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AI at Google (30 min) Slide 1 AI at Google (30 min) Slide 2 AI at Google (30 min) Slide 3 AI at Google (30 min) Slide 4 AI at Google (30 min) Slide 5 AI at Google (30 min) Slide 6 AI at Google (30 min) Slide 7 AI at Google (30 min) Slide 8 AI at Google (30 min) Slide 9 AI at Google (30 min) Slide 10 AI at Google (30 min) Slide 11 AI at Google (30 min) Slide 12 AI at Google (30 min) Slide 13 AI at Google (30 min) Slide 14 AI at Google (30 min) Slide 15 AI at Google (30 min) Slide 16 AI at Google (30 min) Slide 17 AI at Google (30 min) Slide 18 AI at Google (30 min) Slide 19 AI at Google (30 min) Slide 20 AI at Google (30 min) Slide 21 AI at Google (30 min) Slide 22 AI at Google (30 min) Slide 23 AI at Google (30 min) Slide 24 AI at Google (30 min) Slide 25 AI at Google (30 min) Slide 26 AI at Google (30 min) Slide 27 AI at Google (30 min) Slide 28 AI at Google (30 min) Slide 29 AI at Google (30 min) Slide 30 AI at Google (30 min) Slide 31 AI at Google (30 min) Slide 32 AI at Google (30 min) Slide 33 AI at Google (30 min) Slide 34 AI at Google (30 min) Slide 35 AI at Google (30 min) Slide 36 AI at Google (30 min) Slide 37
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AI at Google (30 min)

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Here's a high level overview of what motivates many AI teams at Google, what gives us confidence that humans will solve intelligence, the recent impact of advances in this work, and some examples of how people can get started today... for free! I first gave this talk to recipients of the 2019 AI for Good Awards, then again to recipients of the 2019 NASA FDL Challenge Fellowships. The slides are mainly a backdrop, but people still seemed to want a copy.

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AI at Google (30 min)

  1. 1. AI at Google Scott Penberthy Director of Applied AI, Google
  2. 2. Agenda Mission Examples How it works 1 2 3 4 Start Now!
  3. 3. Mission
  4. 4. Mission Solve Intelligence.1 2 Use (1) to solve everything else.
  5. 5. Approaches Hand-coded solutions Inspired by logic Learn from experience (data) Inspired by neuroscience
  6. 6. Framework
  7. 7. Inspiration https://physicsworld.com/a/quantum-microscope-peers-into-the-hydrogen-atom/
  8. 8. Confidence
  9. 9. Examples
  10. 10. Seeing Deep Learning has become a better driver than humans by literally going from pixels to steering, throttle, distance, and more. https://waymo.com/tech/
  11. 11. Hearing Deep Learning now powers real time audio speech translation for the top languages, allowing more humans to connect than before.
  12. 12. Speaking Deep Learning now generates realistic human speech, much how we evolved as mammals.
  13. 13. Reading Deep Learning can analyze 51 different file formats, condensing information to tensors for search, labeling and summarizing. Iron Mountain InSight™ 51 File Formats
  14. 14. Creating Generative techniques are now creating imagery and art with humans, as well as simulated environments for learning. source: thispersondoesnotexist.com
  15. 15. Operating Reinforcement learning techniques from AlphaGo are optimizing data centers, simulating quantum chemistry, and controlling robots. source: thispersondoesnotexist.com
  16. 16. How it works
  17. 17. Scalar Vector Matrix Tensor
  18. 18. input input 5 3 a b add mul c d add e 23 3 5 8 15 3 5 x F(x) Tensors flow through graphs
  19. 19. Gradient Descent - find F(x)
  20. 20. “Our results are 10^5-10^6 faster with double-digit process improvement...” ...multiple projects F(x) v. f(x) Universal Approximation
  21. 21. Tensor Pods (11.5 pflops)
  22. 22. Unity (US, JP) 2010 Monet (US, BR) 2017 Tannat (BR, UY, AR) 2017 Junior (Rio, Santos) 2017 FASTER (US, JP, TW) 2016 PLCN (HK, LA) 2019SJC (JP, HK, SG) 2013 Indigo (SG, ID, AU) 2019 Edge node locations >1000 Edge points of presence >100 Network Network sea cable investments The largest cloud network, comprising >100 points of presence 25% of the World’s Internet Traffic Tensor connectors - 1000x our speed
  23. 23. Impact? Product Users Data20161950 $ Cost of Prediction Source: “Managing the Machines: AI is making prediction cheap, posing new challenges for managers” by Ajay Agrawal, Joshua Gans, and Avi Goldfarb © 2016 (ajay@agrawal.ca)
  24. 24. Start now
  25. 25. Democratizing AI https://ai.google Normal humans AI Nerds ML frameworks: TensorFlow, XGBoost, Sklearn, PyTorch Cloud ML Engine: managed service for training & serving custom models RPA: Build robots for the office worker Kubeflow: deploy ML pipelines for pre-processing data, training and serving models on Kubernetes Deep Learning VM images: spin up VMs with popular ML frameworks pre-installed AutoML, BQML: train & serve no model code ML APIs: integrate AI into codebase
  26. 26. Process screens across SAP, Windows, Web, Citrix at 10+ clicks per second AI-powered Robotic Process Automation
  27. 27. Enable your entire team to automatically build and deploy state-of-the-art ML models on structured data at massively increased speed and scale. Cloud AutoML Tables
  28. 28. It’s almost not fair For each product: ● Relevant tables joined by given IDs ● Some minimal preprocessing done to match input requirements ● Run until converge ● Benchmarks run between H2 2018 to today (as they became available)
  29. 29. Learn through play https://ai.google/education
  30. 30. AIY Vision Build your own smart camera (cloud + edge)
  31. 31. AIY Voice Build your own assistant (cloud + edge)
  32. 32. ML5.js Tensorflow for poets https://www.youtube.com/watch?v=jmznx0Q1fP0
  33. 33. Text recognition Image labeling Barcode scanning Face detection Landmark recognition ML Kit Mobile SDK for vision, reading, ocr and barcodes
  34. 34. http://colab.research.google.com https://kubeflow.org AI Raspberry Kubes! Pico 3S Raspberry Pi Cluster, Coral Edge TPU
  35. 35. http://colab.research.google.com https://kubeflow.org (free) Personal Supercomputers
  36. 36. Exponential growth of AI Because tensors … work. Arxiv Papers 18 months Google Directories 18 months Model Computation 3.5 months
  37. 37. Thank you!
  • ZhengZhang78

    Feb. 23, 2021

Here's a high level overview of what motivates many AI teams at Google, what gives us confidence that humans will solve intelligence, the recent impact of advances in this work, and some examples of how people can get started today... for free! I first gave this talk to recipients of the 2019 AI for Good Awards, then again to recipients of the 2019 NASA FDL Challenge Fellowships. The slides are mainly a backdrop, but people still seemed to want a copy.

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