Knowledge Vault 2/53 - ICLR 2014-2023
Emily Shuckburgh ICLR 2019 - Invited Talk - Can Machine Learning Help to Conduct a Planetary Healthcheck?
<Resume Image >

Concept Graph & Resume using Claude 3 Opus | Chat GPT4 | Gemini Adv | Llama 3:

Emily Shuckburgh
ICLR 2019
Urgency of addressing
climate change. 1
Carbon dioxide levels
have risen significantly. 2
Species at risk
of extinction. 3
Can machine learning
help understand climate change? 4
Challenges: uncertainty, interpretability,
complexity, rare events. 5
Planetary health check
using key indicators. 6
Climate indicators explorer
for policymakers and public. 7
Global temperature risen
by 1C. 8
Machine learning could
synthesize sparse data. 9
Standardized climate risk
assessment using ML. 10
ML could map
models to local weather. 11
ML can connect
emissions to future risks. 12
ML for efficient
energy and behavior solutions. 13
Blending data and
models improves projections. 14
ML cloud simulators
enhance climate models. 15
Satellite image classification
tracks ice stability. 16
Causal inference improves
climate understanding. 17
ML infers projections
for unmodeled scenarios. 18
Benchmark tasks could
drive climate science. 19
ML enables geoengineering
confidence with impact assessment. 20
Climate datasets need
cleaning for ML use. 21
Localizing projections makes
threat more tangible. 22
AI community resources
could address climate challenge. 23
Collaboration with AI
Commons to solve problems. 24
Interpretability crucial for
trustworthy ML climate models. 25
AI community implored
to focus on climate. 26
Engaging with problem
and facilitating progress. 27
Climate system understanding:
difficult and important. 28
Vast climate data
lacks actionable information tools. 30

Resume:

1.-Emily discusses the urgency of addressing climate change and its devastating impacts, such as increased hurricanes and cyclones.

2.-Despite warnings, carbon dioxide levels have risen from 378 ppm in 2005 to 415 ppm today, exacerbating climate change.

3.-A recent report states that one million species are at risk of extinction in the coming decades due to human impact.

4.-Emily asks if machine learning can help understand and reduce the impact of climate change using vast available data sets.

5.-Challenges in applying machine learning to climate data include uncertainty, interpretability, provenance, complexity, non-stationarity, and rare extreme events.

6.-A planetary health check could involve monitoring key indicators, building resilience and adaptation, and addressing climate change directly.

7.-A climate indicators explorer could deliver and visualize data on the changing climate for policymakers and the public to analyze.

8.-Global average surface temperature has risen by 1°C in the last 150 years, approaching concerning thresholds for ice sheet collapse.

9.-Constructing global temperature is complex due to sparse Arctic data; machine learning could help interpolate and synthesize diverse data sets.

10.-A standardized climate risk assessment methodology could enable evidence-based decision-making for adaptation and resilience using machine learning.

11.-Climate models struggle with robust city-level projections; machine learning could map coarse models to observed local weather conditions.

12.-Learning relationships between meteorological variables and impacts like health could connect emission scenarios to future risks using diverse data.

13.-Machine learning can help make energy systems efficient and understand human behavior to develop climate change solutions.

14.-Blending observational data and climate models could improve projections for challenging components like Arctic sea ice.

15.-Data-driven or optimized cloud process simulators using machine learning could enhance climate models' accuracy and efficiency.

16.-Satellite image classification can track icebergs to understand key glacier and ice sheet stability processes.

17.-Causal inference could elucidate physical process relationships within climate models and observations to improve understanding.

18.-Machine learning could infer climate projections for emission scenarios not explicitly modeled, aiding policy-relevant research.

19.-Emily suggests establishing benchmark tasks to drive climate science forward, similar to the ImageNet project's impact.

20.-Improved climate understanding through machine learning could facilitate confidence in geoengineering but requires careful impact assessment.

21.-While many climate datasets exist, significant cleaning and coordination are needed to make them usable for machine learning analysis.

22.-Localizing climate projections using machine learning could make the threat more tangible for the public and policymakers.

23.-Deploying intellectual resources in the AI community could make a major difference in addressing the climate challenge.

24.-Collaboration with initiatives like AI Commons could help formulate and solve critical climate change problems.

25.-Interpretability in machine learning climate models is important for trustworthiness when informing high-stakes policy decisions.

26.-Emily implores the AI community to focus on climate change as one of the most important issues of our time.

27.-The AI community can help tackle climate change by engaging with the problem and facilitating progress.

28.-Fourier, 200 years ago, considered understanding the climate system the most difficult and important question in natural philosophy.

29.-Climate change is a defining issue, and robust information is needed to assess risks and guide responses.

30.-Vast climate datasets exist, but tools to generate actionable information are lacking, presenting a data science challenge for the community.

Knowledge Vault built byDavid Vivancos 2024