7 ms·

This feels like the worst possible outcome of the current AI hype. We've essentially been ripping off the entire internet and feeding it to the models already,

by crote 3y ago

This feels like the worst possible outcome of the current AI hype. We've essentially been ripping off the entire internet and feeding it to the models already, spending many billions of dollars in the process. It's pretty much the largest possible dataset you can currently get, and due to the ever-increasing and now rapidly accelerated AI poisoning of the internet most likely the largest possible dataset which will ever exist. All that and all we're getting out of it is not-entirely-useless but still quite crappy AI? We would've been better off if we had never done this.

some_random 3y ago
Quite crappy? It seems to me like the current SOTA is working plenty good enough for most use cases and like the highest impact (practical) way to improve right now is going to be advancements in domain knowledge acquisition and retention.
- crote 3y ago
  I sure haven't seen any of those "plenty good" results yet - current SOTA seems to be about as useful as semi-coherently gluing together random Google results. Good enough to perhaps replace a minimum-wage worker, not good enough to provide actual value when you care about the quality of the result. This paper seems to suggest that significant advancement in domain knowledge acquisition and retention is exactly the problem, as you seem to need exponentially more data due to a lack of generalization. What's the point of a model which can perfectly quote Shakespeare if you're a programmer trying to refactor a proprietary codebase and it fails to make a link to whatever garbage it picked up from StackOverflow?
  - Filligree 3y ago
    In CLIP. Replacing CLIP with an LLM is the current meta in image generation models, specifically because of that lack of generalisation. This isn’t a surprise to anyone.
greenavocado 3y ago
I'm getting a heck of a lot of useful work out of these so-called useless models
panarky 3y ago
> not-entirely-useless but still quite crappy 15 months ago, general-purpose LLMs that have not been specifically trained on legal reasoning could score better than 90% of humans on the multistate bar exam, and these are humans who actually completed law school. General-purpose LLMs get similar results in medicine, and when the models are fine-tuned for medical diagnosis they're even better. And that was more than a year ago. Those who have seen current models not yet released tell us they'll make the current state of the art look like toys. Progress is still tracking the steep part of the S-curve, and there's no indication that they're near the top yet.
- flimsypremise 3y ago
  It's not super surprising that LLMs perform well on standardized tests, given that they have a lot of standardized test related text in their training data. There are a lot of claims out there about the zero-shot ability of LLMs, and very little specific research to back it up. Until now that is.
  - MacsHeadroom 3y ago
    This paper is about CLIP not LLMs and does not generalize to LLM architectures.
    - elicksaur 3y ago
      Why not?
- crote 3y ago
  > Progress is still tracking the steep part of the S-curve, and there's no indication that they're near the top yet. If I understand it correctly, that seems to be exactly what this paper is suggesting. Scoring high on the bar exam is pretty trivial for AI - the data needed for that is fairly generic and widely available on the internet. It requires you to demonstrate a relatively basic understanding of the concepts by answering a bunch of multiple-choice answers. If anything, I'd expect AI to have a perfect score. Like I said, such an AI is not entirely useless. You can replace quite a few legal assistants with that, and I bet it could be used to create first drafts or to expand a core concept into a full legal argument. There is plenty of money to be made there, and it's going to make an awful lot of people jobless. But that's just replacing more-trivial jobs with automation, it doesn't add anything novel to society. On the other hand, the actual difficult work involves being able to come up with completely novel concepts, and being able to expand upon some obscure but crucial stuff few people have ever heard about. Current models simply aren't capable of that, and the results achieved here with multimodal models suggests that they never will. We risk getting stuck with models which can do some trivial work, but silently produce complete garbage when you ask them to do anything providing substantial value.
HarHarVeryFunny 3y ago
Even if LLMs (pre-trained transformers) turn out to be a dead end as far as AGI goes, there are productivity applications for them, and perhaps just as importantly interesting insights/confirmations about how the mind works and directions for future AGI research/architectures. The use cases for LLMs will no doubt grow as hallucinations are reduced, and they gain planning/reasoning ability over next couple of years. It'll be interesting to see what they subjectively "feel" like with these improvements.
- crote 3y ago
  Oh absolutely, LLMs are already causing a slaughter in applications where quality doesn't really matter to the company, like customer service. With minor improvements they're going to be a serious problem for any junior developer/lawyer/journalist/reviewer/artist/whatever, and if they ever fix the hallucinations issue it's game-changing. On the other hand, it's still a big "if" whether a general hallucinations solution exists, and in the meantime we're paying a pretty high price for it, as the entire internet is being flooded with absolute garbage. We risk getting stuck in a situation where there is no way to get new senior people because nobody hired them as juniors because AI is cheaper and good enough, and senior people are getting less and less productive due to them being unable to use websites like StackOverflow as reference material. That's a pretty high price for a tiny gain, if you ask me.
  - Art9681 3y ago
    One less potential competitor employee we have to worry about in the future economy. Thank you for your service.
int_19h 3y ago
The training sets used for current models, even the largest ones, is nowhere even close to "the entire Internet". Some napkin math based on known sizes of public datasets says it's less than 1%.
eru 3y ago
> It's pretty much the largest possible dataset you can currently get, [...] No. What you describe encompasses only one poor modality: text. We have oodles more data, and can create almost arbitrary amounts more, by just eg pointing webcams at the world. > We would've been better off if we had never done this. Who is 'we'?

by crote 3y ago

some_random 3y ago

Quite crappy? It seems to me like the current SOTA is working plenty good enough for most use cases and like the highest impact (practical) way to improve right now is going to be advancements in domain knowledge acquisition and retention.

crote 3y ago
I sure haven't seen any of those "plenty good" results yet - current SOTA seems to be about as useful as semi-coherently gluing together random Google results. Good enough to perhaps replace a minimum-wage worker, not good enough to provide actual value when you care about the quality of the result. This paper seems to suggest that significant advancement in domain knowledge acquisition and retention is exactly the problem, as you seem to need exponentially more data due to a lack of generalization. What's the point of a model which can perfectly quote Shakespeare if you're a programmer trying to refactor a proprietary codebase and it fails to make a link to whatever garbage it picked up from StackOverflow?
- Filligree 3y ago
  In CLIP. Replacing CLIP with an LLM is the current meta in image generation models, specifically because of that lack of generalisation. This isn’t a surprise to anyone.

greenavocado 3y ago

I'm getting a heck of a lot of useful work out of these so-called useless models

panarky 3y ago

> not-entirely-useless but still quite crappy 15 months ago, general-purpose LLMs that have not been specifically trained on legal reasoning could score better than 90% of humans on the multistate bar exam, and these are humans who actually completed law school. General-purpose LLMs get similar results in medicine, and when the models are fine-tuned for medical diagnosis they're even better. And that was more than a year ago. Those who have seen current models not yet released tell us they'll make the current state of the art look like toys. Progress is still tracking the steep part of the S-curve, and there's no indication that they're near the top yet.

flimsypremise 3y ago
It's not super surprising that LLMs perform well on standardized tests, given that they have a lot of standardized test related text in their training data. There are a lot of claims out there about the zero-shot ability of LLMs, and very little specific research to back it up. Until now that is.
- MacsHeadroom 3y ago
  This paper is about CLIP not LLMs and does not generalize to LLM architectures.
  - elicksaur 3y ago
    Why not?
crote 3y ago
> Progress is still tracking the steep part of the S-curve, and there's no indication that they're near the top yet. If I understand it correctly, that seems to be exactly what this paper is suggesting. Scoring high on the bar exam is pretty trivial for AI - the data needed for that is fairly generic and widely available on the internet. It requires you to demonstrate a relatively basic understanding of the concepts by answering a bunch of multiple-choice answers. If anything, I'd expect AI to have a perfect score. Like I said, such an AI is not entirely useless. You can replace quite a few legal assistants with that, and I bet it could be used to create first drafts or to expand a core concept into a full legal argument. There is plenty of money to be made there, and it's going to make an awful lot of people jobless. But that's just replacing more-trivial jobs with automation, it doesn't add anything novel to society. On the other hand, the actual difficult work involves being able to come up with completely novel concepts, and being able to expand upon some obscure but crucial stuff few people have ever heard about. Current models simply aren't capable of that, and the results achieved here with multimodal models suggests that they never will. We risk getting stuck with models which can do some trivial work, but silently produce complete garbage when you ask them to do anything providing substantial value.

HarHarVeryFunny 3y ago

Even if LLMs (pre-trained transformers) turn out to be a dead end as far as AGI goes, there are productivity applications for them, and perhaps just as importantly interesting insights/confirmations about how the mind works and directions for future AGI research/architectures. The use cases for LLMs will no doubt grow as hallucinations are reduced, and they gain planning/reasoning ability over next couple of years. It'll be interesting to see what they subjectively "feel" like with these improvements.

crote 3y ago
Oh absolutely, LLMs are already causing a slaughter in applications where quality doesn't really matter to the company, like customer service. With minor improvements they're going to be a serious problem for any junior developer/lawyer/journalist/reviewer/artist/whatever, and if they ever fix the hallucinations issue it's game-changing. On the other hand, it's still a big "if" whether a general hallucinations solution exists, and in the meantime we're paying a pretty high price for it, as the entire internet is being flooded with absolute garbage. We risk getting stuck in a situation where there is no way to get new senior people because nobody hired them as juniors because AI is cheaper and good enough, and senior people are getting less and less productive due to them being unable to use websites like StackOverflow as reference material. That's a pretty high price for a tiny gain, if you ask me.
- Art9681 3y ago
  One less potential competitor employee we have to worry about in the future economy. Thank you for your service.

int_19h 3y ago

The training sets used for current models, even the largest ones, is nowhere even close to "the entire Internet". Some napkin math based on known sizes of public datasets says it's less than 1%.

eru 3y ago

> It's pretty much the largest possible dataset you can currently get, [...] No. What you describe encompasses only one poor modality: text. We have oodles more data, and can create almost arbitrary amounts more, by just eg pointing webcams at the world. > We would've been better off if we had never done this. Who is 'we'?