Deep learning gets the glory, deep fact checking gets ignored

Honestly, if your accuracy/performance metrics are too good, that's almost a sure sign that something has gone wrong.

Source: bitter, bitter experience. I once predicted the placebo effect perfectly using a random forest (just got lucky with the train/test split). Although I'd left academia at that point, I often wonder if I'd have dug in deeper if I'd needed a high impact paper to keep my job.

Is it, even when applied to trivial classifiers (possibly "classical" ones)?

I feel that we're wrong to be focusing so much on the conversational/inference aspect of LLMs. The way I see it, the true "magic" hides in the model itself. It's effectively a computational representation of understanding. I feel there's a lot of unrealized value hidden in the structure of the latent space itself. We need to spend more time studying it, make more diverse and hands-on tools to explore it, and mine it for all kinds of insights.

oh yes i recently become a transformer too

its the em dashes?

Yes, but it's using the understanding they acquired to guide a more reliable tool, instead of also making them generate the final answer, which they're likely to hallucinate in this problem space.

a lot of applications auto convert -- to an em dash

and a bunch of phone/tablet keyboards do so, too

I like em dashes I had considered installing a plugin to reliably turn -- into em dash in the past, if I hadn't discarded that idea you would have seen some in this post ;)

And I think I have seen at lest one spell checking browser plugin which does stuff like that.

Oh and some people use 3rd party interfaces to interact with HN, such which do auto convert consecutive dashes to em dashes.

In the places where I have been using AI from time to time it's also not supper common to use em dashes.

So IMHO "em dash" isn't a tall tell sign for something being AI written.

But then wrt. the OP comment I think you might be right anyway. It's writing style is ... strange. Like taking a writing style from a novel and not any writing style but such which over exaggerates that currently a story is told inside a story. But then fills semantics of a HN comment. Like what you might get if you ask a LLM to "tell a story" for you set of bullet points.

But this opens a question, if the story still comes from a human isn't it fine? Or is it offensive that they didn't just give us compact bullet points?

Putten that aside, there is always the option that the author is just very well read/written, maybe a book author, maybe a hobby author and picked up such a writing style.

A lot of phones do this automatically when doing double dash -- -> —

The Android client I use, Harmonic, has a shortcut to report a user, although it just prefills an email to hn@ycombinator.com.

option-shift-minus on a Mac (option-minus for an en dash).

I’m not sure anyone I know could make an em dash with their keyboard off the top of their head.

I have endash bound to ⇧⌥⌘0, and emdash bound to ⇧⌥⌘=.

Not refuse, but remark that none of the answers seems correct. After all, we are only 2 days away from an AGI pro gamer researcher AI according to experts[1], so I would expect this behavior at least.

1: People who have a financial stake in the AI hype

In multiple choice if you don't know then a random guess is your best answer in most cases. In a few tests blank is scored better than wrong but that is rare and the professors will tell you.

as such I would expect students to but in something. However after class they would talk about how bad they think they did because they are all self aware enough to know where they guessed.

I would love to see someone try this. I would guess 85-90% of undergrads would fill out the whole test, but not everyone. There are some people who believe the things that they know.

I think the issue is the confidence with which it lies to you.

A good analogy would be if someone claimed to be a doctor and when I asked if I should eat lead or tin for my health they said “Tin because it’s good for your complexion”.

Yes, it should refuse.

Humans have made progress by admitting when they don’t know something.

Believing an LLM should be exempt from this boundary of “responsible knowledge” is an untenable path.

As in, if you trust an ignorant LLM then by proxy you must trust a heart surgeon to perform your hip replacement.

Still has data contamination though.

> You would have to have a very complete audit trail for the LLM and ensure the paper shows up nowhere in the dataset.

We can do both known and novel reproductions. Like with both LLM training process and human learning, it's valuable to take it in two broad steps:

1) Internalize fully-worked examples, then learn to reproduce them from memory;

2) Train on solving problems for which you know the results but have to work out intermediate steps yourself (looking at the solution before solving the task)

And eventually:

3) Train on solving problems you don't know the answer to, have your solution evaluated by a teacher/judge (that knows the actual answers).

Even parroting existing papers is very valuable, especially early on, when the model is learning how papers and research looks like.

…because there are no consequences for AI. Humans understand shame, pain, and punishment. Until AI models develop this conditional reasoning as part of their process, to me, they’re grossly overestimated in capability and reliability.

I once had a university assignment where they provided the figures from a paper and we had to write the paper around the just the given figures.

A bit like how you might write a paper yourself - starting with the data.

As it turned out I thought the figures looked like data that might be from a paper referenced in a different lecturers set of lectures ( just on the conclusion, he hadn't shown the figures ) - so I went down the library ( this is in the days of non-digitized content - you had to physically walk the stacks ) and looked it up - found the original paper and then a follow up paper by the same authors....

I like to think I was just doing my background research properly.

I told a friend about the paper and before you know it the whole class knew - and I had to admit to the lecturer that I'd found the original paper when he wondered why the whole class had done so well.

Obviously this would be trivial today with an electronic search.

I guess it would also need the experimental data. It would, I guess, also need some ability to do little experiments and write off those ideas as not worth following up on…

Like, have the AI do the experiment? That could be interesting. Although I guess it would be limited to experiments that could be done on a computer.

I think what I would rather like to see is the reproduction of results from experiments that the AI didn't see but are well known. Not reproducing AI papers. For example, assuming a human can build it, would an AI, not knowing anything except what was known at the time, be able to design the millikan oil drop experiment? Or would it be able to design an Taylor-Coutte setup for exploring turbulence? Would it be able to design a linear particle accelerator or a triaxial compression experiment? I think an interesting line of reasoning would be to restrict the training data to what was known before a seminal paper was produced. Like take Lorenz atmospheric circulation paper, train an AI on only data that comes from before that paper was published. Does the AI produce the same equations in the paper and the same description of chaos that Lorenz arrived at?

People typically get paid as a thank you for their corporate work, not just a lukewarm 'thanks for pointing that out.'

In the protein annotation world, which is largely driven by inferring common ancestry between a protein of unknown function and one of known function, common error thresholds range from FDR of 0.001 to 10^-6. Even a 1% error rate would be considered abysmal. This is in part because it is trivial to get 95% accuracy in prediction; the challenging problem is to get some large fraction of the non-trivial 5% correct.

"Acceptable" thresholds are problem specific. For AI to make a meaningful contribution to protein function prediction, it must do substantially better than current methods, not just better than some arbitrary threshold.

I think it's worth being highly skeptical about fraud rates that are stated to two decimal places of precision. Fraud is by design hard to accurately detect. It would be more accurate to say, Medicare decides 7.66% of its cases are fraudulent according to its own policies and procedures, which are likely conservative, and cannot take into account undetected fraud. The true rate is likely higher, perhaps much higher.

There's also the problem of false negatives vs positives. If your goal is to cover 100% of true cases you can achieve that easily by just never denying a claim. That would of course yield stratospheric false positive rates (fraud). You have to understand both the FN rate (cost of missed fraud) vs the FP rate (cost of fraud fighting) and then balance them.

The same applies with using models in science to make predictions.

  > The better question is: what is the acceptable threshold?

I'd be fine if we could. Well, at least far less annoyed. I'm not sure what the threshold should be, but we should always try to minimize it. At least error bounds would do a lot of good at making this happen. But right now we have no clue and that's why this is such a big question that people keep bringing up. We don't point out specific levels of error because they are small and we don't want you looking at them, rather we don't point them out because nobody has a fucking clue.

And until someone has a clue, you shouldn't trust that they error rate is low. The burden of proof is on the one making the claim of performance, not the one asking for evidence to that claim (i.e. skeptics).

Btw, I'd be careful with percentages. Especially when numbers are very high. e.g. LLMs are being trained on trillions of tokens. 10% of 1 trillion is 100 bn. The entire work of Shakespeare is 1.2M tokens... Our 10% error rate would be big enough to spoil any dataset. The bitter truth is that as the absolute number increases, the threshold for acceptable spoilage (in terms of percentage) needs to decrease.

Data leakage is an eval problem, not an accuracy problem.

That is, the problem is not that the AI is wrong X% of the time. The problem is that, in the presence of a data leak, there is no way of knowing what the value of X even is.

This problem is recursive - in the presence of a data leak, you also cannot know for sure the quantity of data that has leaked.

Can you elaborate? You've made a claim, but I really think there'd be value in continuing to what you actually mean.

What is the relevance of this generic statement to the discussion at hand?

Just curious, could you expand on what about that process takes years?

Programming languages are created by humans and the training dataset is complete enough to train LLMs with good results. Most importantly, natural language is the native domain of the programming code.

Whereas in biology, the natural domain is in physical/chemical/biological reactions occuring between organisms and molecules. The laws of interactions are not created by human, but by Creator(tm), and so the training dataset is barely capturing a tiny fraction and richness of the domain and its interactions. Because of this, any model will be inadequate

This is all awesome, but a bit off topic for the thread which focuses on AI for science

The disconnect here is that the cost of iteration is low and it’s relatively easy to verify the quality of a generated C program (does the compiler issue warnings or errors? Does it pass a test suite?) or a recipe (basic experience is probably enough to tell if an ingredient sends out of place or proportions are wildly off)

In science, verifying a prediction is often super difficult and/or expensive because at prediction time we’re trying to shortcut around an expensive or intractable measurement or simulation. Unreliable models can really change the tradeoff point of whether AI accelerates science or just massively inflated the burn rate

Crows and parrots are amazing talkers too, but there's a hard limit to how much sense they make. Do you want those birds to teach your kids and serve you medicine?

I don't think it has anything to do with being impressed or not. It's about being careful not to put too much trust in something so fallible. Because it is so amazing, people overestimate where it can be reliably used.

First off all, I appreciate your comment. Yes, it‘s fucking amazing. (I usually imagine it being „light“ and not „sand“ though. „Sand“ is much more poignant!)

But I think people aren‘t arguing about how amazing it is, but about specific applicability. There‘s also a lot of toxic hype and FUD going around, which can be tiring and frustrating.

> does the generated code have the expected output?

That's many times not easy to verify at all ...

It's a common symptom of the Gartner hype cycle.

This doesn't mean that there aren't very valid use cases for these technologies that can benefit humanity in many ways (and I mean this for both digital currencies and machine learning), but unfortunately those get drowned out by the opportunity seekers and charlatans that give the others the same bad reputation.

As usual, it's best to be highly critical of opinions on both extreme sides of the spectrum until (and if) we start climbing the Slope of Enlightenment.