never Been A Better Time To Build Smart Apps

  94. https://github.com/danilop/security-camera

  95. Machines are Learning Bringing Powerful Artificial Intelligence to All Developers

    Danilo Poccia AWS Technical Evangelist @danilop danilop@amazon.com danilop