4 ms·

Even if the statement were true, the data that these models are trained on is, collectively, smarter than the smartest person who ever lived. An individual pers

Even if the statement were true, the data that these models are trained on is, collectively, smarter than the smartest person who ever lived. An individual person simply cannot absorb, in an 80-year-or-so lifetime, even a tiny fraction of the amount of information contained in the training data. It is a truly mind-boggling amount of data. The most frightening thing is that, even with pretty naive and primitive algorithms, this data has been able to reproduce roughly human-level creativity and linguistic ability. Imagine how powerful these models can become---even using the same data---with more sophisticated algorithmic approaches.

_dwt 3y ago
It is, though, by the same argument also collectively dumber than the dumbest person who ever lived. No single human moron's brain could accommodate so much misinformation, useless fluff, spurious reasoning, etc. etc. etc. So I think time will tell. My money is on a sort of regression to the mean: these models will capture the style and "creativity" of the average 2020s Reddit, StackOverflow, etc. user. I can't say I'm terribly excited.
- jstx1 3y ago
  > It is, though, by the same argument also collectively dumber than the dumbest person who ever lived. Not at all, it's not symmetrical. You're ignoring the training data and all the additional RLHF fine tuning. The model is being actively penalized for being dumb which is why it isn't that dumb in a lot of cases.
  - grayhatter 3y ago
    but the popular models plastered across HN are in fact, dumb in a lot of cases. Everyone is impressed when the model does something they don't understand deeply. But it's very rare when someone is impressed when the model is generating text based in something they do understand deeply. I do think it's slightly better than the mean across all topics. But I also strongly suspect it'll soon serve as a great example for regressing to the mean.
    - jstx1 3y ago
      > But it's very rare when someone is impressed when the model is generating text based in something they do understand deeply. This statement sounds out of date. And you can see this sentiment a lot on HN. I don’t know if the people who say this haven’t tried GPT-4 or they have and are just stubbornly refusing to change their mind about something when presented with new evidence.
      - grayhatter 3y ago
        > s haven’t tried GPT-4 or they have and are just stubbornly refusing to change their mind about something when presented with new evidence. Have you considered that the evidence just isn't convincing yet? If you view popular llms as text prediction machines, they are in fact much better than spell check or auto correct from a few years ago. But if you actually ask it to solve the problem with nuance it will not use nuance. That's the part that would impress me. As a recent example if you ask chat GPT how to use ffmpeg to slice out a video. and you tell it that you only want 3 seconds of video. somebody who deeply understands how ffmpeg works, (or even someone who deeply read the documentation) would point out that you have to be aware that it can only cut to keyframes. so if the keyframe is not aligned to the time you ask for you will not get the video that you expect. another example ask it to play 20 questions with you, it will cheat at the end. even if you give it very specific instructions it still is unable to follow them to the fair conclusion of the game. (or it will make a mistake and understanding about some of the semantics of the question, but I don't fault it for a difference in context) I'd caution you that just because you're impressed for the subjects that you understand deeply does not mean that chat gpt is good at all subjects. and therefore I assert that it is disrespectful to be so dismissive of people who have different opinions than yours. I believe the default should be to assume good faith rather than dismissiveness "they just don't understand"
        jstx1 3y ago
        ChatGPT with GPT-4 with just "how to use ffmpeg to slice out a video" pasted directly from your comment without any additional prompting or clarification brought up your point about the keyframes. So whatever you think you're criticising isn't the same thing that I'm using. Kind of demonstrates my point that you're either using an older version or choosing to ignore evidence for some reason.
lisasays 3y ago
Even if the statement were true, the data that these models are trained on is, collectively, smarter than the smartest person who ever lived. "Smarter" in a horizontal sense. But not vertical. The latter being what people really mean by "smarter". You know, that thinking-outside-the-box stuff. That's why LLMs aren't able to "solve" problems that don't have existing writeups somewhere (beyond certain problems more or less equivalent to combinatorial search). Or produce text useful beyond the mild amusement level (or for spam and/or research fraud -- at which it of course excels quite brilliantly). And still trips over itself narratively, left, right and center. And still makes all kind of cute-sounding, and superficially correct (but ultimately misleading) pronouncements about the world that a true expert would never make. Taking an example from a recent NYT article: "Give me some useful mnemonics for learning Spanish" (paraphrased slightly): Hola: "Hola, cola" - sounds like "cola" (Coke) Adiós: "Adiós, my toes" - rhymes with "toes" Gracias: "Gracias, grassy ass" - sounds like "grassy ass" Mañana: "Mañana, banana" - rhymes with "banana" Uno: "Uno, you know" - sounds like "you know" Tres: "Tres, dress" - rhymes with "dress" As everyone who knows Spanish knows -- none of these really rhymes in Spanish (and if you try actually speaking these words (except the sfollowing the templates given, you'll get laughed pretty quickly). But they're certainly cute, and astonishingly impressive.
- vannevar 3y ago
  >That's why LLMs aren't able to "solve" problems that don't have existing writeups somewhere (beyond certain problems more or less equivalent to combinatorial search). Neither can humans. We create by analogy; "thinking outside the box" just means making less obvious (but still valid) analogies. >Or produce text useful beyond the mild amusement level (or for spam and/or research fraud -- at which it of course excels quite brilliantly). People are using LLMs very effectively for writing news reports, opinion articles, legal summaries, and computer code. It's already gone well beyond "mild amusement." There's a reason many companies have had to adopt policies regarding LLMs at work: people are already using them at work. And sure, LLMs still make lots of mistakes. But they are already better writers than the vast majority of humans, and as I noted, the algorithms being applied are pretty simple yet.
  - 3y ago

_dwt 3y ago

It is, though, by the same argument also collectively dumber than the dumbest person who ever lived. No single human moron's brain could accommodate so much misinformation, useless fluff, spurious reasoning, etc. etc. etc. So I think time will tell. My money is on a sort of regression to the mean: these models will capture the style and "creativity" of the average 2020s Reddit, StackOverflow, etc. user. I can't say I'm terribly excited.

jstx1 3y ago
> It is, though, by the same argument also collectively dumber than the dumbest person who ever lived. Not at all, it's not symmetrical. You're ignoring the training data and all the additional RLHF fine tuning. The model is being actively penalized for being dumb which is why it isn't that dumb in a lot of cases.
- grayhatter 3y ago
  but the popular models plastered across HN are in fact, dumb in a lot of cases. Everyone is impressed when the model does something they don't understand deeply. But it's very rare when someone is impressed when the model is generating text based in something they do understand deeply. I do think it's slightly better than the mean across all topics. But I also strongly suspect it'll soon serve as a great example for regressing to the mean.
  - jstx1 3y ago
    > But it's very rare when someone is impressed when the model is generating text based in something they do understand deeply. This statement sounds out of date. And you can see this sentiment a lot on HN. I don’t know if the people who say this haven’t tried GPT-4 or they have and are just stubbornly refusing to change their mind about something when presented with new evidence.
    - grayhatter 3y ago
      > s haven’t tried GPT-4 or they have and are just stubbornly refusing to change their mind about something when presented with new evidence. Have you considered that the evidence just isn't convincing yet? If you view popular llms as text prediction machines, they are in fact much better than spell check or auto correct from a few years ago. But if you actually ask it to solve the problem with nuance it will not use nuance. That's the part that would impress me. As a recent example if you ask chat GPT how to use ffmpeg to slice out a video. and you tell it that you only want 3 seconds of video. somebody who deeply understands how ffmpeg works, (or even someone who deeply read the documentation) would point out that you have to be aware that it can only cut to keyframes. so if the keyframe is not aligned to the time you ask for you will not get the video that you expect. another example ask it to play 20 questions with you, it will cheat at the end. even if you give it very specific instructions it still is unable to follow them to the fair conclusion of the game. (or it will make a mistake and understanding about some of the semantics of the question, but I don't fault it for a difference in context) I'd caution you that just because you're impressed for the subjects that you understand deeply does not mean that chat gpt is good at all subjects. and therefore I assert that it is disrespectful to be so dismissive of people who have different opinions than yours. I believe the default should be to assume good faith rather than dismissiveness "they just don't understand"
      - jstx1 3y ago
        ChatGPT with GPT-4 with just "how to use ffmpeg to slice out a video" pasted directly from your comment without any additional prompting or clarification brought up your point about the keyframes. So whatever you think you're criticising isn't the same thing that I'm using. Kind of demonstrates my point that you're either using an older version or choosing to ignore evidence for some reason.

lisasays 3y ago

Even if the statement were true, the data that these models are trained on is, collectively, smarter than the smartest person who ever lived. "Smarter" in a horizontal sense. But not vertical. The latter being what people really mean by "smarter". You know, that thinking-outside-the-box stuff. That's why LLMs aren't able to "solve" problems that don't have existing writeups somewhere (beyond certain problems more or less equivalent to combinatorial search). Or produce text useful beyond the mild amusement level (or for spam and/or research fraud -- at which it of course excels quite brilliantly). And still trips over itself narratively, left, right and center. And still makes all kind of cute-sounding, and superficially correct (but ultimately misleading) pronouncements about the world that a true expert would never make. Taking an example from a recent NYT article: "Give me some useful mnemonics for learning Spanish" (paraphrased slightly): Hola: "Hola, cola" - sounds like "cola" (Coke) Adiós: "Adiós, my toes" - rhymes with "toes" Gracias: "Gracias, grassy ass" - sounds like "grassy ass" Mañana: "Mañana, banana" - rhymes with "banana" Uno: "Uno, you know" - sounds like "you know" Tres: "Tres, dress" - rhymes with "dress" As everyone who knows Spanish knows -- none of these really rhymes in Spanish (and if you try actually speaking these words (except the sfollowing the templates given, you'll get laughed pretty quickly). But they're certainly cute, and astonishingly impressive.

vannevar 3y ago
>That's why LLMs aren't able to "solve" problems that don't have existing writeups somewhere (beyond certain problems more or less equivalent to combinatorial search). Neither can humans. We create by analogy; "thinking outside the box" just means making less obvious (but still valid) analogies. >Or produce text useful beyond the mild amusement level (or for spam and/or research fraud -- at which it of course excels quite brilliantly). People are using LLMs very effectively for writing news reports, opinion articles, legal summaries, and computer code. It's already gone well beyond "mild amusement." There's a reason many companies have had to adopt policies regarding LLMs at work: people are already using them at work. And sure, LLMs still make lots of mistakes. But they are already better writers than the vast majority of humans, and as I noted, the algorithms being applied are pretty simple yet.
- 3y ago