Exactly, and that uncovers the folly in this: what are you trying to do. Often enough it's very specific, and requires actually going out into the world. Sometimes the literature is already there. But nearly always it requires some form of analytical thought. "I want X because I need to prove Y". There is no AI magic sauce around that. It means that the people interacting with the "user friendly" interface need to know, and need to know how to get there somehow. That's rarely the case. And from a product perspective it makes things very hard: you get bombarded with very specific requests that require time and effort to build UIs and models for, that at the end the of the day only solve that specific question. Instead, it would be better to equip people with the tools needed to solve the meta-question so to speak, but that meta question is much much harder (at least, I've been trying but no success yet). As a funny anecdote: my medical knowledge had exploded. But I see that as a symptom of the broader problem: I know how to do /some/ things because I've gained that domain knowledge, but I can't teach the system yet to solve the "general" problem because a) I don't know what it is b) there is no data to get there c) what even is the UI for that.

Is this a fair way to express your problem?

You're being asked to use AI to replace an entire process all at once, rather than to assist with or replace a step in that process.

Instead of being asked "how do I solve this problem" it would be nice if you were asked "can you replace this thing I have to do all the time?"

Maybe this doesn't apply to your work, but if I were in your position I'd talk to the human that does the human version of the process and break down everything they do to get from A to Z into steps. If you can start by just replacing or augmenting a single step, you're adding value right away and you've also built a part of your eventual A to Z machine, right? For example, your search engine idea is a good direction. Can you automate the process of finding relevant articles? That would help the humans but would also help whatever AI you're working up to, right? Then maybe further in there's some other process involving physics modeling or something that is clear enough in scope to do programmatically.

I do much simpler work, but I find it's much easier to automate processes one step at a time rather than all at once.

It almost sounds like the real solution is teaching the users how to program. Possible with a suitable abstraction to make it easier for them.

Well, that's because physics has a model. Biology has nearly no model at all, so people first look for features at the data, and then verify those empirically.

It' actually the obvious thing to do.

As someone involved in the field, I agree on the need for a better theory, but disagree with your claim that "there's no getting away from the wet lab". The traditional wet lab absolutely has to be gotten away from, because there are huge issues with transferring knowledge gained on organism X onto organism Y.

Reason? Lack of theory. We don't understand enough about what causes something to work in one case, but not another.

There's just no getting away from theory, in the end.

Perfect answer. This is exactly the case, from Swanson's seminal text mining in biology paper on fish oil in the 80's with Arrow, to modern tools like Leach's Hanalyzer: You need to focus on a very narrow target application or have a clear hypothesis you wish to prove. And, wetlab work, human intervention, and tons of labor are major ingredients of those (rare) examples that go beyond information retrieval or very targeted information extraction. (Also note how none of these guys was able to scale that work and produce a whole chain of findings... ;-))

Do you know of any good resources to start automating some wet lab experiments, such as culturing, pipetting, etc, in a cost effective manner?