agronomicon

> I find 77,000 or so articles on "rice" in PubAg

Analyzing this data set with an LLM would be a very good research project.

AI is simply about finding correlations in large data sets. Computers don't understand anything, they just shuffle symbols. So training an LLM on agricultural research will likely uncover patterns that would not be obvious to people and these patterns could point to new techniques and processes for increasing yields like scheduled flooding (as explained in the article). LLMs don't understand code but they consistently can complete code fragments which end up being correct more often than not. A model for yield optimization doesn't have to understand farming to suggest techniques and processes for increasing yields just like LLMs do for code fragments.

Understanding is not necessary for uncovering statistical correlations.

That's exactly the type of data set that can be analyzed by large neural networks. Heterogeneous data with hidden and non-obvious statistical correlations which would be hard to uncover with classical statistical tools and techniques.

I just mean finding correlations in data sets that are hard to find in other ways. The main idea is that there are plenty of data sets on various cultivars and experiments for how to increase yields. There are probably patterns in the data that would be amenable to analysis by neural networks. The article gives an example for how scheduled flooding can increase yields and I bet there are a lot of low hanging fruits like that to pick. This doesn't require discovering anything novel but simply surfacing some patterns in the data that is buried across several papers and hard to uncover by classical meta-analysis and statistical techniques. Neural networks are very good for uncovering non-obvious statistical correlations which can then be verified by experimentation.

After reading the article I'm sure there are plenty of low hanging fruits to uncover in yield optimization by trying different schedules for flooding and soil enrichment with different kinds of fertilizers. A neural network doesn't have to understand anything to point out useful statistical correlations just like it doesn't have to understand code semantics for incomplete code fragments to suggest potential completions which are then verified by the programmer/compiler/type system.