AI in my plasma physics research didn’t go the way I expected

It is similarly easy to look ahead from a pessimist standpoint (e.g. how will this bubble collapse, and who will pay the bill for the hype?). The problem rather is that this overhyped optimistic standpoint is much more encouraged from society (and of course from the marketing).

> There's also the other category who are terrified about the consequences for their lives and jobs, and who are driven in a very human way to rubbish the tech to convince themselves it's doomed to failure.

There is also a third type who are not terrified of AI, but of the bad decisions managers (will) make because of all this AI craze.

1) Instead of identifying a problem and then trying to find a solution, we start by assuming that AI will be the solution and then looking for problems to solve.

hammer in search of a nail

2) nearly complete non-publication of negative results

survivorship (and confirmation bias)

3) same people who evaluate AI models also benefit from those evaluations

power of incentives (and conflicts therein)

4) ai bandwagon effect, and fear of missing out

social-proof

I agree that this is useful! It will even take natural language and augment the script, and maybe get it right! Nice!

The AI is combing through scraped data with an LLM, and conjuring forth some imagemagick snippets into a shell script. This is very useful, and if you’re like most people, who don’t know imagemagick intimately, it’s going to save you tons of time.

Where it gets incredibly frustrating is tech leadership seeing these trivial examples, and assuming it extrapolates to general software engineering at their companies. “Oh it writes code, or makes our engineers faster, or whatever. Get the managers mandating this, now! Also, we need to get started on the layoffs. Have them stack rank their reports by who uses AI the best, so that we are ready to pull the trigger.”

But every real engineer who uses these tools on real (as in huge, poorly written) codebases, if they are being honest (they may not be, given the stack ranking), will tell you “on a good day it multiplies my productivity by, let’s say, 1.1-2x? On a bad day, I end up scrapping 10k lines of LLM code, reading some documentation on my own, and solving the problem with 5 lines of intentional code.”

Please, PLEASE pay attention to this details that I added: Huge, poorly written codebases. This is just the reality at most software companies that have graduated from series A startup. What my colleagues and I are trying to tell you, leadership, is that these “it made a script” and “it made a html form with a backend” examples ARE NOT cleanly extrapolating to the flaming dumpster fire codebases we actually work with. Sometimes the tools help! Sometimes, they don’t.

It’s as if LLM is just another tool we use sometimes.

This is why I am annoyed. It’s incredibly frustrating to be told by your boss “use tool or get fired” when that tool doesn’t always fit the task at hand. It DOES NOT mean I see zero value in LLMs.

If you're looking for breakthroughs due to AI, they're not going to be obviously attributable to AI I think. Focusing on the biology related foundation models ... The ability to more quickly search through the space of sequences->structure, drugs, and predicted cell states (1) like with the biology based foundation models will certainly lead to some things being discovered/rejected/validated faster.

I heard about this vevo company recently so it's on my mind. Biology experiments are hard and time consuming, and often hard to exactly replicate across labs. A lot of the data is of the form

a) start in cell state X (usual 'healthy' normal state) under conditions Y (environment variables like temperature, concentrations, etc.)

b) introduce some environmental pertubation P like some drug/chemical at some concentration, or maybe multiple pertubations at once

c) record trajectory or steady/final state of cell.

This data is largely hidden within published papers in non-standardized format. Vevo is attempting to collate all of those results with considerations for reproducibility into a standard easy-to-use format. The idea being that you can gradually build up a virtual sort of input-output (causal!) model that you can throw ideas for interventions against and see what it thinks would happen. Cells and biology are obviously enormously complicated so it's certainly not going to be 100% accurate/predictive, but my experience with network models in quantitative biology plus their proclaimed results make me pretty confident it's a sound approach.

Thus approach is clearly "AI" driven (maybe I would call this ML) and if their claims are anything close to reality, this is an incredibly powerful tool for all of academia and industry. You can reduce the search space enormously and target your experiments to cover the areas the virtual model doesn't seem so good, continuously improving it in a sort of crowd-sourced "active learning" manner. A continuously improving experimentally backed causal (w.r.t. perturbations) model of cells has so many applications. Again, i don't think this will directly lead to a breakthrough, but it can certainly make the breakthrough more likely and come faster.

There are many other examples like this that are some combination of 1) collating+filtering+refining existing data into an accessible easily query-able format

2) combining data + some physically motivated modeling to yield predictions where there is no data

3) targeted, informed feedback loop via experiments, simulations, or modeling to improve the whole system where it's known to be weak or where more accuracy is desired.

Assuming it all stays relatively open, that's undeniably a very powerful model for more effective science.

And that's just one approach. In physics ML can be use for finding and characterizing phase transitions, as one example. In the world of soft matter/biophysics simulation here's a few ways ML is used:

a) more efficient generation of configurations (Noe generative models). This is a big one, albeit still kind of early stages. Historically(simplifying), to generate independent samples in the right regions of phase means you need to integrate the system for long enough to hit that region multiple times. So regions of the space separated by rare transitions will take a loooong time to hit multiple times. The solution was simply longer simulations. Now, under some restrictions, you can leverage and augment existing data (including simulation data) to directly generate independent samples in the regions of interest. This is a really big deal.

b) more efficient, complex and accurate NN force-fields. Better incorporation of many-body and even quantum effects.

c) more complex simulation approaches via improved pipelines like automated parameterization and discovery of collective variables to more efficiently explore relevant configuration space.

Again, this is tooling that improves the process of discovery & investigation and thus directly contributes to science. Maybe not in the way you're picturing, but it is happening right now.

1) vevo https://www.tahoebio.ai/

But I don't think "the transformer paper was eight years ago" is strong evidence for that argument at all. First of all, the incremental improvement and commercialization and scaling that has happened in that period of time is already incredibly fast. Faraday had most of the pieces in place for electricity in the 1830s and it took half a century to scale it, including periods where the state of the art began to stagnate before hitting a new breakthrough.

I see no reason to believe it's impossible that we'll see further step-change progressions in AI. Indeed, "Attention is All You Need" itself makes me think it's more likely than not. Out of the infinite space of things to try, they found a fairly simple tweak to apply to existing techniques, and it happened to work extremely well. Certainly a lot more of the solution space has been explored now, but there's still a huge space of things that haven't been tried yet.