3 ms·
It's not very easy to do. LLMs aren't capable of understanding, they can merely regurgitate what they've read based on statistical analysis of what words appear
by gamblor956 3y ago
It's not very easy to do. LLMs aren't capable of understanding, they can merely regurgitate what they've read based on statistical analysis of what words appear to be linked to each other. That doesn't help you when you need to do something new; at best an LLM can tell you what someone else has already done.
There are computer programs that do the kind of thing you're thinking about, for example, for protein structure analysis. They're incredibly complicated and generally require a lot of processing power.
- PaulHoule 3y agoHow about https://www.frontiersin.org/articles/10.3389/fpls.2023.1128388/full https://www.frontiersin.org/articles/10.3389/fpls.2023.11283... ? That's a simple application of machine learning algorithms you might find in scikit-learn. Here is a special issue of another alleged "predatory journal" that is full of papers on the subject https://www.mdpi.com/journal/agronomy/special_issues/E18K759IAF https://www.mdpi.com/journal/agronomy/special_issues/E18K759...
- gamblor956 3y agoLLMs are a type of machine learning. The stupidest type of machine learning. The OP did not suggest machine learning in generally, they suggested LLMs specifically, which time and time again have been shown to be incapable of this task as a matter of fundamental design. Worse, because LLMs can't understand the their training data, the output of an LLM must be verified, which in situations like this would probably take more time than simply conducting novel research in the form of random experimentation. Also, you really need to read your citations. The first one found that machine learning was unsuited for the task of agricultural prediction...