Comment by plaidfuji | Hacker News Reader

Comment by plaidfuji | Hacker News Reader

> Rodriques: Many people assume we’re focused on wet lab automation. There are certainly opportunities there and we are exploring them, but the biggest opportunities are actually on the cognitive side.

Wet lab automation is very difficult and capital intensive. And once you build your lab you are constraining yourself to answering questions within a certain domain for which you have the relevant sample prep and characterization equipment. Your equipment in essence defines your design space, and thus your potential solution space, which places a bound on your TAM.

So of course automating the thinking part of science is more approachable with current AI - but is that what people want? It’s certainly an attractive proposition for management: automate away the highly-paid SMEs and turn R&D into more of a factory environment with replaceable lab techs.. but actually implementing this depends on where the power lies in an org. My theory is that in many orgs the “cognitive” folks hold the true power (= the unwritten expertise about what works and what doesn’t, when to work around your existing setup, how much to trust each number an instrument produces). They’ll resist this change to their last breath.

You may gain some short-term efficiency by accelerating the experiments of today, but in the long run you lose the expertise to break out of local minima imposed by your equipment and training data.

Or to think about this another way, imagine a PhD student who was never allowed to talk to other people, attend conferences etc., and could only read papers and try things in lab. But they can read papers extremely fast. Would they be successful?

>Or to think about this another way, imagine a PhD student who was never allowed to talk to other people, attend conferences etc., and could only read papers and try things in lab. But they can read papers extremely fast. Would they be successful?

For anyone who thinks this is sufficient, be aware that papers only tell you what's successful (for some definition of successful). Being part of a research community gives you access to the other side: hallway conversations at conferences and informal collaborative networks are far more candid, where people will tell you what they've tried and didn't work, or what resources they need for an ambitious study that's just out of reach for their current budget. This is also where a lot of new ideas and collaborations come from, where people come together with matching problems and solutions to new interesting questions.

I'm not sure how an AI is supposed to help with this, as research is ultimately a very social activity from my perspective.

This comment made me realize something. There’s always been grumbling in the academic community about how negative or less exciting results aren’t publishable. As a result, there is quite a bit of knowledge that is essentially “lost” as no one ever bothers to write it down. Part of the reason this happens is that publishing unexciting results is unhelpful professionally, but I suspect another aspect here is that researchers know that no one would read such results; it is hard enough to keep up with the positive results in the field, much less negative results. So, in that sense they are not “contributing to the sum total of human knowledge”, which I think is a major part of many scientists’ motivations.

So then, in a world where the outcomes of all experiments could be reasonably fed into an AI model, it seems that there could be a great deal of value in having scientists publish these “low value” negative results, even in a relatively informal format (i.e. not worrying about formatting, perhaps skipping peer review, etc.). That way even if a human never reads the paper, at the very least an AI “scientist” model would pick it up.

I don’t think you can professionally incentivize this. Rather, scientists would need to do this out of the desire to contribute to the sum total of human knowledge, which could be embodied and not forgotten by these scientist AI’s.

There seems to be a couple of field-specific journals of negative results for similar purposes. It seems like there should be value in citing negative results to inform current research. Perhaps if there were more journals dedicated to this, or a single one not limited to specific fields, there would still be some incentive to publish there, if the effort required was low enough (another area where AI might be applied: writing it up).

Where the publishing of negative results is needed (and generally wanted) is in the paper of the experiments that did work. The problem is that paper is missing all the failures along the way. It is totally fine if this is all thrown in the appendix but the problem becomes that we have to write things quickly and including negative results only increases the likelihood of your work getting rejected because reviewers see their role as antagonistically, to find errors. The problem really comes down to not letting people admit mistakes. Good work can be done even while many mistakes exist. But a work is better when the mistakes are easier to find. Short term vs long term reward. Because in the long term someone needs to replicate your experiments, even in some minor way just to show that their thing is better than yours. Having information about what didn't work is helpful for learning new paths that will work.

I don't think many scientists really want to publish papers like "We did X, X had no results", but rather "We did X! X is awesome! But along the way we tried Y, Z, ... which were not so successful. We think Y because... we have no fucking clue about Z." You're still pushing the exciting stuff. You're just giving it more context.

> imagine a PhD student who was never allowed to talk to other people, attend conferences etc., and could only read papers and try things in lab. But they can read papers extremely fast. Would they be successful?

A lot of scientific knowledge cannot be communicated through papers. This is especially true in wet labs, where there's no procedural standardization. Keoni Gandall wrote an excellent post on this topic as it applies to synthetic bio [0]. I've experienced this first hand as a student participating in chemistry labs. Even when you're given a step by step procedure, it's impossible to predict the spacial logistics and inefficiencies you'll run into when you actually try to execute the procedure, regardless of your analytical preparation.

The other type of knowledge that is rarely communicated through papers is the informal exploratory thought process of the researcher, and their embarrassing failures/mistakes.

If I have my own lab someday, I think it would be cool if everyone wore bodycams, showing their first person view. By publishing the raw footage with the paper/code, hopefully this would help with reproducibility.

[0] https://keonigandall.com/posts/too_much_tacit.html

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