Invited Talks

Jeff Dean: Computation + Systems vs Climate Change

ML is the challenge for the 21st century.

Getting to zero-carbon computation for Google:

Focus for 6+ years; from 2012 to 2017 ramped up to 100% renewable energy purchase; now net-zero carbon* where the * is that it's only averaged over time.

Computation and ML to help fight climate change. Dean's general approach is Algorithm → System → Climate Application

Bayesian inference → TensorFlow Probability → Fusion energy. Examples:
- Fusion energy models at TAE where they make vibrational model for Norman machine to estimate plasma conditions w/ TF Probability
- Also: Forecasting greenhouse gas concentrations, forecasting demand for energy or electricity, predicting the occurrence and spread of forest fires, detecting deforestation and environmental change, modelling salinity in estuaries and coral reefs
Simulation → TPUs → Flood forecasting
- Google has hydraulic model maps that are 90x the resolution of public maps, but that'd be 700,000x as much compute to simulate; TPUs can do these computations efficiently enough to make them realistic.
- Google has alerted 2B people over 100k's of events (including floods) through Maps and Android.
(Simulation → Neural Network Proxies) → TensorFlow → Weather Prediction
- Current weather models use partial differential equations (PDEs) that require a large amount of computation especially as compute increases when you make your grid finer; EU weather just switched to 9km grid, while 1km grid is desired for cloud simulation.
- Or use a neural network directly from sensor predictions, which is more efficient and makes a more fine-grained (in time and space) prediction at the short term (< 12 hours)

Technology + People—leverage: What information do people need to make good decisions? What are the information leverage points? Who should we build tools for?

Environmental insights explorer: estimating building emissions, transport emissions, rooftop solar potential (e.g. Project Sunroof)
Challenge to community: building climate decision-making tools using AI/ML for all the high leverage points.

Questions:

Why not put servers in places where all electricity is renewable (e.g. Quebec with hydro)? → This is a big factor in where Google decides to build a new datacenter.