6 ms·

> Now, is it 10k examples? No, but I think it was on the order of hundreds, if not thousands. I have kids so I'm presuming I'm allowed to have an opinion here.

> Now, is it 10k examples? No, but I think it was on the order of hundreds, if not thousands. I have kids so I'm presuming I'm allowed to have an opinion here. This is ignoring the fact that babies are not just learning labels, they're learning the whole of language, motion planning, sensory processing, etc. Once they have the basics down concept acquisition time shrinks rapidly and kids can easily learn their new favorite animal in as little as a single example. Compare this to LLMs which can one-shot certain tasks, but only if they have essentially already memorized enough information to know about that task. It gives the illusion that these models are learning like children do, when in reality they are not even entirely capable of learning novel concepts. Beyond just learning a new animal, humans are able to learn entirely new systems of reasoning in surprisingly few examples (though it does take quite a bit of time to process them). How many homework questions did your entire calc 1 class have? I'm guessing less than 100 and (hopefully) you successfully learned differential calculus.

_carbyau_ 2y ago
Two other points - I've also forgotten a bunch, but also know I could "relearn" it faster than the first time around. To continue your example, I know I've learned calculus and was lauded at the time. Now I could only give you the vagaries, nothing practical. However I know if I was pressed, I could learn it again in short order.
p1esk 2y ago
illusion that these models are learning like children do, when in reality they are not even entirely capable of learning novel concepts Now imagine how much would your kid learn if the only input he ever received was a sequence of words?
- _flux 2y ago
  Are you saying it's not fair for LLMs, because of the way they are taught is different? The difference is that we don't know better methods for them, but we do know of better methods for people.
  - p1esk 2y ago
    So a billion years of evolutionary search plus 20 years of finetuning is a better method?
  - TeMPOraL 2y ago
    I think they're saying that it's silly to claim humans learn with less data than LLMs, when humans are ingesting a continuous video, audio, olfactory and tactile data stream for 16+ hours a day, every day. It takes at least 4 years for a human children to be in any way comparable in performance to GPT-4 on any task both of them could be tested on; do people really believe GPT-4 was trained with more data than a 4 year old?
    - _flux 2y ago
      I think that's all fair that both LMMs and and people get a certain (even unbounded) amount of "pretraining" before actual tasks. But after the training people are much more equipped to do single-shot recognition and cognitive tasks of imagery and situations they have not encountered before, e.g. identifying (from pictures) which animals is being shown, even if it is the second time of seeing that animal (the first being shown that this animal is a zebra). So, basically, after initial training, I believe people are superior in single-shot tasks—and things are going to get much more interesting once LMMs (or something after that?) are able to do that well. It might be that GPT-4o can actually do that task well! Someone should demo it, I don't have access. Except, of course, GPT-4o already knows what zebras look like, so something else than exactly that..
    - ben_w 2y ago
      > do people really believe GPT-4 was trained with more data than a 4 year old? I think it was; the guesstimate I've seen is GPT-4 was trained on 13e12 tokens, that over 4 years is 8.9e9/day or about 1e5/s. Then it's a question of how many bits per token — my expectation is 100k/s is more than the number of token-equivalents we experience, even though it's much less than the bitrate even of just our ears let alone our eyes.
      - dwaltrip 2y ago
aamar 2y ago
> How many homework questions did your entire calc 1 class have? I'm guessing less than 100… I’m quite surprised at this guess and intrigued by your school’s methodology. I would have estimated >30 problems average across 20 weeks for myself. My kids are still in pre-algebra, but they get way more drilling still, well over 1000 problems per semester once Zern, IReady, etc. are factored in. I believe it’s too much, but it does seem like the typical approach here in California.
- com2kid 2y ago
  I preferred doing large problem sets in math class because that is the only way I felt like I could gain an innate understanding of the math. For example after doing several hundred logarithms, I was eventually able to do logs to 2 decimal places in my head. (Sadly I cannot do that anymore!) I imagine if I had just done a dozen or so problems I would not have gained that ability.
com2kid 2y ago
> This is ignoring the fact that babies are not just learning labels, they're learning the whole of language, motion planning, sensory processing, etc. Sure, but they learn a lot of labels. > How many homework questions did your entire calc 1 class have? I'm guessing less than 100 At least 20 to 30 a week, for about 10 weeks of class. Some weeks were more, and I remember plenty of days where we had 20 problems assigned a day. Indeed, I am a huge fan of "the best way to learn math is to do hundreds upon hundreds of problems", because IMHO some concepts just require massive amounts of repetition.
educasean 2y ago
> kids can easily learn their new favorite animal in as little as a single example Until they encounter a similar animal and get confused, at which point you understand the implicit heuristic they were relying on. (Eg. They confused a dairy cow as a zebra, which means their heuristic was a black-and-white quadrupedal) Doesn't this seem remarkably close to how LLMs behave with one-shot or few-shot learning? I think there are a lot more similarities here than you give it credit for. Also, I grew up in South Korea where early math education is highly prioritized (for better or for worse). I remember having to solve 2 dozen arithmetic problems every week after school with a private tutor. Yes, it was torture and I was miserable, but it did expose me to thousands more arithmetic questions than my American peers. All that misery paid off when I moved to the U.S. at the age of 12 and realized that my math level was 3-4 years above my peers. So yes, I think human intelligence accuracy also does improve with more training data.
- interloxia 2y ago
  Not many zebras where I live but lots of little dogs. Small dogs were clearly cats for a long time no matter what I said. The training can take a while.
  - TeMPOraL 2y ago
    This. My 2.5 y.o. still argues with me that a small dog she just saw in the park is a "cat". That's in contrast to her older sister, who at 5 is... begrudgingly accepting that I might be right about it after the third time I correct her.
    - lynx23 2y ago
      And once they learn sarcasm, small dogs are cats again :-)
    - HarHarVeryFunny 2y ago
      The thing is that the labels "cat" and "dog" reflect a choice in most languages to name animals based on species, which manifests in certain physical/behavioral attributes. Children need to learn by observation/teaching and generalization that these are the characteristics they need to use to conform to our chosen labelling/distinction, and that other things such as size/color/speed are irrelevant. Of course it didn't have to be this way - in a different language animals might be named based on size or abilities/behavior, etc. So, your daughter wanting to label a cat-sized dog as a cat is just a reflection of her not having aligned her generalization of what you are talking about when you say "cat" vs "dog" with her own.
dimask 2y ago
> How many homework questions did your entire calc 1 class have? I'm guessing less than 100 and (hopefully) you successfully learned differential calculus. Not just that: people learn mathematics mainly by _thinking over and solving problems_, not by memorising solutions to problems. During my mathematics education I had to practice solving a lot of problems dissimilar what I had seen before. Even in the theory part, a lot of it was actually about filling in details in proofs and arguments, and reformulating challenging steps (by words or drawings). My notes on top of a mathematical textbook are much more than the text itself. People think that knowledge lies in the texts themselves; it does not, it lies in what these texts relate to and the processes that they are part of, a lot of which are out in the real world and in our interactions. The original article is spot on that there is no AGI pathway in the current research direction. But there are huge incentives for ignoring this.
- whyever 2y ago
  I think there is a component of memorizing solutions. For example, for mathematical proofs there is a set of standard "tricks" that you should have memorized.
  - shkkmo 2y ago
    Sure memory helps a lot, it allows you to concentrate your mental effort on the novel ot unique parts of the problem.
- TeMPOraL 2y ago
  > People think that knowledge lies in the texts themselves; it does not, it lies in what these texts relate to and the processes that they are part of, a lot of which are out in the real world and in our interactions And almost all of it is just more text, or described in more text. You're very much right about this. And that's exactly why LLMs work as well as they do - they're trained on enough text of all kinds and topics, that they get to pick up on all kinds of patterns and relationships, big and small. The meaning of any word isn't embedded in the letters that make it, but in what other words and experiences are associated with it - and it so happens that it's exactly what language models are mapping.
TeMPOraL 2y ago
> This is ignoring the fact that babies are not just learning labels, they're learning the whole of language, motion planning, sensory processing, etc. Yes. All that learning is feeding off one another. They're learning how reality works. Every bit of new information informs everything else. It's something that LLMs demonstrate too, so it shouldn't be a surprising observation. > Once they have the basics down concept acquisition time shrinks rapidly Sort of, kind of. > and kids can easily learn their new favorite animal in as little as a single example. Under 5 they don't. Can't speak what happens later, as my oldest kid just had their 5th birthday. But below 5, all I've seen is kids being quick to remember a name, but taking quite a bit longer to actually distinguish between a new animal and similarly looking ones they already know. It takes a while to update the classifier :). (And no, they aren't going to one-shot recognize an animal in a zoo that they saw first time on a picture hours earlier; it's a case I've seen brought up, and I maintain that even most adults will fail spectacularly at this test.) > Compare this to LLMs which can one-shot certain tasks, but only if they have essentially already memorized enough information to know about that task. It gives the illusion that these models are learning like children do, when in reality they are not even entirely capable of learning novel concepts. Correct, in the sense that the models don't update their weights while you use them. But that just means you have to compare them with ability of humans to one-shot tasks on the spot, "thinking on their feet", which for most tasks makes even adults look bad compared to GPT-4. > How many homework questions did your entire calc 1 class have? I'm guessing less than 100 and (hopefully) you successfully learned differential calculus. I don't believe someone could learn calc in 100 exercises or less. Per concept like "addition of small numbers", or "long division", or "basic derivatives", or "trivial integrals", yes. Note that in-class exercises count too; learning doesn't happen primarily by homework (mostly because few have enough time in a day to do it).
- shkkmo 2y ago
  > But that just means you have to compare them with ability of humans to one-shot tasks on the spot, "thinking on their feet", which for most tasks makes even adults look bad compared to GPT-4. This simply is not true as stated in the article. ARC-AGI is a one-shot task test that humans reliably do much, much better on than any AI model. > I don't believe someone could learn calc in 100 exercises or less. I learned the basics of integration in a foreign language I barely understood by watching a couple of diagrams get drawn out and seeing far less than 100 examples or exercises.

_carbyau_ 2y ago

Two other points - I've also forgotten a bunch, but also know I could "relearn" it faster than the first time around. To continue your example, I know I've learned calculus and was lauded at the time. Now I could only give you the vagaries, nothing practical. However I know if I was pressed, I could learn it again in short order.

p1esk 2y ago

illusion that these models are learning like children do, when in reality they are not even entirely capable of learning novel concepts Now imagine how much would your kid learn if the only input he ever received was a sequence of words?

_flux 2y ago
Are you saying it's not fair for LLMs, because of the way they are taught is different? The difference is that we don't know better methods for them, but we do know of better methods for people.
- p1esk 2y ago
  So a billion years of evolutionary search plus 20 years of finetuning is a better method?
- TeMPOraL 2y ago
  I think they're saying that it's silly to claim humans learn with less data than LLMs, when humans are ingesting a continuous video, audio, olfactory and tactile data stream for 16+ hours a day, every day. It takes at least 4 years for a human children to be in any way comparable in performance to GPT-4 on any task both of them could be tested on; do people really believe GPT-4 was trained with more data than a 4 year old?
  - _flux 2y ago
    I think that's all fair that both LMMs and and people get a certain (even unbounded) amount of "pretraining" before actual tasks. But after the training people are much more equipped to do single-shot recognition and cognitive tasks of imagery and situations they have not encountered before, e.g. identifying (from pictures) which animals is being shown, even if it is the second time of seeing that animal (the first being shown that this animal is a zebra). So, basically, after initial training, I believe people are superior in single-shot tasks—and things are going to get much more interesting once LMMs (or something after that?) are able to do that well. It might be that GPT-4o can actually do that task well! Someone should demo it, I don't have access. Except, of course, GPT-4o already knows what zebras look like, so something else than exactly that..
  - ben_w 2y ago
    > do people really believe GPT-4 was trained with more data than a 4 year old? I think it was; the guesstimate I've seen is GPT-4 was trained on 13e12 tokens, that over 4 years is 8.9e9/day or about 1e5/s. Then it's a question of how many bits per token — my expectation is 100k/s is more than the number of token-equivalents we experience, even though it's much less than the bitrate even of just our ears let alone our eyes.
    - dwaltrip 2y ago

aamar 2y ago

> How many homework questions did your entire calc 1 class have? I'm guessing less than 100… I’m quite surprised at this guess and intrigued by your school’s methodology. I would have estimated >30 problems average across 20 weeks for myself. My kids are still in pre-algebra, but they get way more drilling still, well over 1000 problems per semester once Zern, IReady, etc. are factored in. I believe it’s too much, but it does seem like the typical approach here in California.

com2kid 2y ago
I preferred doing large problem sets in math class because that is the only way I felt like I could gain an innate understanding of the math. For example after doing several hundred logarithms, I was eventually able to do logs to 2 decimal places in my head. (Sadly I cannot do that anymore!) I imagine if I had just done a dozen or so problems I would not have gained that ability.

com2kid 2y ago

> This is ignoring the fact that babies are not just learning labels, they're learning the whole of language, motion planning, sensory processing, etc. Sure, but they learn a lot of labels. > How many homework questions did your entire calc 1 class have? I'm guessing less than 100 At least 20 to 30 a week, for about 10 weeks of class. Some weeks were more, and I remember plenty of days where we had 20 problems assigned a day. Indeed, I am a huge fan of "the best way to learn math is to do hundreds upon hundreds of problems", because IMHO some concepts just require massive amounts of repetition.

educasean 2y ago

> kids can easily learn their new favorite animal in as little as a single example Until they encounter a similar animal and get confused, at which point you understand the implicit heuristic they were relying on. (Eg. They confused a dairy cow as a zebra, which means their heuristic was a black-and-white quadrupedal) Doesn't this seem remarkably close to how LLMs behave with one-shot or few-shot learning? I think there are a lot more similarities here than you give it credit for. Also, I grew up in South Korea where early math education is highly prioritized (for better or for worse). I remember having to solve 2 dozen arithmetic problems every week after school with a private tutor. Yes, it was torture and I was miserable, but it did expose me to thousands more arithmetic questions than my American peers. All that misery paid off when I moved to the U.S. at the age of 12 and realized that my math level was 3-4 years above my peers. So yes, I think human intelligence accuracy also does improve with more training data.

interloxia 2y ago
Not many zebras where I live but lots of little dogs. Small dogs were clearly cats for a long time no matter what I said. The training can take a while.
- TeMPOraL 2y ago
  This. My 2.5 y.o. still argues with me that a small dog she just saw in the park is a "cat". That's in contrast to her older sister, who at 5 is... begrudgingly accepting that I might be right about it after the third time I correct her.
  - lynx23 2y ago
    And once they learn sarcasm, small dogs are cats again :-)
  - HarHarVeryFunny 2y ago
    The thing is that the labels "cat" and "dog" reflect a choice in most languages to name animals based on species, which manifests in certain physical/behavioral attributes. Children need to learn by observation/teaching and generalization that these are the characteristics they need to use to conform to our chosen labelling/distinction, and that other things such as size/color/speed are irrelevant. Of course it didn't have to be this way - in a different language animals might be named based on size or abilities/behavior, etc. So, your daughter wanting to label a cat-sized dog as a cat is just a reflection of her not having aligned her generalization of what you are talking about when you say "cat" vs "dog" with her own.

dimask 2y ago

> How many homework questions did your entire calc 1 class have? I'm guessing less than 100 and (hopefully) you successfully learned differential calculus. Not just that: people learn mathematics mainly by _thinking over and solving problems_, not by memorising solutions to problems. During my mathematics education I had to practice solving a lot of problems dissimilar what I had seen before. Even in the theory part, a lot of it was actually about filling in details in proofs and arguments, and reformulating challenging steps (by words or drawings). My notes on top of a mathematical textbook are much more than the text itself. People think that knowledge lies in the texts themselves; it does not, it lies in what these texts relate to and the processes that they are part of, a lot of which are out in the real world and in our interactions. The original article is spot on that there is no AGI pathway in the current research direction. But there are huge incentives for ignoring this.

whyever 2y ago
I think there is a component of memorizing solutions. For example, for mathematical proofs there is a set of standard "tricks" that you should have memorized.
- shkkmo 2y ago
  Sure memory helps a lot, it allows you to concentrate your mental effort on the novel ot unique parts of the problem.
TeMPOraL 2y ago
> People think that knowledge lies in the texts themselves; it does not, it lies in what these texts relate to and the processes that they are part of, a lot of which are out in the real world and in our interactions And almost all of it is just more text, or described in more text. You're very much right about this. And that's exactly why LLMs work as well as they do - they're trained on enough text of all kinds and topics, that they get to pick up on all kinds of patterns and relationships, big and small. The meaning of any word isn't embedded in the letters that make it, but in what other words and experiences are associated with it - and it so happens that it's exactly what language models are mapping.

TeMPOraL 2y ago

> This is ignoring the fact that babies are not just learning labels, they're learning the whole of language, motion planning, sensory processing, etc. Yes. All that learning is feeding off one another. They're learning how reality works. Every bit of new information informs everything else. It's something that LLMs demonstrate too, so it shouldn't be a surprising observation. > Once they have the basics down concept acquisition time shrinks rapidly Sort of, kind of. > and kids can easily learn their new favorite animal in as little as a single example. Under 5 they don't. Can't speak what happens later, as my oldest kid just had their 5th birthday. But below 5, all I've seen is kids being quick to remember a name, but taking quite a bit longer to actually distinguish between a new animal and similarly looking ones they already know. It takes a while to update the classifier :). (And no, they aren't going to one-shot recognize an animal in a zoo that they saw first time on a picture hours earlier; it's a case I've seen brought up, and I maintain that even most adults will fail spectacularly at this test.) > Compare this to LLMs which can one-shot certain tasks, but only if they have essentially already memorized enough information to know about that task. It gives the illusion that these models are learning like children do, when in reality they are not even entirely capable of learning novel concepts. Correct, in the sense that the models don't update their weights while you use them. But that just means you have to compare them with ability of humans to one-shot tasks on the spot, "thinking on their feet", which for most tasks makes even adults look bad compared to GPT-4. > How many homework questions did your entire calc 1 class have? I'm guessing less than 100 and (hopefully) you successfully learned differential calculus. I don't believe someone could learn calc in 100 exercises or less. Per concept like "addition of small numbers", or "long division", or "basic derivatives", or "trivial integrals", yes. Note that in-class exercises count too; learning doesn't happen primarily by homework (mostly because few have enough time in a day to do it).

shkkmo 2y ago
> But that just means you have to compare them with ability of humans to one-shot tasks on the spot, "thinking on their feet", which for most tasks makes even adults look bad compared to GPT-4. This simply is not true as stated in the article. ARC-AGI is a one-shot task test that humans reliably do much, much better on than any AI model. > I don't believe someone could learn calc in 100 exercises or less. I learned the basics of integration in a foreign language I barely understood by watching a couple of diagrams get drawn out and seeing far less than 100 examples or exercises.