[streetcat1]

The competition for big LLM AI companies is not other big LLM AI companies, but rather small LLM AI companies with good enough models. This is a classic innovator dilemma. For example, I can imagine a team of cardiologists creating a fine tune LLM model.

[Terr_]

What on earth would cardiologists use a Large Language Model for, except drafting fluff for journals?

Safely and effectively, that is. Dangerous and inappropriate is obviously a much wider set of possibilities.

[Leherenn]

Like some kind of linter?

The cardiologist checks the ECG, compare with the LLM results and checks the difference. If it can reduce error rate by like 10%, that's already really good.

My current stance on LLM is that it's good for stuff which is painful to generate, but easy to check (for you). It's easier/faster to read an email than to write it. If you're a domain expert, you can check the output, and so on. The danger is in using it for stuff you cannot easily check, or trusting it implicitly because it is usually working.

[SoftTalker]

> trusting it implicitly because it is usually working

I think this danger is understated. Humans are really prone to developing expectations based on past observations and then not thinking very critically about or paying attention to those things once those expectations are established. This is why "self driving" cars that work most of the time but demand that the driver remain attentive and prepared to take over are such a bad idea.

[Terr_]

> The cardiologist checks the ECG, compare with the LLM results and checks the difference.

Perhaps you're confusing the acronym LLM (Large Language Model) with ML (Machine Learning)?

Analyzing electrocardiogram waveform data using a text-predictor LLM doesn't make sense: No matter how much someone invests in tweaking it to give semi-plausible results part of the time, it's fundamentally the wrong tool/algorithm for the job.