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Advanced AI for People in a Hurry

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Advanced AI for People in a Hurry

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Here are the backdrop slides to my recent SIFMA talk, "Advanced AI for People in a Hurry." We talk about the advent of deep learning and the rapid rise of software that can see, read, hear, speak and create... often better than humans. I end with a few examples of how people can get started today with offerings from Google.

Here are the backdrop slides to my recent SIFMA talk, "Advanced AI for People in a Hurry." We talk about the advent of deep learning and the rapid rise of software that can see, read, hear, speak and create... often better than humans. I end with a few examples of how people can get started today with offerings from Google.

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Advanced AI for People in a Hurry

  1. 1. AI… for People in a Hurry Scott Penberthy Director of Applied AI, Google
  2. 2. Agenda Examples How it works Start now 1 2 3
  3. 3. Examples
  4. 4. 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/
  5. 5. Hearing Deep Learning now powers real time audio speech translation for the top languages, allowing more humans to connect than before.
  6. 6. Speaking Deep Learning now generates realistic human speech, much how we evolved as mammals.
  7. 7. Reading Deep Learning can analyze 51 different file formats, condensing information to tensors for search, labeling and summarizing. Iron Mountain InSight™ 51 File Formats
  8. 8. Creating Generative techniques are now creating imagery and art with humans, as well as simulated environments for learning. source: thispersondoesnotexist.com
  9. 9. How it works
  10. 10. Scalar Vector Matrix Tensor
  11. 11. 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
  12. 12. Gradient Descent - find F(x)
  13. 13. “Our results are 10^5-10^6 faster with double-digit process improvement...” ...multiple projects F(x) v. f(x) Universal Approximation
  14. 14. Tensor Pods (11.5 pflops)
  15. 15. 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
  16. 16. 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)
  17. 17. Start now
  18. 18. 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
  19. 19. Process screens across SAP, Windows, Web, Citrix at 10+ clicks per second AI-powered Robotic Process Automation
  20. 20. 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 Beta
  21. 21. 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)
  22. 22. http://colab.research.google.com https://kubeflow.org Personal Supercomputers
  23. 23. Exponential growth AI Because tensors … work. Arxiv Papers 18 months Google Directories 18 months Model Computation 3.5 months
  24. 24. Q&A
  25. 25. Thank you!
  26. 26. Let’s connect: sifma-ops@google.com Learn more: https://cloud.google.com/solutions /financial-services/ Project X March, 2018

Notas del editor

  • And these boats are hard at work across the Pacific and the Atlantic oceans where we have been laying down the world's largest IP network, we have connections between every continent. (except antarctica)

    Blue is operational, Green is under construction, and you can see there is a region of world we are heavily focused on.
    Our network does not just connect our data centers to each other, like some others cloud providers, but connects us to nearly every ISP on the planet, and from a security and performance perspective this is amazing. Because we will be keeping your data on our network for high security and performance longer and closer to your customers.
    We control it entirely, end to end,
  • Limitations of Colab:

    You may hit the memory limit during training (~12 GB), will cause the runtime to start over
    May not be scalable for training jobs that take a long time
    Probably want a place to deploy your model in production after it’s been trained

  • [SARA]
    Switch to demo. Walkthrough doc is here: https://docs.google.com/document/d/1TuBlbheGuRrXqdiFvon8biFb_F9dbNO6KviX1fPAllA/edit
  • And based on benchmarks we’ve done, the results speak for themselves
    There are a number of vendors in this space, and we chose to benchmark against a subset of them with similar functionality
    Benchmarked on Kaggle competitions, which I love as a benchmark because they involve real data from a real company that is putting 10s to 100s of thousands of dollars of prize money on the line to get a good solution, and willing to wait months to get a result, and thousands of serious data scientists around the world compete
    X-axis, Y-axis
    Tables usually in the top 25% which is usually better than the existing vendors we tested. So overall, we do quite well

  • [SARA]
    Switch to demo. Walkthrough doc is here: https://docs.google.com/document/d/1TuBlbheGuRrXqdiFvon8biFb_F9dbNO6KviX1fPAllA/edit
  • And based on benchmarks we’ve done, the results speak for themselves
    There are a number of vendors in this space, and we chose to benchmark against a subset of them with similar functionality
    Benchmarked on Kaggle competitions, which I love as a benchmark because they involve real data from a real company that is putting 10s to 100s of thousands of dollars of prize money on the line to get a good solution, and willing to wait months to get a result, and thousands of serious data scientists around the world compete
    X-axis, Y-axis
    Tables usually in the top 25% which is usually better than the existing vendors we tested. So overall, we do quite well
  • Closing

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