Uploaded byAdrian Hornsby
Moving Forward with AI
The document outlines the fundamentals of artificial intelligence (AI), its historical development, and various neural network applications such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). It emphasizes the importance of data collection, algorithms, and integrating AI into organizational processes to improve decision-making. Additionally, the document introduces AWS services that facilitate AI deployment, encouraging developers and data scientists to leverage these tools for innovation.
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Moving Forward with AI
- 1. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. Adrian Hornsby, Cloud Architecture Evangelist @ AWS @adhorn Moving forward with AI
- 3. What is ArtificialIntelligence?
- 4. “A system orservice which can perform tasks that usually require human intelligence”
- 5. Predicting the priceof a house with humans Price City ZipCode Life Quality Parking Size # Room Accessibility Family Friendly
- 6. Predicting the priceof a house with neural network Price City ZipCode Life Quality Parking Size # Room Accessibility Family Friendly Input Output Discovered by the neural network
- 7.
- 8. The (60 years)rise of Artificial Intelligence
- 9. One of the”Founding Father" of Artificial Intelligence John McCarthy 1955
- 10. Photo from the1956 Dartmouth Conference with Marvin Minsky, Ray Solomonoff, Claude Shannon, John McCarthy, Trenchard More, Oliver Selfridge and Nathaniel Rawchester
- 11.
- 12. First known deepnetwork https://devblogs.nvidia.com/deep-learning-nutshell-history-training/ Alexey Grigorevich Ivakhnenko, 1965
- 13.
- 14. LeCun, 1989 First applicationof backpropagation https://www.youtube.com/watch?v=FwFduRA_L6Q
- 15. The curse ofdimensionality
- 16. The Advent ofAI Algorithms
- 17. The Advent ofAI Data Algorithms
- 18. The Advent ofAI Data GPUs & Acceleration Algorithms
- 19. The Advent ofAI Data GPUs & Acceleration Cloud Computing Algorithms AWS
- 24. Common neural networks &use cases
- 26. Convolutional Neural Networks(CNN) Conv 1 Conv 2 Conv n … … Feature Maps Labrador Dog Beach Outdoors Softmax Probability Fully Connected Layer
- 27. https://www.youtube.com/watch?v=qGotULKg8e0 • Over 10million 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
- 28. ImageNet Classification ErrorOver Time
- 29. CNN: Object Detection https://github.com/precedenceguo/mx-rcnnhttps://github.com/zhreshold/mxnet-yolo
- 30.
- 31. Autonomous Driving Systems CNN:Object Segmentation
- 32.
- 33.
- 34. CNN: Neural StyleTransfer
- 35. Long Short TermMemory 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.
- 36.
- 37. Generative Adversarial Networks(GAN) The future at work (already) today Generating new ”celebrity” faces https://github.com/tkarras/progressive_growing_of_gans
- 38. Generative adversarial networks(GAN) The future at work (already) today Semantic labels → Cityscapes street views https://tcwang0509.github.io/pix2pixHD/
- 39. Data, Algorithms, Humansand Artificial Intelligence
- 40.
- 41. How much datado you need?
- 42. Predicting the priceof a house castle 150+ rooms
- 43. 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
- 44. Most important foryou to do today? “Data is gold”
- 45. Pro-tip • Make itridiculously 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.
- 46. What processes shouldyou boost with AI?
- 47. Where to lookat in your organisation ? • Where data is being analysed to help making decisions. • Sales • Marketing • Social media • Customer supports • Logs • Etc.
- 48. How do youstart? The Low Hanging Fruits
- 49. Put AI inthe hands of every developer and data scientist AI @ AWS: Our mission
- 52. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved.
- 53. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved.
- 54. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved.
- 55. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved.
- 56. 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
- 57. Application Services The lowhanging 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
- 58. Amazon Rekognition Object and scenedetection Facial analysis Face comparison Person Tracking Celebrity recognition Image moderation Text-in-Image Amazon Rekognition (Image & Video) Deep learning-based visual analysis service
- 60. Marinus Analytics usesfacial 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
- 61. Amazon Polly Hei! Jegheter 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
- 62. “With Amazon Pollyour users benefit from the most lifelike Text-to-Speech voices available on the market.” Severin Hacker CTO, Duolingo
- 63. ” “ Amazon Pollydelivers 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
- 65. Amazon Lex “What’s theweather forecast?” “It will be sunny and 25°C” Weather Forecast Amazon Lex Build Conversational Chatbots
- 66. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved.
- 67. “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
- 68. “Hello, this isAllan speaking” Amazon Transcribe Automatic speech recognition service Amazon Transcribe
- 69. Amazon Comprehend Discover insightsfrom text Entities Key Phrases Language Sentiment Amazon Comprehend
- 70. STORM WORLD SERIES STOCK MARKET WASHINGTON LIBRARYOF NEWS ARTICLES * Amazon Comprehend * Integrated with Amazon S3 and AWS Glue Amazon Comprehend Support for large data sets and topic modeling
- 71. Twitter Stream API Kinesis Lambda S3 Athena TranslateComprehend Transcribe 1 day to build, $17/day to run (to analyze tweets for AWS-size customers) Multilingual Social Analytics
- 72. Moving deeper inthe rabbit-hole
- 73. © 2017, AmazonWeb 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
- 74. Say hello toTransfer 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.
- 75. Model Zoos (hiddengem 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.
- 76.
- 77. End-to-End Machine Learning Platform Zero setupFlexible Model Training Pay by the second $ Amazon SageMaker Build, train, and deploy machine learning models at scale
- 78.
- 79. 1. Understand whatAI 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.
- 80. There’s Never BeenA Better Time To Build New Businesses @adhorn