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. 2.

    Machines are Learning Bringing Powerful Artificial Intelligence to All Developers

    Danilo Poccia AWS Technical Evangelist @danilop danilop@amazon.com danilop
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    Machine Learning Supervised Learning Unsupervised Learning Inferring a model from

    labeled training data Inferring a model to describe hidden structure from unlabeled data
  4. 12.

    Reinforcement Learning Perform a certain goal in a dynamic environment

    Machine Learning Supervised Learning Unsupervised Learning
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    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
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    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 %
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    1969 Marvin Minsky, Seymour Papert Perceptrons: An Introduction to Computational

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

    layer hidden layer output layer input output Multiple Layers Lots of Parameters Backpropagation
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    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
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    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
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    “Stacks of differentiable non-linear functions with lots of parameters solve

    nearly any predictive modeling problem” —Jeremy Howard, fast.ai
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    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
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    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
  15. 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
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    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
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    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
  18. 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
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    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
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    ImageNet Classification Error Over Time 0 5 10 15 20

    25 30 2010 2011 2012 2013 2014 2015 2016 2017 Classification Error CNNs
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    How Do Neural Networks Learn? ? More generic and can

    be reused as feature extractor for other visual tasks Specific to task Cat Dog 0
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    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
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    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
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    Fulfilment & logistics Search & discovery Existing products New products

    Thousands Of Amazon Engineers Focused On Machine Learning
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    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
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    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
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    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
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    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
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    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
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    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
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    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
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    “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
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    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
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    ” “ 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
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    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
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    Amazon Lex Speech recognition and natural language understanding Automatic speech

    recognition Natural language understanding “What’s the weather forecast?” Weather forecast Amazon Lex
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    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
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    “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
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    ” “ 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.
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    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
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    Machines are Learning Bringing Powerful Artificial Intelligence to All Developers

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