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A conversation about AI for science with Jason Pruet

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Re: A conversation about AI for science with Jason Pruet

#131

Post by KolibriFly »

godelski wrote: post #52
hbartab wrote: post #6

> We certainly need to partner with industry. Because they are so far ahead and are making such giant investments, that is the only possible path. And therein lies the risk: research labs may become wholly dependent on companies whose agendas are fundamentally commercial. In exchange for access to compute and frontier models, labs may cede control over data, methods, and IP—letting private firms quietly extract value…

There's a risk but there's also great reward if it is done properly. The only way to maximize utility of any individual player is to play cooperatively[0]. A single actor might get a momentary advantage by defecting from cooperation, but it decreases their total eventual rewards and frankly it quickly becomes a net negative in many cases. That said, I'm not very confident such a situation would happen in reality. I'm…

The optimist in me hopes we'll eventually reach some equilibrium where collaboration wins out

Re: A conversation about AI for science with Jason Pruet

#132

Post by KolibriFly »

zkmon wrote: post #9

I like how he says that AI is a general-purpose technology like electricity.

Whether it's hype or not, treating AI as a general-purpose tech isn't that wild when it's already touching code, writing, design, logistics, education (you name it)

Re: A conversation about AI for science with Jason Pruet

#134

Post by __loam »

KolibriFly wrote: post #128
fumeux_fume wrote: post #113

> If you’ve played with the most recent AI tools, you know: They’re very good coders, very good legal analysts, very good first drafters of writing, very good image generators. They’re only going to get better. Most of the bullshitters will tip their hand pretty early that they're just hype men for AI. Right off the bat, the fact that AI is disruptive and transforming society is apparently self-evident because they n…

The gap between demo impressive and reliably useful in real-world scenarios is still huge in a lot of cases. That said, I think the concern isn't that AI is flawless now, but that it's improving at a pace we haven't really seen with other technologies

It's really funny to see someone say this technology, which seems to be plateauing with marginal improvements after a few years, is improving at a pace we have never seen before, in an industry that is built on the microprocessor. Astoundingly ignorant.

Re: A conversation about AI for science with Jason Pruet

#136

Post by fsflover »

sampullman wrote: post #120
saretup wrote: post #117

Earlier quoted context omitted.

> "they’re very good coders" and "they make trivial mistakes or hallucinate" seems incongruous. It seems that way if you judge them the same way you would human coders, but they’re different. They might be able to do things that veteran coders can’t without spending days on it, and fail at things that beginners can do in half an hour. A car might not be able to traverse difficult terrain as well as a horse, but it do…

I'm not sure if the analogy fits. I would say AI is more like a fast car that turns the wrong way every couple of blocks, or a car that randomly refuses to go on certain roads. Is it still a useful mode of transportation? Sometimes, but, it's not reliable. The sibling comment says it better - it's about predicability of mistakes, and the effort and knowledge necessary to correct them. Maybe the next generation of mod…

> AI is more like a fast car that turns the wrong way every couple of blocks

So if you drive well and can control this "car", you are able to fix that and benefit from the fast "car" a lot.

> or a car that randomly refuses to go on certain roads

This doesn't seem like a good comparison. AI will do anything, it may just be wrong.

Re: A conversation about AI for science with Jason Pruet

#137

Post by Zorass »

Whether AI is a “good programmer” really depends on what you mean by programming. If it means being fluent in syntax, quickly generating prototypes, and recalling large amounts of code patterns, then yes, it's surprisingly strong. But if being a programmer includes debugging intuition, tracking context over multiple sessions, and knowing when not to write code, it's still not there yet.

Re: A conversation about AI for science with Jason Pruet

#138

Post by falcor84 »

th0ma5 wrote: post #111
falcor84 wrote: post #89

Earlier quoted context omitted.

How is this related to TFA which focuses on national (public) labs? In any case, if you want to take a stab at it, feel free to go ahead and start a company whereby you as the owner would assign the CEO responsibilities to an AI.

No they probably meant existing companies... when are AI companies going to get rid of their CEOs?

Well, if you were to ask me, I would say that the biggest bottle-neck is the productive time horizon for AIs, which is currently at about 1h before SoTA agents go off-rails, based on the METR report[0]. As the report models, this time horizon doubles approximately every 7 months (R² = 0.98 on a log-scale), and thus if we assume that a CEO's work requires a time horizon of e.g. 10,000 hours to effectively plan and implement a long-term strategy, then we could expect companies to be able to replace CEOs by around 2033. There are of course other factors at play, but knowing companies' propensity to move fast and break things, I would indeed put some money that by the end of 2033, we'll see at least one mid-sized tech company delegate most (if not all) of its CEO decision-making to an AI agent.

[0] https://arxiv.org/pdf/2503.14499

Re: A conversation about AI for science with Jason Pruet

#139

Post by JimDabell »

a_bonobo wrote: post #118
saretup wrote: post #117

Earlier quoted context omitted.

> "they’re very good coders" and "they make trivial mistakes or hallucinate" seems incongruous. It seems that way if you judge them the same way you would human coders, but they’re different. They might be able to do things that veteran coders can’t without spending days on it, and fail at things that beginners can do in half an hour. A car might not be able to traverse difficult terrain as well as a horse, but it do…

Yes, human coders are predictably 'bad' - it scales roughly with the complexity of the task and the years of their experience, along with some randomness ('brain farts'). LLMs are completely unpredictably 'bad' - sometimes a task that a high schooler can solve sends an LLM into a spin, sometimes an LLM solves a task that would require a PhD in the field. The unpredictability is what makes these tools so unfit for pur…

> LLMs are completely unpredictably 'bad' - sometimes a task that a high schooler can solve sends an LLM into a spin, sometimes an LLM solves a task that would require a PhD in the field.

That doesn’t mean they are unpredictably bad, it means assuming that they behave like humans is a bad way of predicting their behaviour. “Unlike humans” and “unpredictable” are not the same thing. If you spend time working with them, you get a better sense of what they are good and bad at and get better at predicting their behaviour.

Re: A conversation about AI for science with Jason Pruet

#140

Post by [deleted] »

keiferski wrote: post #119
fumeux_fume wrote: post #113

> If you’ve played with the most recent AI tools, you know: They’re very good coders, very good legal analysts, very good first drafters of writing, very good image generators. They’re only going to get better. Most of the bullshitters will tip their hand pretty early that they're just hype men for AI. Right off the bat, the fact that AI is disruptive and transforming society is apparently self-evident because they n…

I don’t understand this viewpoint at all. Even if AI tools are nothing more than complex autocomplete systems - and not some kind of new consciousness - that alone is enough to dramatically shift entire industries. And it already is doing so…this isn’t theoretical anymore.

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