Jyunmi Hatcher

If you pick the wrong chatbot, you got nothing.

The researchers were very clear.

AI still produces misleading results.

It still requires human oversight and is not a replacement for scientific expertise.

Someone still had to design the experiment, write the right prompts, and critically evaluate what comes back.

But what AI did do is eliminate the most tedious, time-consuming part of the process, writing and debugging the analysis code.

And

if you've been paying attention to any of the AI in science stories that I have been talking about over the last few months, they all kind of have the same general framework.

AI does the tedious, boring science part of everything.

Okay.

And this is, this is where it lives, at least for right now, until we eventually get to a point where we'll get novel, truly novel ideas.

Um,

Everybody in chat, give me your guess until we get truly novel science discoveries from AI.

Now, like I said, it freed the researchers to focus on asking the right questions instead of fighting with the software.

Marina Sirota, who led the study at UCSF, put it this way.

These AI tools could relieve one of the biggest bottlenecks in data science, building the analysis pipelines.

And she said the speed up couldn't come sooner for patients who need help now.

And I think that's the other factor is speed and time in data.

that it takes for the boring or mundane or tedious parts of things is the largest amount of time through the process.

Combine that with rigorous validation steps.