Challenges and Opportunities

Paper

Difficulties

• Post-processing of data
  • Costs for specific scenarios and analysis (ex. outliers in rare events)
  • Under-utilization of existing data since it is expensive to process
• Data quality and quantity

Opportunities

• Multimodal models: radar, satellite, numerical weather prediction, etc.
• Interpretable models / explainable AI / causal AI
• Generizable models
  • can the model predict out of scope?
  • can the model avoid bias and flaws in the training data?
• Continuous learning: can the model learn from new data?
• On-device adaptation: customize a model based on local data (ex. adjust to local climate)
• Federated Learning: each company trains their own model, but they can share their models to improve the overall model. Global model learns from updates from local models.