6 ms·

You are conflating multiple things. 1) First, you are talking about positive forward transfer in continual learning. I've been giving talks for the past 6-7 ye

You are conflating multiple things. 1) First, you are talking about positive forward transfer in continual learning. I've been giving talks for the past 6-7 years about how that community (I was one of the founders) went astray and wasn't focusing enough on that topic, but continual learning of the kind you are thinking isn't in any of these systems right now. I think some people left the Grok team to make a start-up to focus on that. By forward transfer, what I mean is weights update over time and past learning improves future learning such that we get better sample efficiency. 2) Psychologists distinguish among different kinds of intelligence for Spearman's g (IQ). Crystalized intelligence is using already acquired knowledge (frontier models probably have maxed out that). Fluid intelligence is reasoning and finding solutions in novel situations or without the necessary crystalized knowledge. [Giving colloquial definitions] 3) Now, interestingly, neither of those are correlated with _creativity_ (just they are independent, note some have this threshold theory but it hasn't held up in recent papers). That's what the AI's really are terrible at -- creativity. But I'd argue the vast majority of humans aren't very creative, with truly out-of-the-box ideas. Given that this is HN, and a non-trivial number of us have ADHD, creativity is positively correlated with ADHD. I did a bunch of research on these topics for my AGI course that I teach each Spring (where I then point out conflicting definitions and start using multiple alternative terms rather than AGI to distinguish among the different definitions).

somethingsome 13d ago
Very interesting! I was looking at your website and wondering if there is a way to have access to the course material/videos? In particular: Spring 2025 @ UR : CSC 209/409 Seminar on Artificial General Intelligence Fall 2024 @ UR : CSC 277/477 End-to-End Deep Learning Spring 2023 @ UR : CSC 266/466 Frontiers in Deep Learning Spring 2022 @ Cornell Tech : CS 5787 – Deep Learning Fall 2021 @ RIT : IMGS 684 – Deep Learning for Vision
monkeydust 12d ago
> That's what the AI's really are terrible at -- creativity Good thought piece here "We Are Losing the Ability to Discover What We Didn’t Know to Ask[1]" By Anne-Laure Le Cunff It keeps playing on my mind as I see people at work follow some predetermined AI workflow to get their jobs done, the art of being curious and exploring around the problem is so important to the really big innovations. Been thinking about how to address this through some of the harnesses we are developing in the knowledge working space. [1] https://archive.is/IAxf9 https://archive.is/IAxf9
- chrisweekly 12d ago
  Tangent: Anne-Laure Le Cunff is a neuroscientist with a talent for community-building and writing, and bringing evidence-based approaches to bear on practical solutions to various domains. See eg https://nesslabs.com https://nesslabs.com
- mlmonkey 12d ago
  I met a senior (as in, 4th year of college) recently and she asked me: what advice do you have for someone just graduating in the AI age? (as I had told her that I've been in ML for 20+ years, etc.) My recommendation to her was: just _play_ with the AI! It's a brand new tool, and none of us knows its capabilities, limitations, boundaries etc. (which are fluid, of course). So just spend as much time as you can tinkering with it, playing with it, making it do things it was not expected to do, etc. and you'll develop an idea of how to make better use of it.
  - monkeydust 12d ago
    Good advice. Never underestimate the power of play, something I recently re-learnt after having kids.
jtrn 12d ago
This comment and the one above from astrobiased feel like coming into a messy codebase, and it’s more work to sort it out than it would have been to write it from scratch.... And since I actually do intelligence testing as a clinical psychologist, I have experience with this in both practice and theory. So now I’m going to waste an hour because I just have to respond to “something is wrong on the internet.”. Chollet's distinction is useful. High performance on known tasks is not the same thing as efficient adaptation to a novel task. Prior knowledge and training data can buy skill. That is a central point of On the Measure of Intelligence. But it does not follow that current frontier progress is only "coverage-driven competence." That is a hypothesis. It is not a result established by Chollet's framework. "Overfitting at scale" is also the wrong term. A model that learns broad representations and applies them successfully to unseen examples is generalizing. The relevant concern is whether apparent novelty is actually inside the effective training distribution, not whether the model is "overfit." There is also an unstated premise here: that adding broad knowledge and skills cannot improve the machinery used for novel problem solving. I do not see a basis for assuming that. Learned representations, abstractions, reasoning patterns, and cross-domain analogies can themselves support transfer to new tasks. Whether this becomes sufficient for general intelligence is an open question with insufficient data. But its a perfectly valid hypothesis right now that, given enough domain knowledge and symbolic reasoning examples, LLM COULD maybe "Grok" AGI at a certain critical threshold. And ARC-AGI-3 was specifically designed around novel abstract environments that require exploration and adaptation. Astra scores 99.9% with OpenAI's context-preserving Provider Adapter, and ARC reports that Astra constructed compact symbolic models of unfamiliar environments. That does not prove AGI, but it points in that direction more so than the other way around. Gc roughly maps to acquired knowledge. Gf roughly maps to reasoning in relatively novel situations. Naming those two categories does not tell us whether increasing acquired knowledge and learned abstractions in an AI can improve Gf-like behavior. That causal question is exactly what is disputed. And "Frontier models probably have maxed out crystallized intelligence" is just obviously wrong, unless you think they have been able to dig up every a scrap of paper with knowledge/information on it in the entire world, AND that there is no more useful knowledge to be generated left in the universe. And the statement that intelligence and creativity are independent is simply wrong. A meta-analysis of 112 studies and 34k participants found a positive correlation of about r .25 between intelligence and divergent thinking. It also found that using g, Gf, or Gc did not eliminate that relationship. Creative achievement has a smaller but still positive meta-analytic association with intelligence, around r = .16. These are distinct constructs, not independent constructs. And this is just a bad take: "AIs are terrible at creativity". At best that depends on which creativity, and I think its straight up wrong. On divergent thinking tasks, the operationalization behind every ADHD study you could cite, LLMs score above most humans, with the top humans still ahead. If you means Big-C, paradigm-shifting creativity, that is a different construct and none of the ADHD evidence transfers to it. And if I where to say what I subjectively feel and see.... I have ABSOLUTELY no idea how people can say that we are not seeing sparks of creativity from AIs already. If a PERSON produced some of the music, solutions or deductions that I have seen AIs do, people would have NO problem celebrating it as extremely creative. And finally, the ADHD claim is also, at best, overstated and just as often debunked. There is some evidence that higher subclinical ADHD trait scores, often survey studies only, are associated with better performance on some divergent-thinking measures. But a review of 31 studies did not find a consistent creativity advantage for people with clinical ADHD, and it found no evidence of better convergent thinking. Okay, I’m done… And nobody noticed that I’m not doing my job here.
evrydayhustling 12d ago
> weights update over time and past learning improves future learning such that we get better sample efficiency Is there an architecture-independent definition of forward transfer? For the practical experience and implications of AI progress, I think we are increasingly discussing what these LLMs can accomplish inside a stateful harness, the state of which could be described as part of a (very squirrely) parameter space.
lelanthran 12d ago
> That's what the AI's really are terrible at -- creativity. But I'd argue the vast majority of humans aren't very creative, with truly out-of-the-box ideas. Given that this is HN, and a non-trivial number of us have ADHD, creativity is positively correlated with ADHD. I think it's hard to define creativity in the context of AI because they seemingly just make up new hyphenated terms for everything. Is that creativity? If not, what about when they do the same thing different ideas in the latent space? If we say that simply nailing one concept to another isn't creativity, then AIs are incapable of creativity, while the vast majority of humans are incapable of creativity. This is just a long way of saying "0 AIs have creativity, 0.00001% of humans have creativity", and the difference between zero and a very small number is infinity.
- datsci_est_2015 12d ago
  It always comes back to taste. It’s not that they’re incapable of creativity, it’s that LLM-driven creativity is terrible, and nothing makes me cringe more than when it uses a word in a “novel” way. But monkeys-with-typewriters, they sometimes stumble upon something that doesn’t suck. But if you don’t want to spend a fortune retrying the same task until you get a suitable result you have to inject your own taste. Sometimes I start with a super vague prompt and see how close agents can get to something that doesn’t suck. I inevitably get frustrated about 6-7 prompts in when they’ve created a complete mess because they have no taste. So I restart and inject my taste into the process. Things like linters, test suites, which 3PLs to use, etc.
  - fspeech 12d ago
    Taste or vision? They are hill climbing and they can't see the other side. And sometimes that is because they don't live in your head and don't know what you want.
    - clord 12d ago
      Very insightful actually. Taste and vision are both instances of holding a model that predicts a good result beyond the threshold of validity in theory but reality happens to align. Is it luck then? Perhaps meta-luck where lucky weights produce “accidentally great” results with some predictability. Steve Jobs had a mental model that brought the iPhone. No one really wanted it but something in his life biased the result. So creativity is having weights so good you can project way out into latent space beyond what is reasonable.
itissid 12d ago
Hi thanks for the insight. Do you see a role for Control Systems(i.e. ones analogus to Instrumentation engineering) playing a role to modulate certain parts of continual learning? One very important way we learn are lived experiences, it's like telling memory:this part is more important( for emotional or social utility values), pay attention. Good or bad lived experiences both count. I guess is that a path that practical research is considering?
heyjamesknight 12d ago
I think our AI systems are essentially massive Central Executive Networks. But novel ideas (creativity) come from the Default Mode Network. These are the difference in what Kahneman called System 2&1 thinking and what the ancients called the Ratio and the Intellect. LLMs are all ratio. They depend on our intellect for guidance.
mannanj 12d ago
What have you defined as creativity and intelligence?
corndoge 12d ago
> Given that this is HN, and a non-trivial number of us have ADHD, creativity is positively correlated with ADHD. Very strong reasoning here. Is there anything this ADHD condition cannot explain?
- zxexz 12d ago
  Socrates was a man. Socrates was creative. Ergo, Socrates is on HN. Ergo, we’re all dead.
  - reverius42 9d ago
    Dead from ergo poisoning.
- dev_dan_2 11d ago
  It should obviously not come as a surprise that having areas of the brains working differently can explain an awfull lot of things... but to answer your question directly, yes of course of course there are! Simply look up all the many, well -supported and -researched known correlations with ADHD first. In the second step, you can construct the set of all possible correlations, and subtract the well-researched ones if it. What is left is the set of correlations that are either not explained by ADHD (the big majority I would assume) or explained by ADHD, but as-of-yet unknowingly so. This might be a bit anticlimatic, but clinical psychology is pretty straightforward study design and statistics, and set theory is not that new either, so... no big surprises I am afraid.
- prpl 11d ago
  Not sure the thrust of your comment, but what is really going on is just novelty seeking to get a bit of a dopamine rush, which slides into creativity. It all comes down to dopamine at the end of the day.
chermi 12d ago
"Given that this is HN, and a non-trivial number of us have ADHD, creativity is positively correlated with ADHD." Is this a statement of fact? As someone with ADHD and pretty confident in my creativity, I still believe this is much more cope than fact(which could be more a self-doubt thing than anything else). Further, if there is a correlation, I'd bet it's not so much an intrinsic "creativity" trait, but more effectively higher creativity because more trials. That is, along the lines of Chollet's paper, a measure of creativity should be based on a fixed budget with fixed knowledge. Among many other possibilities I haven't considered, perhaps another mechanism could be that because ADHD people spend more time thinking in less goal-oriented ways and mixing thoughts on accident, perhaps we do in fact gain some learned creativity via experience with vagueness[1]? But that might also imply that part of creativity is actually being able to diffuse more freely through thought space and lowering the barrier to attempted connections between ideas. That lower barrier leads to less likelihood of any "collision" being meaningful but maybe it's overcome by higher collision rates? Or maybe effectively higher order (not just pairwise) collisions? Disclaimer in case it's not obvious: I don't know any of the literature on what creativity even means or how it's quantified. [1] Which is me injecting an assumption that creativity ~= connecting things with no obvious or well-troden reasoning path between them. edit -- oops just looked at your profile after seeing someone elses comment. I assume you are stating a fact then, leaving original anyway
- r_lee 12d ago
  I think it's just that many of us don't have the capability to just do things in rote or the conventional ways, instead we must rely more on the creative and unconventional ways of doing things and parts of the brain responsible for that
astrobiased 12d ago
Appreciate the input! Responses below to your first two items: 1) I'm leaning more into a broader sense, which is that given the priors a system already possesses, how efficient can it acquire competence on a novel task? If I'm reading the point you make, you're focussing on continual learning right? If so, I'm not necessarily restricting my statement above to that. Here's another reframing: How much of the benchmark improvements come from overwhelmingly large training distributions vs improving the models for adapting to things genuinely outside of it? 2) Excellent points about crystalized and fluid intelligence. Wouldn't the LLM scaling gains be a representation of crystallized capabilities? In regard to Gf, that is exactly what I am asking about. That is what seems to be lacking, Gf like adaptation under genuine novelty. My concern is that it's increasingly difficult to tell of what looks like Gf like behavior is really coming from better adaption vs. having broad priors from the model's large learned distributions.
gjvc 11d ago
> Given that this is HN, and a non-trivial number of us have ADHD, creativity is positively correlated with ADHD. big pharma must LOVE people like you parroting the ADHD bullshit all the time

somethingsome 13d ago

Very interesting! I was looking at your website and wondering if there is a way to have access to the course material/videos? In particular: Spring 2025 @ UR : CSC 209/409 Seminar on Artificial General Intelligence Fall 2024 @ UR : CSC 277/477 End-to-End Deep Learning Spring 2023 @ UR : CSC 266/466 Frontiers in Deep Learning Spring 2022 @ Cornell Tech : CS 5787 – Deep Learning Fall 2021 @ RIT : IMGS 684 – Deep Learning for Vision

monkeydust 12d ago

> That's what the AI's really are terrible at -- creativity Good thought piece here "We Are Losing the Ability to Discover What We Didn’t Know to Ask[1]" By Anne-Laure Le Cunff It keeps playing on my mind as I see people at work follow some predetermined AI workflow to get their jobs done, the art of being curious and exploring around the problem is so important to the really big innovations. Been thinking about how to address this through some of the harnesses we are developing in the knowledge working space. [1] https://archive.is/IAxf9 https://archive.is/IAxf9

chrisweekly 12d ago
Tangent: Anne-Laure Le Cunff is a neuroscientist with a talent for community-building and writing, and bringing evidence-based approaches to bear on practical solutions to various domains. See eg https://nesslabs.com https://nesslabs.com
mlmonkey 12d ago
I met a senior (as in, 4th year of college) recently and she asked me: what advice do you have for someone just graduating in the AI age? (as I had told her that I've been in ML for 20+ years, etc.) My recommendation to her was: just _play_ with the AI! It's a brand new tool, and none of us knows its capabilities, limitations, boundaries etc. (which are fluid, of course). So just spend as much time as you can tinkering with it, playing with it, making it do things it was not expected to do, etc. and you'll develop an idea of how to make better use of it.
- monkeydust 12d ago
  Good advice. Never underestimate the power of play, something I recently re-learnt after having kids.

jtrn 12d ago

This comment and the one above from astrobiased feel like coming into a messy codebase, and it’s more work to sort it out than it would have been to write it from scratch.... And since I actually do intelligence testing as a clinical psychologist, I have experience with this in both practice and theory. So now I’m going to waste an hour because I just have to respond to “something is wrong on the internet.”. Chollet's distinction is useful. High performance on known tasks is not the same thing as efficient adaptation to a novel task. Prior knowledge and training data can buy skill. That is a central point of On the Measure of Intelligence. But it does not follow that current frontier progress is only "coverage-driven competence." That is a hypothesis. It is not a result established by Chollet's framework. "Overfitting at scale" is also the wrong term. A model that learns broad representations and applies them successfully to unseen examples is generalizing. The relevant concern is whether apparent novelty is actually inside the effective training distribution, not whether the model is "overfit." There is also an unstated premise here: that adding broad knowledge and skills cannot improve the machinery used for novel problem solving. I do not see a basis for assuming that. Learned representations, abstractions, reasoning patterns, and cross-domain analogies can themselves support transfer to new tasks. Whether this becomes sufficient for general intelligence is an open question with insufficient data. But its a perfectly valid hypothesis right now that, given enough domain knowledge and symbolic reasoning examples, LLM COULD maybe "Grok" AGI at a certain critical threshold. And ARC-AGI-3 was specifically designed around novel abstract environments that require exploration and adaptation. Astra scores 99.9% with OpenAI's context-preserving Provider Adapter, and ARC reports that Astra constructed compact symbolic models of unfamiliar environments. That does not prove AGI, but it points in that direction more so than the other way around. Gc roughly maps to acquired knowledge. Gf roughly maps to reasoning in relatively novel situations. Naming those two categories does not tell us whether increasing acquired knowledge and learned abstractions in an AI can improve Gf-like behavior. That causal question is exactly what is disputed. And "Frontier models probably have maxed out crystallized intelligence" is just obviously wrong, unless you think they have been able to dig up every a scrap of paper with knowledge/information on it in the entire world, AND that there is no more useful knowledge to be generated left in the universe. And the statement that intelligence and creativity are independent is simply wrong. A meta-analysis of 112 studies and 34k participants found a positive correlation of about r .25 between intelligence and divergent thinking. It also found that using g, Gf, or Gc did not eliminate that relationship. Creative achievement has a smaller but still positive meta-analytic association with intelligence, around r = .16. These are distinct constructs, not independent constructs. And this is just a bad take: "AIs are terrible at creativity". At best that depends on which creativity, and I think its straight up wrong. On divergent thinking tasks, the operationalization behind every ADHD study you could cite, LLMs score above most humans, with the top humans still ahead. If you means Big-C, paradigm-shifting creativity, that is a different construct and none of the ADHD evidence transfers to it. And if I where to say what I subjectively feel and see.... I have ABSOLUTELY no idea how people can say that we are not seeing sparks of creativity from AIs already. If a PERSON produced some of the music, solutions or deductions that I have seen AIs do, people would have NO problem celebrating it as extremely creative. And finally, the ADHD claim is also, at best, overstated and just as often debunked. There is some evidence that higher subclinical ADHD trait scores, often survey studies only, are associated with better performance on some divergent-thinking measures. But a review of 31 studies did not find a consistent creativity advantage for people with clinical ADHD, and it found no evidence of better convergent thinking. Okay, I’m done… And nobody noticed that I’m not doing my job here.

evrydayhustling 12d ago

> weights update over time and past learning improves future learning such that we get better sample efficiency Is there an architecture-independent definition of forward transfer? For the practical experience and implications of AI progress, I think we are increasingly discussing what these LLMs can accomplish inside a stateful harness, the state of which could be described as part of a (very squirrely) parameter space.

lelanthran 12d ago

> That's what the AI's really are terrible at -- creativity. But I'd argue the vast majority of humans aren't very creative, with truly out-of-the-box ideas. Given that this is HN, and a non-trivial number of us have ADHD, creativity is positively correlated with ADHD. I think it's hard to define creativity in the context of AI because they seemingly just make up new hyphenated terms for everything. Is that creativity? If not, what about when they do the same thing different ideas in the latent space? If we say that simply nailing one concept to another isn't creativity, then AIs are incapable of creativity, while the vast majority of humans are incapable of creativity. This is just a long way of saying "0 AIs have creativity, 0.00001% of humans have creativity", and the difference between zero and a very small number is infinity.

datsci_est_2015 12d ago
It always comes back to taste. It’s not that they’re incapable of creativity, it’s that LLM-driven creativity is terrible, and nothing makes me cringe more than when it uses a word in a “novel” way. But monkeys-with-typewriters, they sometimes stumble upon something that doesn’t suck. But if you don’t want to spend a fortune retrying the same task until you get a suitable result you have to inject your own taste. Sometimes I start with a super vague prompt and see how close agents can get to something that doesn’t suck. I inevitably get frustrated about 6-7 prompts in when they’ve created a complete mess because they have no taste. So I restart and inject my taste into the process. Things like linters, test suites, which 3PLs to use, etc.
- fspeech 12d ago
  Taste or vision? They are hill climbing and they can't see the other side. And sometimes that is because they don't live in your head and don't know what you want.
  - clord 12d ago
    Very insightful actually. Taste and vision are both instances of holding a model that predicts a good result beyond the threshold of validity in theory but reality happens to align. Is it luck then? Perhaps meta-luck where lucky weights produce “accidentally great” results with some predictability. Steve Jobs had a mental model that brought the iPhone. No one really wanted it but something in his life biased the result. So creativity is having weights so good you can project way out into latent space beyond what is reasonable.

itissid 12d ago

Hi thanks for the insight. Do you see a role for Control Systems(i.e. ones analogus to Instrumentation engineering) playing a role to modulate certain parts of continual learning? One very important way we learn are lived experiences, it's like telling memory:this part is more important( for emotional or social utility values), pay attention. Good or bad lived experiences both count. I guess is that a path that practical research is considering?

heyjamesknight 12d ago

I think our AI systems are essentially massive Central Executive Networks. But novel ideas (creativity) come from the Default Mode Network. These are the difference in what Kahneman called System 2&1 thinking and what the ancients called the Ratio and the Intellect. LLMs are all ratio. They depend on our intellect for guidance.

mannanj 12d ago

What have you defined as creativity and intelligence?

corndoge 12d ago

> Given that this is HN, and a non-trivial number of us have ADHD, creativity is positively correlated with ADHD. Very strong reasoning here. Is there anything this ADHD condition cannot explain?

zxexz 12d ago
Socrates was a man. Socrates was creative. Ergo, Socrates is on HN. Ergo, we’re all dead.
- reverius42 9d ago
  Dead from ergo poisoning.
dev_dan_2 11d ago
It should obviously not come as a surprise that having areas of the brains working differently can explain an awfull lot of things... but to answer your question directly, yes of course of course there are! Simply look up all the many, well -supported and -researched known correlations with ADHD first. In the second step, you can construct the set of all possible correlations, and subtract the well-researched ones if it. What is left is the set of correlations that are either not explained by ADHD (the big majority I would assume) or explained by ADHD, but as-of-yet unknowingly so. This might be a bit anticlimatic, but clinical psychology is pretty straightforward study design and statistics, and set theory is not that new either, so... no big surprises I am afraid.
prpl 11d ago
Not sure the thrust of your comment, but what is really going on is just novelty seeking to get a bit of a dopamine rush, which slides into creativity. It all comes down to dopamine at the end of the day.

chermi 12d ago

"Given that this is HN, and a non-trivial number of us have ADHD, creativity is positively correlated with ADHD." Is this a statement of fact? As someone with ADHD and pretty confident in my creativity, I still believe this is much more cope than fact(which could be more a self-doubt thing than anything else). Further, if there is a correlation, I'd bet it's not so much an intrinsic "creativity" trait, but more effectively higher creativity because more trials. That is, along the lines of Chollet's paper, a measure of creativity should be based on a fixed budget with fixed knowledge. Among many other possibilities I haven't considered, perhaps another mechanism could be that because ADHD people spend more time thinking in less goal-oriented ways and mixing thoughts on accident, perhaps we do in fact gain some learned creativity via experience with vagueness[1]? But that might also imply that part of creativity is actually being able to diffuse more freely through thought space and lowering the barrier to attempted connections between ideas. That lower barrier leads to less likelihood of any "collision" being meaningful but maybe it's overcome by higher collision rates? Or maybe effectively higher order (not just pairwise) collisions? Disclaimer in case it's not obvious: I don't know any of the literature on what creativity even means or how it's quantified. [1] Which is me injecting an assumption that creativity ~= connecting things with no obvious or well-troden reasoning path between them. edit -- oops just looked at your profile after seeing someone elses comment. I assume you are stating a fact then, leaving original anyway

r_lee 12d ago
I think it's just that many of us don't have the capability to just do things in rote or the conventional ways, instead we must rely more on the creative and unconventional ways of doing things and parts of the brain responsible for that

astrobiased 12d ago

Appreciate the input! Responses below to your first two items: 1) I'm leaning more into a broader sense, which is that given the priors a system already possesses, how efficient can it acquire competence on a novel task? If I'm reading the point you make, you're focussing on continual learning right? If so, I'm not necessarily restricting my statement above to that. Here's another reframing: How much of the benchmark improvements come from overwhelmingly large training distributions vs improving the models for adapting to things genuinely outside of it? 2) Excellent points about crystalized and fluid intelligence. Wouldn't the LLM scaling gains be a representation of crystallized capabilities? In regard to Gf, that is exactly what I am asking about. That is what seems to be lacking, Gf like adaptation under genuine novelty. My concern is that it's increasingly difficult to tell of what looks like Gf like behavior is really coming from better adaption vs. having broad priors from the model's large learned distributions.

gjvc 11d ago

> Given that this is HN, and a non-trivial number of us have ADHD, creativity is positively correlated with ADHD. big pharma must LOVE people like you parroting the ADHD bullshit all the time