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.
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. Hearing
Deep Learning now powers real time
audio speech translation for the top
languages, allowing more humans to
connect than before.
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. Creating
Generative techniques are now
creating imagery and art with
humans, as well as simulated
environments for learning.
source: thispersondoesnotexist.com
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. Process screens across SAP,
Windows, Web, Citrix at 10+ clicks per
second
AI-powered
Robotic Process
Automation
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. 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)
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
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
[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