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Moving Forward with AI

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AI: State of the Union
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Moving Forward with AI

Self-Driving cars. Commercial drones. Smart cameras. Movie and music creation. Powerful & intelligent robots. Over the past few years, a new revolution has brought AI almost to the level of science-fiction. However, most companies are not worried about far-off futuristic applications of AI, they want to know what AI can do - today - for their organisations. Distinguishing the hype from reality can be a bit confusing, especially when you consider the attention that AI gets from the media and commentators. So, how can your organisation get started and put AI to work for you? That is the question I will answer in this talk. From greater customer intimacy, increasing competitive advantage and improving efficiency, I will discuss and show how AI can be used today and help the organisation in more impactful ways.

Self-Driving cars. Commercial drones. Smart cameras. Movie and music creation. Powerful & intelligent robots. Over the past few years, a new revolution has brought AI almost to the level of science-fiction. However, most companies are not worried about far-off futuristic applications of AI, they want to know what AI can do - today - for their organisations. Distinguishing the hype from reality can be a bit confusing, especially when you consider the attention that AI gets from the media and commentators. So, how can your organisation get started and put AI to work for you? That is the question I will answer in this talk. From greater customer intimacy, increasing competitive advantage and improving efficiency, I will discuss and show how AI can be used today and help the organisation in more impactful ways.

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Moving Forward with AI

  1. 1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Adrian Hornsby, Cloud Architecture Evangelist @ AWS @adhorn Moving forward with AI
  2. 2. What is Artificial Intelligence?
  3. 3. “A system or service which can perform tasks that usually require human intelligence”
  4. 4. Predicting the price of a house with humans Price City ZipCode Life Quality Parking Size # Room Accessibility Family Friendly
  5. 5. Predicting the price of a house with neural network Price City ZipCode Life Quality Parking Size # Room Accessibility Family Friendly Input Output Discovered by the neural network
  6. 6. Artificial Neural Network
  7. 7. The (60 years) rise of Artificial Intelligence
  8. 8. One of the ”Founding Father" of Artificial Intelligence John McCarthy 1955
  9. 9. Photo from the 1956 Dartmouth Conference with Marvin Minsky, Ray Solomonoff, Claude Shannon, John McCarthy, Trenchard More, Oliver Selfridge and Nathaniel Rawchester
  10. 10. Frank Rosenblatt, 1957 Perceptron
  11. 11. First known deep network https://devblogs.nvidia.com/deep-learning-nutshell-history-training/ Alexey Grigorevich Ivakhnenko, 1965
  12. 12. Paul Werbos, 1975 Backpropagation
  13. 13. LeCun, 1989 First application of backpropagation https://www.youtube.com/watch?v=FwFduRA_L6Q
  14. 14. The curse of dimensionality
  15. 15. The Advent of AI Algorithms
  16. 16. The Advent of AI Data Algorithms
  17. 17. The Advent of AI Data GPUs & Acceleration Algorithms
  18. 18. The Advent of AI Data GPUs & Acceleration Cloud Computing Algorithms AWS
  19. 19. Common neural networks & use cases
  20. 20. Convolutional Neural Networks (CNN) Conv 1 Conv 2 Conv n … … Feature Maps Labrador Dog Beach Outdoors Softmax Probability Fully Connected Layer
  21. 21. https://www.youtube.com/watch?v=qGotULKg8e0 • Over 10 million images from 300,000 hotels • Using Keras and EC2 GPU instances • Fine-tuned a pre-trained Convolutional Neural Network using 100,000 images • Hotel descriptions now automatically feature the best available images CNN: Object Classification Nuno Castro - Ranking hotel images using deep learning
  22. 22. ImageNet Classification Error Over Time
  23. 23. CNN: Object Detection https://github.com/precedenceguo/mx-rcnn https://github.com/zhreshold/mxnet-yolo
  24. 24. CNN: Face Detection https://github.com/tornadomeet/mxnet-face
  25. 25. Autonomous Driving Systems CNN: Object Segmentation
  26. 26. CNN: Object Segmentation
  27. 27. FDA-approved medical imaging https://www.periscope.tv/AWSstartups/1vAGRgevBXRJl https://www.youtube.com/watch?v=WE81dncwnIc CNN: Object Segmentation
  28. 28. CNN: Neural Style Transfer
  29. 29. Long Short Term Memory Networks (LSTM) • LSTM are capable of learning long-term dependencies • Designed to recognize patterns in sequences of data such as: • text • genomes • handwriting • spoken words • numerical times series data coming from sensors, stock markets, etc.
  30. 30. LSTM: Machine Translation https://github.com/awslabs/sockeye
  31. 31. Generative Adversarial Networks (GAN) The future at work (already) today Generating new ”celebrity” faces https://github.com/tkarras/progressive_growing_of_gans
  32. 32. Generative adversarial networks (GAN) The future at work (already) today Semantic labels → Cityscapes street views https://tcwang0509.github.io/pix2pixHD/
  33. 33. Data, Algorithms, Humans and Artificial Intelligence
  34. 34. Ground Truth Generation Training
  35. 35. How much data do you need?
  36. 36. Predicting the price of a house castle 150+ rooms
  37. 37. Rule of thumbs • Data should cover as many combinations of features as possible • More data is almost always better • Approx. 10x more than the number of features
  38. 38. Most important for you to do today? “Data is gold”
  39. 39. Pro-tip • Make it ridiculously easy to collect and store any type of data. • One line of code should be all it takes for anyone in the company to start collecting and storing new data type.
  40. 40. What processes should you boost with AI?
  41. 41. Where to look at in your organisation ? • Where data is being analysed to help making decisions. • Sales • Marketing • Social media • Customer supports  • Logs • Etc.
  42. 42. How do you start? The Low Hanging Fruits
  43. 43. Put AI in the hands of every developer and data scientist AI @ AWS: Our mission
  44. 44. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  45. 45. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  46. 46. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  47. 47. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  48. 48. Application Services Platform Services Frameworks & Infrastructure API-driven services: Vision & Language Services, Conversational Chatbots Deploy machine learning models with high-performance machine learning algorithms, broad framework support, and one-click training, tuning, and inference. Develop sophisticated models with any framework, create managed, auto- scaling clusters of GPUs for large scale training, or run inference on trained models. AI @ AWS – Stack AI in the hands of every developer and data scientist
  49. 49. Application Services The low hanging fruits • API-driven • Not training required • Pre-trained on general datasets • No infrastructure to manage • Use it now with one line of code Application Services API-driven services: Vision & Language Services, Conversational Chatbots
  50. 50. Amazon Rekognition Object and scene detection Facial analysis Face comparison Person Tracking Celebrity recognition Image moderation Text-in-Image Amazon Rekognition (Image & Video) Deep learning-based visual analysis service
  51. 51. Marinus Analytics uses facial recognition to stop human trafficking “Now with Traffic Jam’s FaceSearch, powered by Amazon Rekognition, investigators are able to take effective action by searching through millions of records in seconds to find victims.” http://www.marinusanalytics.com/articles/2017/10/17/amazon-rekognition-helps-marinus-analytics-fight-human-trafficking
  52. 52. Amazon Polly Hei! Jeg heter Liv. Skriv inn noe her, så leser jeg det opp. Amazon Polly Text In, Life-like Speech Out The Text-To-Speech technology behind Amazon Polly takes advantage of bidirectional long short-term memory (LSTM)* * https://www.allthingsdistributed.com/2016/11/amazon-ai-and-alexa-for-all-aws-apps.html
  53. 53. “With Amazon Polly our users benefit from the most lifelike Text-to-Speech voices available on the market.” Severin Hacker CTO, Duolingo
  54. 54. ” “ 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
  55. 55. Amazon Lex “What’s the weather forecast?” “It will be sunny and 25°C” Weather Forecast Amazon Lex Build Conversational Chatbots
  56. 56. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  57. 57. “Hello, what’s up? Do you want to go see a movie tonight?” Amazon Translate Natural and fluent language translation "Bonjour, quoi de neuf ? Tu veux aller voir un film ce soir ?" Amazon Translate
  58. 58. “Hello, this is Allan speaking” Amazon Transcribe Automatic speech recognition service Amazon Transcribe
  59. 59. Amazon Comprehend Discover insights from text Entities Key Phrases Language Sentiment Amazon Comprehend
  60. 60. STORM WORLD SERIES STOCK MARKET WASHINGTON LIBRARY OF NEWS ARTICLES * Amazon Comprehend * Integrated with Amazon S3 and AWS Glue Amazon Comprehend Support for large data sets and topic modeling
  61. 61. Twitter Stream API Kinesis Lambda S3 Athena Translate Comprehend Transcribe 1 day to build, $17/day to run (to analyze tweets for AWS-size customers) Multilingual Social Analytics
  62. 62. Moving deeper in the rabbit-hole
  63. 63. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Data Visualization & Analysis Business Problem – ML problem framing Data Collection Data Integration Data Preparation & Cleaning Feature Engineering Model Training & Parameter Tuning Model Evaluation Are Business Goals met? Model Deployment Monitoring & Debugging YesNo DataAugmentation Feature Augmentation The AI Process Re-training Predictions
  64. 64. Say hello to Transfer Learning (hidden gem 1) • Initialise parameter with pre-trained model • Use pre-trained model as fixed feature extractor and build model based on feature • Why? • It takes a long time and a lot of resources to train a neural network from scratch.
  65. 65. Model Zoos (hidden gem 2) • Full implementations of many state-of-the-art models reported in the academic literature. • Complete models, with scripts, pre-trained weights and instructions on how to build and fine tune these models.
  66. 66. https://mxnet.apache.org/model_zoo/index.html
  67. 67. End-to-End Machine Learning Platform Zero setup Flexible Model Training Pay by the second $ Amazon SageMaker Build, train, and deploy machine learning models at scale
  68. 68. Wrapping up
  69. 69. 1. Understand what AI is. 2. Take great care of your data. 3. Find the processes that need improvements. 4. Start with the low hanging fruits. 5. Slowly develop yourself into an AI-powered organisation.
  70. 70. There’s Never Been A Better Time To Build New Businesses @adhorn

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