## Legal prediction

This paper outlines an attempt to build a system that can provide judgement on legal cases in case law systemss. It works by using BERT for semantive search, then combining the current case embedding with evidence. This is then weighted and used for prediction of whether it violated or not. LLM were used to summarise the articles into shorter text which seems like a weak spot in the model as the nuance in the case can be lost.

The quesetion I would have are:
- What was the accuracy achieved and the F1 score?
- What was the best top k found?
- Was any tesets conducted on this model from a different court system?

## Weather
_No summary so response written solely from slides_

Medium range forecasting is forecasting at the one to two week range. It gets much harder to predict after the one week mark and traditional methods using physics models and such break down. Graph Neural Network based methods have performance that is on par and exceeds traditional methods. The Deep learning revolution has made its way to Weather forecasting!

Some questions I would have:
- Does the MLWP models always require whole earth input and output?
- Are these NN based systems in deployment anywhere in the world?