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

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

#271

Post by overfeed »

ktallett wrote: post #57
raesene9 wrote: post #10

Interesting article. There is always risk that a new hot technique will get more attention that it ultimately warrants. For me the key quote in the article is "Most scientists aren’t trying to mislead anyone, but because they face strong incentives to present favorable results, there’s still a risk that you’ll be misled." Understanding people's incentives is often very useful when you're looking at what they're sayin…

There are those who have realised they can make a lot of cash from it and also get funding by using the term AI. But at the end of the day what software doesn't have some machine learning built in. It's nothing new, nor is the current implementations particularly extraordinary or accurate.

AI is a fuzzy term, and a moving target. Expert Systems have zero ML and were considered cutting-edge AI once upon a time.

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

#272

Post by strogonoff »

Closi wrote: post #169
roenxi wrote: post #138

Earlier quoted context omitted.

Also, the strong predictions about AI are using a vague term because the tech often doesn't exist yet. There isn't a chatbot right now that I feel confident can out-perform me at systems design but I'm pretty certain something that can is coming. Odds are also good that in 2-4 years there will be new hotness to replace LLMs that are much more functional (maybe MLLMs, maybe called something else). We can start to pred…

Totally agree. Also the term 'LLM' is more about the mechanics of the thing than what the user gets. LLM is the technology, but some sort of automated artificial intelligence is what people are generally buying. As an example, when people use ChatGPT and get an image back, most don't think "oh, so the LLM called out to a diffusion API?" - they just think "oh chat GPT can give me an image if I give it a prompt". Altho…

> As an example, when people use ChatGPT and get an image back, most don't think "oh, so the LLM called out to a diffusion API?" - they just think "oh chat GPT can give me an image if I give it a prompt".

Note: your first part skipped entirely the process of obtaining the data for and training of both of the above, which is a crucial part at least on par with what called which API.

I don’t think it’s unreasonable to expect people to build an intuition for it, though. It’s healthy when underlying processes are understood to at least a few layers of abstraction, especially in potentially problematic or morally grey areas.

As an analogy to your example, you could say that when people drink milk they usually don’t think “oh, so this cow was forced to reproduce 123 times with all her children taken away and murdered so that it makes more milk” and simply think “the cow gave this milk”.

However, like with milk, like with the ML tech, it is important to realize that 1) people do indeed learn the former and build the relevant intuitions, and 2) the industry is reliant on information asymmetry and mass ignorance of these matters (and we all know that information asymmetry is the #1 enemy of free market working as designed).

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

#273

Post by rhubarbtree »

const_cast wrote: post #266
rhubarbtree wrote: post #5

I think this is mostly just a repeat of the problems of academia - no longer truth-seeking, instead focused on citations and careerism. AI is just a.n.other topic where that is happening.

To be fair, the problems of careerism is really a side-effect of academia becoming more enthralled with the private sector, and therefore inheriting it's problems. If there's one thing working as a software dev has taught me, it's that all decisions are made from a careerist, selfish perspective. Nobody cares what's best, they care what's most impressive and what will personally get them ahead. After it's done, it's…

Well, the good news - and I think from the sound of your post you will take it as good news, because you care - is that you are not correct.

Some careers are vocations, and in vocations people work less for egoist reasons and more from the desire to help people. Fortunately in the UK, we still have a very strong example of a vocation - nursing. I know many nurses, none of them can be described as careerist or selfish. So to begin, we know that your statement doesn’t hold true. Nurses’ pay is appalling and career paths are limited, so I’m confident that these many datapoints generalise.

The obvious next question is why academia is not a vocation. You say it’s because it has become too like the private sector. Well, I can tell you that is also wrong, as I have spent many years in both sectors, and the private sector is much less selfish and careerist. This is surprising at first, but I think it’s about incentives.

In the private sector very few people are in direct competition with each other, and it is rarely a zero sum game. The extreme of this is startups, where founders will go to great lengths to help each other. Probably the only area their interests are not aligned is in recruitment, but it is so rare for them to be recruiting the same type of person at exactly the same time that this isn’t really an issue. There are direct competitors of course, but that situation is so exceptional as to be easily ignored.

In academia, however, the incentives encourage selfishness, competition, obstruction, and most of all vicious politics. Academics are not paid well, and mostly compete for egoist rewards such as professorships. I believe in the past this was always somewhat a problem, but it has been exacerbated by multiple factors: (a) very intelligent people mostly left, because more money could be made in finance and tech, and thus little progress can be made and there is no status resulting from genuine science, (b) governments have used research assessment exercises, nonsense bureaucracy invented by fools that encourages silly gaming of stats rather than doing real work, (c) a system of reinforcement where selfish egotists rise at the expense of real scientists, and then - consciously or not - reinforce the system they gamed, thinking it helped them up the ladder and thus must be a good system. The bad drive out the good.

Ultimately the problem is academia is now filled with politicians pretending to be scientists, and such institutional failure is I think a one way street. The only way to fix it is to create new institutions and protect them from infiltration by today’s “scientists”.

This is of course a generalisation, and there are some good eggs left, just not many. Most of them eventually realise they’re surrounded by egoist politicians and eventually leave.

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

#274

Post by elcritch »

dekhn wrote: post #262
elcritch wrote: post #259

Earlier quoted context omitted.

> What "precise and accurate theory for protein folding" exists? It’s called Quantum Mechanics. > Nobody has been able to demonstrate convincingly that any simulation or theory method can reliably predict the folding trajectory of anything but the simplest peptides. No we don’t have simplified models or specialized theories to reduce the computational complexity enough to efficiently solve the QM or even molecular dy…

I don't think anybody is 100% certain that doing a full quantum simulation of a protein (in a box of water) would recapitulate the dynamics of protein folding. It seems like a totally reasonable claim, but one that could not really be evaluated. If you have a paper that makes a strong argument around this claim, I'd love to see it. BTW- regarding folding funnels, I learned protein folding from Ken Dill as a grad stud…

> I don't think anybody is 100% certain that doing a full quantum simulation of a protein (in a box of water) would recapitulate the dynamics of protein folding.

True, until it's experimentally shown there's still some possibility QM wouldn't suffice. Though I've not read anything that'd give reason to believe QM couldn't capture the dynamic behavior of folding, unlike the uncertainty around dark matter or quantum supremacy or quantum gravity.

Though it might be practically impossible to setup a simulation using QM which could faithfully capture true protein folding. That seems more likely.

> It seems like a totally reasonable claim, but one that could not really be evaluated.

If quantum supremeacy holds, my hunch would be that it would be feasible to evaluate it one day.

The paper I linked was mostly to showcase that there seem to be approaches utilizing quantum computing to speed up solving QM simulations. We're still in the early days of quantum computing algorithms and it's unclear what's possible yet. Tackling a dynamic system like a unfolded protein folding is certainly a ways away though!

> Also the article you linked- they are trying to find the optimal structure (called fold by some in the field). That's not protein folding- it's ground state de novo structure prediction.

Thanks! I haven't worked on quantum chemistry for many years, and only tangentially on protein folding, so useful to know the terminology. The meta table states and that whole possibility of folding states / pathways / etc fascinates me as potentially being emergent property of protein folding physics and biology as we know it.

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

#275

Post by shantnutiwari »

Could it be we are all scared, because if we call the Emperor naked, and 15 years from now someone finds a useful case for AI(even if its completely different to what exists today), everyone will point to our post and say "Hahaha look at those Luddites, didnt even believe AI was real LOL"

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

#276

Post by Flamentono2 »

nodar86 wrote: post #244
Flamentono2 wrote: post #139

Earlier quoted context omitted.

Crypto is not making people rich, it is about moving money from Person A to Person B. And sure everyone who got the money from others by gambling are biased. Fine with me. But in comparision to crypto, people around me actually use AI/ML (most of them).

Every activity that is making people rich is by definiton moving money from Person A to Person B.

Lets be nitpicky :)

I said move money from A to B, which implies that nothing else is happening. Otherwise it would be an exchange.

Sooo i would say my wording was right?! :)

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

#277

Post by StopDisinfo910 »

apwell23 wrote: post #104
StopDisinfo910 wrote: post #39

Earlier quoted context omitted.

I don’t remember when NFTs and cryptos helped me draft an email, wrote my meetings minutes for me or allowed me to easily search information previously locked in various documents. I think there is this weird take amongst some on HN where LLMs are either completely revolutionary and making break through or utterly useless. The truth is that they are useful already as a productivity tool.

> wrote my meetings minutes why is this such a posterchild for llms. everyone always leads with this. how boring are these meetings and do ppl actually review these notes? i never ever saw anyone reading meeting minutes or even mention them. Why is this usecase even mentioned in LLM ads.

Because meeting minutes are hard, annoying to do and LLMs are good at it.

To be blunt, I think most HNers are young software developers, never attend any meetings of significance and don’t have to deal with many différent topics so they fail to see the usefulness because they are not in a position to understand it.

The tails are everywhere like people mentioning work orders which is something extremely operational. If nothing messy and complicated is discussed in the meetings you attend, it’s no surprise you don’t get why minutes are useful or where the value is. It doesn’t mean there is no value.

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

#278

Post by StopDisinfo910 »

ktallett wrote: post #123
StopDisinfo910 wrote: post #118

Earlier quoted context omitted.

> valid alternatives that really take no longer time wise That’s not my experience. We use them more and more at my job. It was already great for most office tasks including brainstorming simple things but now suppliers are starting to sell us agents which pretty much just work and honestly there are a ton of things for which LLMs seem really suited for. CMDB queries? Annoying SAP requests for which you have to delve…

How do you check accuracy of these? You stated brainstorming as an example that they are great at. As obviously experts are experts for a reason. My issue here is that a lot of this is solved by good practice, for example,travel management and expenses have been solved, company credit card. I don't need one slightly better piece of software to manage one terrible piece of software to solve an issue that has a solutio…

> How do you check accuracy of these?

Because LLMs send you back links to the tools and you still get the usual confirmation process when you do things.

The main issue never was knowing what to do but actually getting the tools to do it. LLMs are extremely good at turning messy stuff into tools manipulation especially where there never was an API available in the first place.

It’s not a question of practices. Anyone who has ever worked for a very large company knows that systems are complicated by need and everything move at the speed of a freighter ship if you want to make significant changes.

Of course we need one slightly better piece of software to manage terrible pieces of software. There are insane value there. This is a major issue for most companies. I have seen millions spent into getting better dashboards from SAP which paid for themselves in actual savings.

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

#279

Post by Ukv »

shantnutiwari wrote: post #275

Could it be we are all scared, because if we call the Emperor naked, and 15 years from now someone finds a useful case for AI(even if its completely different to what exists today), everyone will point to our post and say "Hahaha look at those Luddites, didnt even believe AI was real LOL"

The recent high level of funding (Stargate, HUMAIN, ...), seemingly prompted mostly by LLMs, could plausibly be an emperor's new clothes fear of missing out among investors - will have to wait and see how it pans out.

But for 2010s-era machine learning this article is talking about, I feel it largely already has been validated - from shunned and unfunded at the start of the decade to being the almost universal go-to for any NLP or computer vision task by the end. The article itself lists a few use-cases (protein folding, weather forecasting, drug discovery), and I think it's unlikely you've gone through the day without encountering at least a few more (maybe search engines query-understanding, language translation, generated video captions, OCR, or using a product that was scanned for defects).

Not that every ML method will work out first try when applied to a new problem, but it's far from the case that we're waiting 15 years hoping for someone to maybe find a use-case for the field.

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

#280

Post by BlueTemplar »

hyttioaoa wrote: post #21
omneity wrote: post #15

The article initially appears to suggest that all AI in science (or at least the author’s field) is hype. But their gripe seems to be specific to an architecture named PINN that seems to be overhyped, as they mention in the end how they end up using other DL models to successfully compute PDEs faster than traditional numerical methods.

He published a whole paper providing a systematic analysis of a wide range of models. There's a whole section on that. So it's not specific to PINN.

The use of the term «AI» is, yet again, annoying by its vagueness.

I'm assuming that they do not refer to the general use of machines to solve differential equations (whether exactly or approximately), which is centuries old (Babbage's engine).

But then how restricted these «Physics-Informed Neural Networks» are ? Are there other methods using Neural Networks to solve differential equations ?

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