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Pushing the boundaries of MLA case of Simplification & Scale
Magnus Hyttsten
The AI SummitSan Francisco
Guinea Pig
Meet Robin
“The network performed similarly to senior orthopedic surgeons when presented with images at the same resolution as the network.”
www.tandfonline.com/doi/full/10.1080/17453674.2017.1344459
RadiologyOphthalmology
0.95Algorithm Ophthalmologist (median)
0.91
Pathology
neural (GNMT)
phrase-based (PBMT)
English >
Spanish
English >
French
English >
Chinese
Spanish >
English
French >
English
Chinese >
English
Translation model
Tran
slat
ion
qual
ity
0
1
2
3
4
5
6
human
perfect translation
Closes gap between old system and human-quality translation from 58% to 87%
Enables better communication across the world
Neural Machine Translation
Robotics - Discovery Algorithms
ImageNet
Alaskan Malamute Siberian Husky
https://cloud.google.com/blog/big-data/2016/08/how-a-japanese-cucumber-farmer-is-using-deep-learning-and-tensorflow
Cucumber sorter
From: http://workpiles.com/2016/02/tensorflow-cnn-cucumber/
Data, Data, Data
Compute, Compute, Compute
Data, Data, Data
Compute, Compute, Compute
Humans, Humans, Humans
Improving Inception and Image Classification in TensorFlowresearch.googleblog.com/2016/08/improving-inception-and-image.html
How long did it take for a Human to construct this?
AM!!!
Current:Solution = ML expertise + data + computation
Current:Solution = ML expertise + data + computation
Can we turn this into:Solution = data + 100X computation
Current:Solution = ML expertise + data + computation
Can we turn this into:Solution = data + 100X computation
???Can We Learn How To Teach Machines To Learn
CIFAR-10
Learning Transferable Architectures for Scalable Image Recognition, Barret Zoph, Vijay Vasudevan, Jonathon Shlens and Quoc Le, https://arxiv.org/abs/1707.07012
ImageNet
Large-Scale Evolution of Image ClassifiersEsteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc Le, Alex Kurakinhttps://arxiv.org/abs/1703.01041
Using RL to Distribute Training to
GPUs
Device Placement Optimization with Reinforcement Learning, Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi,Samy Bengio, Jeff Deanhttps://arxiv.org/abs/1706.04972
Using RL to Distribute Training to
GPUs
Growing use of deep learning @ Google
Making this Happen
A Case of Simplification
● An open-source machine learning framework for everyone
● Fast, flexible, and production-ready on all major platforms
● Able to scale from research to production
TensorFlow is Strategic to Google
TensorFlow Distributed Execution Engine
CPU GPU Android iOS ...
C++ FrontendPython Frontend ...
Estimator
tf.layers
tf.keras
Canned Estimators
Data
sets
Demo
Cloud Video Intelligence(animated .gif here)
Making this Happen
A Case of Scale
Scaling to multiple platforms
iOS
CPU GPU
Android
Cloud TPU
Embedded Devices
Scaling Training Speed and
CPU GPU TPU
Tensor Processing Unit (TPU) v2
Tensor Processing Unit (aka TPU) v2Designed for neural net training and inference
● 180 teraflops of computation, 64 GB of HBM memory, 2400 GB/s memory bandwidth
● Designed to be connected together
TPU Pod - 11.5 petaflops
Pushing the boundaries of ML
● Machine Learning & Artificial IntelligenceProven & Ready for Business
A case of simplification & scale
Pushing the boundaries of ML
● Machine Learning & Artificial IntelligenceProven & Ready for Business
● But Designing & Training ModelsTakes Time & Is Complex
A case of simplification & scale
Pushing the boundaries of ML
● Machine Learning & Artificial IntelligenceProven & Ready for Business
● But Designing & Training ModelsTakes Time & Is Complex
● TensorFlow, TPUs, and Model AutomationUsed by Google to push the boundaries of ML
A case of simplification & scale
