Knowledge Vault 2/53 - ICLR 2014-2023
Emily Shuckburgh ICLR 2019 - Invited Talk - Can Machine Learning Help to Conduct a Planetary Healthcheck?
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Concept Graph & Resume using Claude 3 Opus | Chat GPT4 | Gemini Adv | Llama 3:

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ICLR 2019] --> B[Urgency of addressing
climate change. 1] B --> C[Carbon dioxide levels
have risen significantly. 2] B --> D[Species at risk
of extinction. 3] A --> E[Can machine learning
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complexity, rare events. 5] E --> G[Planetary health check
using key indicators. 6] E --> H[Climate indicators explorer
for policymakers and public. 7] E --> I[Global temperature risen
by 1C. 8] I --> J[Machine learning could
synthesize sparse data. 9] E --> K[Standardized climate risk
assessment using ML. 10] K --> L[ML could map
models to local weather. 11] K --> M[ML can connect
emissions to future risks. 12] K --> N[ML for efficient
energy and behavior solutions. 13] E --> O[Blending data and
models improves projections. 14] O --> P[ML cloud simulators
enhance climate models. 15] O --> Q[Satellite image classification
tracks ice stability. 16] O --> R[Causal inference improves
climate understanding. 17] O --> S[ML infers projections
for unmodeled scenarios. 18] A --> T[Benchmark tasks could
drive climate science. 19] T --> U[ML enables geoengineering
confidence with impact assessment. 20] T --> V[Climate datasets need
cleaning for ML use. 21] T --> W[Localizing projections makes
threat more tangible. 22] A --> X[AI community resources
could address climate challenge. 23] X --> Y[Collaboration with AI
Commons to solve problems. 24] X --> Z[Interpretability crucial for
trustworthy ML climate models. 25] A --> AA[AI community implored
to focus on climate. 26] AA --> AB[Engaging with problem
and facilitating progress. 27] A --> AC[Climate system understanding:
difficult and important. 28] AC --> AD[Robust information needed
to assess risks, guide responses. 29] AC --> AE[Vast climate data
lacks actionable information tools. 30] class B,C,D,AC,AD,AE climate; class E,F,I,J,K,L,M,N,O,P,Q,R,S,T,U,V,W,Z machinelearning; class G,H challenges; class X,Y,AA,AB community;

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 1C 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.

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