Machine Learning and Decision Making for Sustainability

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Machine Learning and Decision
Making for Sustainability
Stefano Ermon
Department of Computer Science
Stanford University
April 12
Overview
Fellow, Woods Institute for the Environment
Stanford Artificial Intelligence Lab
Big Data
Computational
Sustainability
Technology
Society
Push
Pull
Sensing
revolution
Artificial Intelligence
2
ML and Decision Making for Sustainability
Vision: sustainability challenges as control problems
Algorithmic challenges and
opportunities at every step
– Data acquisition and
interpretation
– Model fitting
– Decision making and policy
optimization
Data
Models
Policy
3
Computational Sustainability
Decision making and optimization
Poverty traps
natural resources management
Poverty mapping
Water and weather
systems modeling
Optimization
of energy
systems
Large unstructured
datasets
Machine Learning
Materials discovery for energy applications
4
Summary
•
•
•
•
Introduction
Machine Learning for Public Policy
AI for Sustainable Energy
Conclusion
5
UN’s Global Goals for Sustainable Development
The 2030 Development Agenda (Transforming our world)
1. End extreme poverty
2. Fight inequality & injustice
3. Fix climate change
6
Data scarcity
• Expensive to conduct surveys
• Poor spatial and temporal resolution
• Questionable data quality
7
Satellite imagery is low-cost and globally available
Shipping records
Inventory estimates
Agricultural yield
Deforestation rate
Simultaneously becoming cheaper and higher resolution
(DigitalGlobe, Planet Labs, Skybox, etc.)
8
What if…
we could infer socioeconomic
indicators from large-scale,
remotely-sensed data?
9
Standard supervised learning won’t work
Input
Output
Model
-
Poverty, wealth,
child mortality, etc.
Lots of unlabeled data (images)
Very little labeled training data (few thousand data points)
Nontrivial for humans (hard to crowdsource labels)
11
Transfer learning overcomes data scarcity
Transfer learning: Use knowledge gained from
one task to solve a different (but related) task
Perform here
Train here
Transfer
12
Nighttime lights as proxy for economic development
13
Step 1: Predict nighttime light intensities
B. Nighttime light intensities
Deep learning
model
A. Satellite images
training
images
sampled from
these
locations
C. Poverty measures
14
Training data on the proxy task is plentiful
Labeled input/output
training pairs
(
, Low nightlight
intensity
)
…
(
,
High nightlight
intensity
)
training
images
sampled from
these
locations
Millions of training images
15
Images summarized as low-dimensional feature vectors
Inputs: daytime
satellite images
Convolutional
Neural Network
(CNN)
Outputs: Nighttime
light intensities
{Low, Medium, High}
f1
f2
…
f4096
16
Model learns relevant features automatically
f1
f10
Satellite image
Filter activation map
Overlaid image
No supervision beyond nighttime lights - no labeled
example of what a road looks like was provided!
17
Transfer Learning
Inputs: daytime
satellite images
Feature
Learning
Outputs: Nighttime
light intensities
{Low, Medium, High}
f1
f2
Nonlinear
mapping
…
Target task
Socioeconomic
outcomes
f4096
18
We can differentiate different levels of poverty
2 indicators:
• Household assets
We outperform recent methods
based on mobile call record data
Predicted ($/cap/day)
• Consumption expenditures
Observed consumption ($/cap/day)
Blumenstock et al. (2015) Predicting Poverty and Wealth
from Mobile Phone Metadata, Science
19
Models travels well across borders
Models trained in one
country perform well in
other countries
Can make predictions
in countries where no
training data exists
20
Scalable High Resolution Poverty Maps
Run the model on about 500,000 images from Uganda:
Most up-to-date map
Scalable and inexpensive approach to generate high resolution maps.
21
22
Ongoing work
• Describe, model, and predict changes over time
• Incorporate new data sources (phone data, crowdsourcing, etc.)
Credit: premise.com
• Mapping and estimating crop yields
– 1st prize at INFORMS yield prediction challenge
23
Summary
•
•
•
•
Introduction
Machine Learning for Public Policy
AI for Sustainable Energy
Conclusion
24
Computational Sustainability
Optimization
Poverty traps
natural resources management
Poverty mapping
Groundwater and
weather systems
modeling
Optimization
of energy
systems
Artificial Intelligence and
Machine Learning
Large Datasets
Energy Materials discovery
25
White House Materials Genome Initiative
Goal
Accelerate the pace and
reduce the cost of discovery,
and deployment of advanced
material systems
20 years  5 years
Very exciting new
research area for
Computer Science and
Big Data techniques
26
Vision: AI for materials research
Domain
Knowledge
High throughput
experiments
Experiment
Design
Data
analysis
Automatic Data Analysis
Stanford Linear Accelerator
Cornell High Energy Synchrotron Source
Energy Materials Center at Cornell
Caltech
27
Slide courtesy of
Apurva Mehta and Yijin Liu,
SLAC
intensit
y
monochromator
4 million XANES spectrums collected in a few
minutes with 30 nm spatial resolution.
28
Identify materials
Pattern Decomposition with Complex
Combinatorial Constraints:
Application to Materials Discovery.
[AAAI 2015]
29
Vision: AI for materials research
Improved Data Collection
Domain
Knowledge
High throughput
experiments
Experiment
Design
Data
analysis
Stanford Linear Accelerator
Cornell High Energy Synchrotron Source
Energy Materials Center at Cornell
Caltech
30
LCLS tuning at SLAC
Linac Coherent Light Source (LCLS) is the world's first X-ray laser.
10 billion times brighter than any other X-ray source before it
Very complex machine, difficult to operate, requires manual
tuning (hundreds of hours per year)
Operating cost close to $1,000 per minute – want to make
parameter tuning as robust and as quick as possible
31
Bayesian Optimization for LCLS
Archiving system: records almost 200,000 independent variables once a
second, and goes back several years
Bayesian optimization:
– Works by seeking promising points
that aren’t already explored
– Sound way to deal with the classic
exploration vs exploitation
tradeoff
Sparse Gaussian Processes for
Bayesian Optimization
[under review at UAI-16]
32
Vision: AI for materials research
Preliminary work on
dieletric screening via
quantum simulations
Domain
Knowledge
High throughput
experiments
Experiment
Design
Data
analysis
Stanford Linear Accelerator
Cornell High Energy Synchrotron Source
Energy Materials Center at Cornell
Caltech
33
Summary
•
•
•
•
Introduction
Machine Learning for Public Policy
AI for Sustainable Energy
Conclusion
34
Conclusions
• Growing concerns about the threats of Artificial
Intelligence to the future of humanity
• Recent advances in AI also create enormous
opportunities for having deeply beneficial influences
on society (energy, sustainability, …)
Sustainability
Sciences
Computational
• Exciting opportunities for Computer Science research
Sustainability
Computational Sciences
35
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