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AWS Artificial Intelligence Day - Toronto

  1. Amazon AI – Strategy and Current Offering Guy Ernest, Amazon AI
  2. Why we talk about AI & ML?
  3. How to be successful in Business E* BI RT ML EC2 ECS Elastic Beanstalk Redshift EMR Athena Kinesis* ElasticSearch ElastiCache Amazon ML Amazon AI Spark ML Deep Learning AMI
  4. Why we talk about AI & ML?
  5. Why we talk about AI & ML?
  6. The circle of ML Front-End team Data Engineering team Analysts / DS team DevOps team Business Problem Data ML Model ML Application
  7. The circle of ML Front-End team Data Engineering team Analysts / DS team DevOps team Business Problem Data ML Model ML Application S3 EMR Kinesis * Redshift Athena Glue ECS Lambda API-GW Code * DMS EB P2 Spot Batch IoT GreenGrass FPGA
  8. The circle of ML Front-End team Data Engineering team Analysts / DS team DevOps team Business Problem Data ML Data ML Application Heavy Lifting by AWS
  9. Dive Deep as much as you need Hardware - Distributed computing, GPU, FPGA, Green Grass DL - MXNet, TensorFlow, Caffe, Torch, Theano Platform – Data Science Environment (Spark on EMR, R …) Simple API Services Usage&Simplicity Control
  10. What do you need to know to use Machine Learning?
  11. ML Model is a function to split Space Historical Data Model Building Prediction What is my color? And what is mine?
  12. MNIST dataset in t-SNE
  13. Why more data is better? Less Data More Data Even More Data
  14. Why more attributes are better? Less Attributes More Attributes Even More Attributes Where to Split?
  15. Data Engineering
  16. Why Clean Data is better? Messy Data Cleaner Data Fantasy Data Gray Area
  17. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Please watch the re:invent session - Amazon Polly A service that turns text into lifelike speech
  18. What is Amazon Polly • A service that converts text into lifelike speech • Offers 47 lifelike voices across 24 languages • Low latency responses enable developers to build real-time systems • Developers can store, replay and distribute generated speech
  19. Amazon Polly: Quality Natural sounding speech A subjective measure of how close TTS output is to human speech. Accurate text processing Ability of the system to interpret common text formats such as abbreviations, numerical sequences, homographs etc. Today in Las Vegas, NV it's 54°F. "We live for the music", live from the Madison Square Garden. Highly intelligibile A measure of how comprehensible speech is. ”Peter Piper picked a peck of pickled peppers.”
  20. Amazon Polly: Language Portfolio Americas: • Brazilian Portuguese • Canadian French • English (US) • Spanish (US) A-PAC: • Australian English • Indian English • Japanese EMEA: • British English • Danish • Dutch • French • German • Icelandic • Italian • Norwegian • Polish • Portuguese • Romanian • Russian • Spanish • Swedish • Turkish • Welsh • Welsh English
  21. Recording Data for TTS Tons of text Recording script: Few weeks of recordings Automatic selection of texts Recording script: • Covers all combinations of diphones and significant features in a language
  22. an error occurred while searching for your route because snaps weren't all so obedient anymore, now we say apple again. and we say apple, general electric soars today. information on general electric quick breads, zucchini, holiday, crock pot, cake, so are you still keeping tabs on your old team, that weighs more than four tons, disrupts the herring's swim … An apple a day, keeps …
  23. Amazon Polly is cost-effective • Pay-as-You-go • $4 for 1M characters • Free Tier of 5M characters/month - first year • You can store and reuse generated speech
  24. Why voice matters • Spoken language crucial for language learning • Accurate pronunciation matters • Faster iteration thanks to TTS • As good as natural human speech
  25. GoAnimate is a cloud-based, animated video creation plarform. Amazon Polly gives GoAnimate users the ability to immediately give voice to the characters they animate using our platform. Alvin Hung CEO, GoAnimate ” “ • Multi-language communication • Training or HR professionals who have to create content in many languages • Video preproduction • Video makers who need to iterate and fine-tune before the text-to- speech is eventually replaced by a professional voiceover • K–12 education • Students who make videos and don’t have access to professional voices or time for or knowledge of voiceover With Polly, GoAnimate gives voice to the characters in their animations
  26. 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
  27. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Please watch the re:invent session: Introducing Amazon Lex Service for Building Voice or Text Chatbots
  28. Advent of Conversational Interactions 1st Gen: Punch Cards & Memory Registers 2nd Gen: Pointers & Sliders 3nd Gen: Conversational Interfaces
  29. Developer Challenges Speech Recognition Language Understanding Business Logic Disparate Systems Authentication Messaging platforms Scale Testing Security Availability Mobile Conversational interfaces need to combine a large number of sophisticated algorithms and technologies
  30. Amazon Lex - Features Text and Speech language understanding: Powered by the same technology as Alexa Enterprise SaaS Connectors: Connect to enterprise systems Deployment to chat services Designed for Builders: Efficient and intuitive tools to build conversations; scales automatically Versioning and alias support
  31. AWS Mobile Hub Integration Authenticate users Analyze user behavior Store and share media Synchronize data More …. Track retention Conversational Bots LexAWS Mobile SDKs AWS Mobile Hub
  32. Enterprise Connectors with Mobile Hub Amazon Lex Mobile App Mobile Hub SaaS Connector Amazon API Gateway AWS Lambda 1: Understand user intent Amazon API Gateway AWS Lambda 3: Translate REST response into natural language Mobile Hub Custom Connector 2: Invoke a SaaS application or an existing business application Business Application Firewall User Input
  33. Amazon Lex – Use Cases Informational Bots Chatbots for everyday consumer requests Application Bots Build powerful interfaces to mobile applications • News updates • Weather information • Game scores …. • Book tickets • Order food • Manage bank accounts …. Enterprise Productivity Bots Streamline enterprise work activities and improve efficiencies • Check sales numbers • Marketing performance • Inventory status …. Internet of Things (IoT) Bots Enable conversational interfaces for device interactions • Wearables • Appliances • Auto ….
  34. Lex Bot Structure Utterances Spoken or typed phrases that invoke your intent BookHotel Intents An Intent performs an action in response to natural language user input Slots Slots are input data required to fulfill the intent Fulfillment Fulfillment mechanism for your intent
  35. Fulfillment AWS Lambda Integration Return to Client User input parsed to derive intents and slot values. Output returned to client for further processing. Intents and slots passed to AWS Lambda function for business logic implementation.
  36. Customer Testimonials: Capital One “A highly scalable solution, it also offers potential to speed time to market for a new generation of voice and text interactions such as our recently launched Capital One skill for Alexa.” “As a heavy user of AWS, Amazon Lex’s seamless integration with other AWS services like AWS Lambda and AWS DynamoDB is really appealing.”
  37. Customer Testimonials: HubSpot “Through Amazon's Lex, we're adding sophisticated natural language processing capabilities that helps GrowthBot provide a more intuitive UI for our users. Amazon Lex lets us take advantage of advanced A.I. and machine learning without having to code the algorithms ourselves.” “HubSpot's GrowthBot is an all-in-one chatbot which helps marketers and sales people be more productive by providing access to relevant data and services using a conversational interface. With GrowthBot, marketers can get help creating content, researching competitors, and monitoring their analytics.”
  38. Amazon Lex Pricing Text Speech Price per 1000 requests $0.75 $4.00 Free Tier* (requests per month) 10,000 5,000 *Available for the first year upon sign-up to new Amazon Lex customers
  39. Amazon Lex - Technology Amazon Lex Automatic Speech Recognition (ASR) Natural Language Understanding (NLU) Same technology that powers Alexa Cognito CloudTrail CloudWatch AWS Services Action AWS Lambda Authentication & Visibility Speech API Language API Fulfillment End-Users Developers Console SDK Intents, Slots, Prompts, Utterances Input: Speech or Text Multi-Platform Clients: Mobile, IoT, Web, Chat API Response: Speech (via Polly TTS) or Text
  40. Alexa or Lex • Alexa is coming with the eco-system (devices, install base, 1st party and 3rd party skills) • Lex is for single tenant chatbot to integrate with own users
  41. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Please watch the re:invent session: Introducing Amazon Rekognitin Deep Learning image and facial recognition service
  42. Significantly improve many applications on multiple domains “deep learning” trend in the past 10 years image understanding speech recognition natural language processing … Deep Learning autonomy
  43. 0.2 -0.1 ... 0.7 Input Output 1 1 1 1 0 1 0 0 0 3 mx.sym.Pooling(data, pool_type="max", kernel=(2,2), stride=(2,2) lstm.lstm_unroll(num_lstm_layer, seq_len, len, num_hidden, num_embed) 4 2 2 0 4=Max 1 3 ... 4 0.2 -0.1 ... 0.7 mx.sym.FullyConnected(data, num_hidden=128) 2 mx.symbol.Embedding(data, input_dim, output_dim = k) Queen 4 2 2 0 2=Avg Input Weights cos(w, queen) = cos(w, king) - cos(w, man) + cos(w, woman) mx.sym.Activation(data, act_type="xxxx") "relu" "tanh" "sigmoid" "softrelu" Neural Art Face Search Image Segmentation Image Caption “People Riding Bikes” Bicycle, People, Road, Sport Image Labels Image Video Speech Text “People Riding Bikes” Machine Translation “Οι άνθρωποι ιππασίας ποδήλατα” Events mx.model.FeedForward mx.sym.SoftmaxOutput Anatomy of a Deep Learning Model mx.sym.Convolution(data, kernel=(5,5), num_filter=20) Anatomy of a Deep Learning Model
  44. TX1 on Flying Drone TX1 with customized board Drone Realtime detection and tracking on TX1 ~10 frame/sec with 640x480 resolution
  45. Deploy Everywhere Beyond BlindTool by Joseph Paul Cohen, demo on Nexus 4 ✦ Fit the core library with all dependencies into a single C++ source file ✦ Easy to compile on … Amalgamation Runs in browser with Javascript The first image for search “dog” at Outputs “beagle” with prob = 73% within 1 sec
  46. New P2 Instance | Up to 16 GPUs  This new instance type incorporates up to 8 NVIDIA Tesla K80 Accelerators, each running a pair of NVIDIA GK210 GPUs.  Each GPU provides 12 GiB of memory (accessible via 240 GB/second of memory bandwidth), and 2,496 parallel processing cores.  Available in PDX, IAD and DUB Regions Instance Name GPU Count vCPU Count Memory Parallel Processing Cores GPU Memory Network Performance p2.xlarge 1 4 61 GiB 2,496 12 GiB High p2.8xlarge 8 32 488 GiB 19,968 96 GiB 10 Gigabit p2.16xlarge 16 64 732 GiB 39,936 192 GiB 20 Gigabit
  47. Distributed Experiments Google Inception v3 Increasing machines from 1 to 47 2x faster than TensorFlow if using more than 10 machines [from Carlos Gustrin DSS’16 keynote]