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).
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.
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.
> 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.
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.
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.
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.
Nice to get some resonance. And if I may, past --> taste; vision --> future. And to compensate for our poor memory, taste<=>value network while vision<=>policy network, borrowing from RL parlance.
I hope you’re not actually trying to compare human cognitive processes to an LLM, because that would be incredibly reductive, not to mention lacking in any empirical grounding.
> 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.
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.
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
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?
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.
> 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.
"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
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
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.
For me a basic test of A.I. creativity is give the A.I. chapter 1 of a novel it has not seen and ask it to write chapter 2, then compare the quality to the original author's chapter 2. The A.I. is always terrible at this in terms of matching the author's level of quality.
I would think that’s the opposite of creativity. Rather, I would label that as pattern matching and prediction capability.
Why box in creativity basically as the ability to mimic someone else who is creative? Why not choose something that better matches the definition of creativity (the ability to make new things, think of original ideas, or show imagination that is novel, useful, or pleasing)?
And even then, I am assuming the premise that the original novels are good and creative, especially chapter 2. Most novels are not very creative.
If it could make 5 different versions of chapter 2, all with different directions for the story, and all with novel and interesting developments, wouldn’t that be a better definition of creativity than “can it read chapter 1 and be able to copy style and deduce/predict what the author is going to do in chapter 2”?
And also, that’s only a subset of creativity (storytelling). I’ve met plenty of people who are terrible at writing and storytelling but can come up with the most impressive and novel solutions to a practical problem instantly.
Final thought: I have been following the development in the anime AI generation scene for a while now. It’s not even close to anything anyone would call creative or even OK quality. But it’s also massively impressive that it’s moving in that direction really fast. And if you watch enough, you are going to start to see some truly creative sparks. And some of the mainstream stuff that gets created and labeled as creative… really... How many isekai series with the same story have humans not made already?
> If it could make 5 different versions of chapter 2, all with different directions for the story, and all with novel and interesting developments, wouldn’t that be a better definition of creativity than “can it read chapter 1 and be able to copy style and deduce/predict what the author is going to do in chapter 2”?
If I like the novel, an alternative version where things happen differently would be fine.
The problem isn't that it's different. The problem is quality.
A lot of Isekai stories are crap but you can still rank them in terms of the author's ability or inability to have a creative POV that elevates the material.
I think “what I individually like” is a poor measurement of creativity, at least alone.
And yes you can rank quality. My point was that if you made two bell curves of the distribution quality and creativity of all new manga, The one for “AI slop manga” allready started overlapping with “normal human manga”.
That’s just a fancy way of saying the absolute best AI slop is at the level of the absolute worst human creation.
The interesting thing is that the AI slop curve clearly is moving to the right every month. Where it will stop tho, impossible to say.
Is chapter 1 of a novel really enough context to generate a chapter 2 of sufficient quality to match up to an author who spent a good amount of time planning out an entire story and whose manuscript probably went through a lot of revisions?
I don't necessarily just feed it chapter 1. (And chapter length varies between novels).
And no I don't think asking the A.I. to write the next 2000 words would require it to have the entire novel planned out. Not all writers even outline in advance.
I'm an AI professor, and the same thing happened to me. I had written out contracts and specs for some novel algorithms to compare. And instead it created a new sandboxed environment with 20k lines of code to ensure we do gold standard science.... didn't implement the algorithms at all. In another case, I left it overnight with a plan, and it decided to write a 50k line new overly engineered plan. Suffice to say, I don't use any frontier models for doing coding work anymore. I use them to make detailed plans and use good worker AIs for execution, which don't overthink everything.
The average hatching egg costs a few dollars. Can go as high as nearly $20 if it's something remarkably rare and useful in terms of breed/quality.
An egg takes 21 days to hatch.
Then it's 18-24 weeks to start laying their own eggs. That's ~$5-15 in feed over the period, maybe 40-50 pounds. There's utilities, labor, land, etc., but your incremental cost to add a hen if you have the space is minimal. My numbers won't reflect a lot of operators, as scale matters.
Out of a typical 1.5-3 year lifespan that had a startup cost of a few dollars per, paying back its return by about month 6-7, that's not too bad. It's only worth doing financially in this economy at ludicrous, arguably inherently inhumane scales, but that's what it is on paper.
Not quite the same path to course-correction as a silicon shortage.
Eventually 2028 and 2029 will come around and at some point today's data center builds will be (economically) full.
In addition to that there are extra fabs build and (for example) China can use the high margin to catch up with their processes without breaking the bank.
And all those reports of Claude when asked without a system prompt what its name was in Chinese it often would say Qwen or Deepseek, etc. I'd love Anthropic to say they aren't distilling and taking from every model out there, because I'm sure they are. As my mom would say, "the pot calling the kettle black." At least Alibaba and other Chinese companies are giving back to the AI community with detailed scientific papers on how their systems work and releasing open-weight or opensource models. I believe Anthropic has released nothing, and given that they had originally configured Fable to sabotage ML related work because only they can be trusted to do it safely, is just anti-science and anti-aligned with what I would consider good human values. They are way too sanctimonious and I don't trust them at all.
They are doing ta tremendous amount of novel research where American AI companies have "war rooms" to study their papers and models and American labs publish next to nothing. They have to often do more with less. As an AI researcher, Chinese labs are doing tremendous benefit to science whereas some American companies (and I'm American) seem to think only they are able to do AI research responsibility (I've been working on neural networks for 25+ years). I'm pretty sure Fable sabotaged my research codebase (see the news stories about this).
Did you ever watch Star Trek: The Next Generation? The current trajectory is like the Ship's Computer. It know everything humanity has learned and can do a lot. But it can't explore and lacks desires and agency. That's why they made a big deal about the character Data being an entirely new kind of AI. Of course Star Trek has a very different economic system and there is a book called Trekenomics about that. So optimistically people live for themselves and don't persue labor they despise. Half of Americans hate their jobs and live for the dream of retirement when they get to actually do what they want.... But they don't have the same energy anymore.
Jobs are an invention of humanity. About 50% of people dislike their job. People spend much of their lives working. Poverty and inequality are a choice made by society if society chooses poorly.
On the plus side, if there really is no value to labour, then farm work must have been fully automated along with all the other roles.
On the down side, rich elites have historically had a very hard time truly empathising with normal people and understanding their needs even when they care to attempt it, so it is very possible that a lot of people will starve in such a scenario despite the potential abundance of food.
It's either:
1) the rich voluntarily share the means of production so everyone becomes equal,
2) the poor stage successful revolutions so they gain access to the means of production and everyone becomes equal,
3) the poor starve or are otherwise eliminated, and the survivors will be equal.
All roads lead to equality when the value of labour becomes 0 due to 100% automation.
Over history, lots of underclasses have been stuck that way for multiple generations, even without the assistance of a robot workforce that can replace them economically.
Some future rich class so empowered would be quite capable of treating the poor like most today treat pets. Fed and housed, but mostly neutered and the rest going through multiple generations of selective inbreeding for traits the owners deem interesting.
Non-human pets don't have the capacity to rebel though; make humans into pets and there will again be the constant danger of rebellions as with slavery in the past. Without the economic incentive to offset.
On the first, non-human pets rebelling is seen every time an abused animal bites their owner.
On the second, the hypothetical required by the scenario is that AI makes all human labour redundant: that includes all security forces, but it also means the AI moving around the security bots and observing through sensors is at least as competent as every human political campaign strategist, every human propagandist, every human general, every human negotiator, and every human surveillance worker.
This is because if some AI isn't all those things and more, humans can still get employed to work those jobs.
Not at all. A rebellion is an organized effort, with an implicitly delayed response to grievances. I can't think of any non-humans that organize their efforts as such. It would be a heck of a thing if a group of dogs were to plan how they'd take out their masters.
All those "jobs" you describe - and many more - would cease to be a thing, as their purported basis for existence would be no more. Any role that doesn't concretely contribute to our survival and advancement is just "busy work". People could theoretically continue to maintain some simulation of something that keeps them as a retirement, but it'd be meaningless.
> Not at all. A rebellion is an organized effort, with an implicitly delayed response to grievances. I can't think of any non-humans that organize their efforts as such. It would be a heck of a thing if a group of dogs were to plan how they'd take out their masters.
Dogs in particular are pack animals, self-organisation amongst them wouldn't be at our level but that doesn't mean it doesn't exist.
> All those "jobs" you describe - and many more - would cease to be a thing, as their purported basis for existence would be no more. Any role that doesn't concretely contribute to our survival and advancement is just "busy work". People could theoretically continue to maintain some simulation of something that keeps them as a retirement, but it'd be meaningless.
Yes?
I think you've missed the point, though.
When your opponent has all those skills to that level and doesn't sleep and simply applies all the surveillance tech that has already been invented like laser microphones and wall-penetrating radar that can monitor your pulse and breathing, how would you manage to rebel?
How would you find a like mind to organise with, when your opponent knows what you said marginally before the slow biological auditory cortex of the person you're talking to passes the words to their consciousness? Silicon is already that fast at this task.
And that's assuming you even want to. Propaganda and standard cult tactics observably prevent most rebellions from starting. LLMs are already weirdly effective at persuading a lot of people to act against their own interests.
> The question is, to what extent would humans still set goals and priorities, and how.
From what I hear about the US and UK governments, even the elected representatives of these governments don't really set goals and priorities, so the answer is surely "humans don't".
I get your point, but I’d say they do set goals, they’re just do bad at achieving them that it’s hard to tell.
Hopefully AI would help us better achieve our goals, but they still need to be our goals. I’m just not sure what that means. I don’t think anybody does.
That’s a major problem here, if we can’t reliably articulate our goals in unambiguous terms, how in earth can we expect AI to help us achieve them? The chances that whatever they end up achieving will match what we will actually like after the fact seems near zero.
I'd say Maslow's hierarchy[0] is a great starting point. Program that properly and faithfully (no backdoors, military exceptions, etc whatsoever) along with Asimov's 3 laws[1] and it should be pretty hard to find issue with the system that would result.
This is the "draw the rest of the owl"* of the alignment problem.
Or possibly the rest-of-owl of AI in general: Consider that there's still no level-5 self driving cars, despite road traffic law existing and the developers knowing about it since before they started trying.
The film version of I Robot had this right, the three laws are a manifesto for totalitarianism. The AI cannot sit on the sidelines as long as there is anything it can do to prevent crimes or abuse of any kind, no matter how intrusive that intervention may be.
If truly 100% automation (including infantry/police) the most likely scenario is not any if the above; most people will be kept on some kind of minimum sustenance enough to keep them from rebelling (“UBI”) and those who disagree will either be coopted into the elite or eliminated.
There's no reason to keep anyone on minimal sustenance though. They're absolutely useless alive from an economics perspective, and so would probably be better served ground up into fertilizer or some other actually useful form.
> They're absolutely useless alive from an economics perspective, and so would probably be better served ground up into fertilizer or some other actually useful form.
Indeed. "The AI does not hate you, nor does it love you, but you are made out of atoms which it can use for something else."
But while some may care about disassembling this world and all non-rich-human life on it to make a Dyson swarm of data centres, there's also the possibility each will compete for how many billions of sycophants they can get stoking their respective egos.
In 1, 2 and 3, any progress stops because no one is making new means of production, so we must stop population from growing. No? Who’s building the factories or whatever those means of production are?
In the hypothetical where humans can no longer be employed because of AI, it is necessarily the case that AI must be able do any job at least as well as the best human for that job. That includes building factories, doing research.
Humans reproduce, there is no requirement that even destruction and death would lead to equality, not even if the elites still put themselves close enough to the rest of us as to be attackable.
For the latter point, consider that no matter how much the people of North Sentinel Island hate outsiders, they're not going to pose any risk to the rest of us.
Now, an elite whose membership includes those who want equality for the rest of us, that may create conditions for such a rebellion to succeed, but absent such from an insider (which could be encoded into the AI via either a bug or deliberately from whoever created the AI), some elite whose defence is handled by the kind of AI under consideration would not face any more of a threat from the wider population than we here in the west today face from the North Sentinel Islanders.
Note however that I'm not saying what will happen, but what is possible in various conditions. There's no guarantee of anything at this point.
Is that true? In communities or tribes of antiquity I assume there was some trading fruits of different labours before coinage. Still an 'invention' beyond baser individual survivalism.
Many (most?) people make a living from their job whether they like it or not. Having a job that they dislike is far better than losing one because of AI whatever that means.
Could also be possible today, but we chose a capitalistic system that leads to an increasing wealth gap. And now we're in a situation where the richest 1% own 50% of the wealth.
So, if we increase automation and the ownership structures stay the same, this inequality will get worse, not better.
It’s interesting, people talk about inequality and I definitely feel it myself – I see so many rich people around me. But I am in that 1%, just like many on this forum. At least according to https://dqydj.com/average-median-top-individual-income-perce... yet I still have to work for a living.
> The cost will exponentially increase over time and the systen will eventually collapse.
From what I'm seeing in the numbers, the big problem of the coming century is population collapse. Maybe I'm just too much of a believer in the intermediate value theorem, but I'm sure there has to be a way to arrive at a society with a sustainable usage of resources.
Nope. If everything is totally automated, if ever, the gap between the rich and the poor will widen even more. Most people will live in misery while only a handful of people enjoy all the automation.
> Having a job that they dislike is far better than losing one because of AI whatever that means.
Is it really worse even if "whatever it means" is living in a post-scarcity society where everyone can shareel in the fruits of the AI's labor?
I'm not saying that's where this are necessarily going. But I am saying that that's what we should be aiming for, rather than trying to preserve the status quo.
The only thing invented about jobs is that through cooperation, the activity undertaken can seem completely unrelated to obtaining food, shelter etc. All organisms spend a majority of their energy on survival and reproduction.
Every biological being works to survive. Being good at survival is what builds self esteem.
The "problem" with many modern jobs is that they're divorced from the fundamental goal, which is one of: 1) Kill/acquire food, 2) Build shelter, or 3) Kill enemies/competitors/predators
The benefit of modern jobs is that they are much more peaceful ways for society to operate, freeing up time for humans to pursue art and other forms of expression.
What he got wrong was that this alienation results from capitalism.
It actually results from civilization. The people who built the pyramids across every continent, for example, performed assembly line-like work. Any large-scale project requires it. And large-scale projects are fundamentally necessary for most societies.
For the pyramids specifically: their architects and builders were skilled artisans who got to own their craft from top to bottom. As such, they were well-paid and pretty respected. Very much not alienated, under Marx's definition.
I don't think Marx said that worker alienation was specific to capitalism, rather, his work was in describing the economic system of his time, and what that would entail for people living in it.
> It actually results from civilization.
I disagree, I can't think of anyone in Medieval Europe as alienated from their work as a modern sweatshop worker. Not that serfs had it better, but you get me.
The pyramids took 20k+ people to build, which inevitably requires division of labor/specialization. Some chunk of that population had to mine the copper, which was probably an absolutely terrible job with ancient technology.
Serfs were essentially slaves who had effectively 0 ownership over their output, so I'd strongly disagree with that sentiment.
I think the best argument for a time when there was almost 0 alienation of labor was when we were all hunter gatherers. Where every activity was closely connected to something necessary for survival.
As soon as we built larger societies, greater division of labor became necessary to efficiently support the society. And thus alienation of labor became much more pronounced.
And when have we not? When in history has mankind ever treated the idle poor well? What makes this age different, that we who can no longer work would be taken care of?
Well we're animals and "domesticated" is synonymous with "civilized", so no problem there. And I can't see why anyone would make themselves a "nuisance" when literally all their needs - and most of their desires - are being met, so whatever outcome you're referring to is extremely unlikely.
Slightly more nuanced in that the reciprocal reviewer may have been essentially forced to sign despite having other commitments or may not have even been the lead contributor. Nowadays if a student submits a side project to a top-tier conference then it is required that if any authors have significant publication count in top-tier venues, then one must be a mandatory reviewer. Then one must sign that agreement. Students need to publish, much less so for me, where I really want to publish big innovations rather than increments, but now I get all these mandatory reviewer emails demanding I review for a conference because a student has my name on the paper and I'm the most senior, but I may have just seeded the idea or helped them in significant ways. However, many times those are not my passion projects and is just something a student did that I helped with, but now all AI conferences are demanding I review or hurt a student, where I'm the middle author.
But if anything, I think the whole anti-LLM review philosophy is wrong. If anything we need multiple deep background and research analyses of papers. So many papers are trash or are publishing what has already been done or are missing things. The volume of AI papers makes it impossible for a human alone to really critique work because hundreds of new papers come out a day.
I keep not learning how corrupt authorship of academic papers is. When I read papers, I imagine all the authors have been working away together in an office somewhere and they all wrote parts of the paper and all read it and all have a feeling of ownership of it and deeply understand the whole thing. But I forget how the only academic paper I ever had published was one that I never read and had no understanding of. All I did was give some technician-like advice to the actual author. It feels dirty and I sometimes regret accepting it but at the same time, the whole science world seems like it doesn't deserve honesty because everyone else is corrupt too.
Not hard to see why. Being an author helps your cv. Allowing you to be an author for tangential or minimal contribs can help keep good relations, especially if there are future options and financial things depending on having good relations. Putting a name on a paper costs nothing and nobody checks how big the contribution was. It's slightly dilutes the subjective authorship fraction of those who did the work, but sometimes the additional person also brings in a nice prestigious affiliation that even has a positive impact on how seriously the paper is taken... It's a game.
I had lunch with Yann last August, about a week after Alex Wang became his "boss." I asked him how he felt about that, and at the time he told me he would give it a month or two and see how it goes, and then figure out if he should stay or find employment elsewhere. I told him he ought to just create his own company if he decides to leave Meta to chase his own dream, rather than work on the dream's of others.
That said, while I 100% agree with him that LLM's won't lead to human-like intelligence (I think AGI is now an overloaded term, but Yann uses it in its original definition), I'm not fully on board with his world model strategy as the path forward.
You have to understand the strategy of all the other players:
Build attention-grabbing, monetizable models that subsidize (at least in part) the run up to AGI.
Nobody is trying to one-shot AGI. They're grinding and leveling up while (1) developing core competencies around every aspect of the problem domain and (2) winning users.
I don't know if Meta is doing a good job of this, but Google, Anthropic, and OpenAI are.
Trying to go straight for the goal is risky. If the first results aren't economically viable or extremely exciting, the lab risks falling apart.
This is the exact point that Musk was publicly attacking Yann on, and it's likely the same one that Zuck pressed.
There's two points here. The first is that a strategy of monetizing models to fund the goal of reaching AI is indistinguishable from just running a business selling LLM model access, you don't actually need to be trying to reach AGI you can just run an LLM company and that is probably what these companies are largely doing. The AGI talk is just a recruiting/marketing strategy.
Secondly, it's not clear that the current LLMs are a run up to AGI. That's what LeCun is betting - that the LLM labs are chasing a local maxima.
I mean, Sutskevar and Carmack are trying to one-shot AGI. We just don't talk about them as much as we do the labs with products because their labs aren't selling products.
I can see some promise with diffusion LLMs, but getting them comparable to the frontier is going to require a ton of work and these closed source solutions probably won't really invigorate the field to find breakthroughs. It is too bad that they are following the path of OpenAI with closed models without details as far as I can tell.
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).