AI & Enterprise

AI & Enterprise

AI Congress London keynote covering how to understand and implement applied AI for business.

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Melanie Warrick

January 30, 2018
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Transcript

  1. AI & Enterprise Melanie Warrick

  2. @nyghtowl

  3. @nyghtowl Multi years & Multi millions

  4. 90% of Data Last 2 Years

  5. 1.5GB / day avg person 1PB / day smart factory

    By 2020
  6. @nyghtowl Are you innovating with data?

  7. @nyghtowl Machine Learning

  8. @nyghtowl

  9. @nyghtowl “AI is the new electricity” - Andrew Ng ‘17

  10. @nyghtowl New tech is like magic Change => fear |

    excitement
  11. @nyghtowl “Science and engineering of making intelligent machines...” - John

    McCarthy ‘55
  12. @nyghtowl

  13. @nyghtowl

  14. @nyghtowl A.I. is the science of making technology smart

  15. @nyghtowl Artificial Intelligence Machine Learning Deep Learning

  16. @nyghtowl AlphaGo

  17. @nyghtowl Translate

  18. @nyghtowl Computer Vision

  19. @nyghtowl Sentiment Analysis

  20. @nyghtowl AI is not the destination How to apply it?

  21. @nyghtowl

  22. @nyghtowl Speech Recognition

  23. @nyghtowl Auction Site & Real-time Car Image Classifier 30K dealers

    | 5M cars Classification 20 - a couple minutes AUCNET
  24. @nyghtowl Compology Waste Removal Automation IoT Cameras & Vision Modeling

    Reduced pickups by ~50%
  25. @nyghtowl Largest South African pay-TV company Siloed data BigQuery Tailored

    subscriptions & real time offers real-time response 8.5M customers 27+ data sources 960M points consolidated MultiChoice
  26. @nyghtowl Goal Experts Data Solution

  27. @nyghtowl Goal Experts Data Solution

  28. @nyghtowl Define the Problem Revenue Growth Customer Satisfaction Responsiveness Sales

    Expenses Operations Processes Compliance Latency Product & Process Innovation
  29. @nyghtowl Structured Data Classification/ Regression • Customer Churn Analysis •

    Product Diagnostics • Forecasting Recommendation • Content Personalization • Product X-Sells/Up-sells Anomaly Detection • Fraud Detection • Asset Sensor Diagnostics • Log Metric Anomalies Unstructured Data Image Analytics • Identify damaged shipments • Explicit Content Classification • Identify “styles” in images Text Analytics • Call Center log analysis • Language Identification • Topic Classification • Sentiment Analysis Machine Learning - Applications
  30. @nyghtowl Breakdown and Evolve

  31. @nyghtowl Growing Use of ML at Google Number of directories

    containing model description files 2012 2013 2014 2015 1500 1000 500 0 Used across products:
  32. @nyghtowl 40% savings

  33. @nyghtowl Goal Experts Data Solution

  34. @nyghtowl Capture Process & Train Store Analyze & Deploy Build

    the Solution
  35. @nyghtowl Capture Process & Train Store Analyze & Deploy Build

    the Solution
  36. 163 Zetabytes (1 trillion gigabytes) of data by 2025

  37. Proprietary + Confidential Datacenter as a Computer

  38. @nyghtowl Capture Process Store Analyze & Deploy Capture Process &

    Train Store Build the Solution
  39. @nyghtowl Machine Learning Modeling Capture & Store Train Model Process

    Deploy & Serve Analyze & Improve
  40. @nyghtowl Make Data Coherent | Modeling

  41. @nyghtowl Neural Network = tons of multiply and add

  42. @nyghtowl Neural Network can extract hidden features from data

  43. @nyghtowl Neural Network is a function that can learn

  44. @nyghtowl 10 yrs => 1 day Simplify & Speed up

    Comprehension
  45. @nyghtowl Machine Learning Modeling Train Model Process Deploy & Serve

    Analyze & Improve Capture & Store
  46. @nyghtowl ML Data Lifecycle Solution Capture & Store Train Model

    Process Deploy & Serve Analyze & Improve Pub/ Sub Storage Cloud SQL Bigtable Dataflow Dataflow BigQuery TensorFlow Compute Engine ML Engine Datalab ML Engine Kubernetes Engine
  47. @nyghtowl End to End ML Pipeline Inputs Train model Pre

    processing Asset Creation Distributed training, Hyper-parameter tuning Deploy: Including Model Versioning REST API Prediction: load balanced, auto-scaled, scale-to-zero Model Remote Clients REST API call with input variables Asset Creation Model + Assets
  48. @nyghtowl Goal Experts Data Solution

  49. @nyghtowl Collaboration & Communication Business Engineers Researchers

  50. @nyghtowl Finding Solutions | Partners | Tools ➔ Proven experience?

    ➔ Temporary or full production? ➔ Scale & flexibility? ➔ Turnaround time? ➔ Success measure? ➔ Fit into existing systems? ➔ End to end?
  51. @nyghtowl End to End ML Pipeline Inputs Train model Pre

    processing Asset Creation Distributed training, Hyper-parameter tuning Deploy: Including Model Versioning REST API Prediction: load balanced, auto-scaled, scale-to-zero Model Remote Clients REST API call with input variables Asset Creation Model + Assets
  52. @nyghtowl Language API Vision API Translate API Speech API Remove

    Entry Barriers
  53. @nyghtowl Remote Input Predictions served, Load balanced, Auto-scaled, Scale-to-zero Pretrained

    ML Pipeline REST API Capture & Send Deploy & Serve
  54. @nyghtowl Translate Perfect translation Human Neural (GNMT) Phrase-based (PBMT) English

    > Spanish English > French English > Chinese Spanish > English French > Spanish Chinese > Spanish Translation model Translation quality old: PBMT new: GNMT
  55. @nyghtowl AutoML Vision Input Train Deploy Serve REST API Remote

    Clients AutoML Vision
  56. @nyghtowl Custom ML Modeling Collect & Store Train Model Process

    Deploy & Serve Analyze & Improve
  57. @nyghtowl Goal Experts Data Solution

  58. *Source: IDC Applied AI Focus & Simplify the Problem Utilize

    External Expertise
  59. @nyghtowl Thank you... Lindsay Cade Erin O’Connell Isabel Markl Eli

    Bixby Adam Shin Amy Unruh Sara Robinson June Andrews Tyler Benjamin Benjamin Chehebar
  60. @nyghtowl Resources • Garnter Road to Enterprise AI: https://www.gartner.com/imagesrv/media-products/pdf/rage_frameworks/rage-frameworks-1- 34JHQ0K.pdf

    • Google Cloud Platform: https://cloud.google.com/ • Launchpad Studio (AI startup incubator): https://developers.google.com/programs/launchpad/studio/ • Applied AI Examples: https://www.forbes.com/sites/robertadams/2017/01/10/10-powerful-examples-of-artificial-inte lligence-in-use-today/2/#1efe823e3c8b • TensorFlow: https://www.tensorflow.org/ • TensorFlow without a PhD: https://cloud.google.com/blog/big-data/2017/01/learn-tensorflow-and-deep-learning-without-a- phd • Deep Learning Training: www.fast.ai • End-to-end ML with TensorFlow on GCP: https://codelabs.developers.google.com/codelabs/end-to-end-ml/index.html?index=..%2F..%2Fi ndex#0
  61. @nyghtowl Image References: • iStock/diego_cervo • iStock/phive2015 • iStock/alexialex •

    iSTock/Rinelle • iStock/Noctiluxx • iStock/VladimirFloyd • istock/Besjunior • https://commons.wikimedia.org/wiki/File:Stones_go.jpg • http://www.ebss.co.jp/ebs/worldwide/service/implementation.htm • https://www.sciencedaily.com/releases/2013/05/130522085217.htm • https://images.yourstory.com/2016/09/Innovation-is-a-state-of-mind.png?auto=compress • Rosenfeld Media | https://www.flickr.com/photos/rosenfeldmedia/6949089460 | Copyright and disclaimer notice: https://creativecommons.org/licenses/by/2.0/ | License notice: https://creativecommons.org/licenses/by/2.0/legalcode • https://pixabay.com/en/backgammon-board-game-cube-strategy-1903940/ • http://alphastockimages.com/ • https://cloud.google.com/speech/ • https://dillonspace.blogspot.com/2014/08/hitting-bullseye.html • https://cdn.technologynetworks.com/tn/images/thumbnails/rectangle/cloud-based-research-informatics-improving-collabora tion-increasing-agility-and-reducing-operating-289722.png • https://www.techemergence.com/machine-learning-in-genomics-applications/ • https://www.biggerpockets.com/renewsblog/wp-content/uploads/2016/03/transcontinental-railroad.jpg • http://goo.gl/fymPMI
  62. @nyghtowl Questions? @nyghtowl gcppodcast.com