Not learning from new input may be a feature. Back in 2016 Microsoft launched one that did, and after one day of talking on Twitter it sounded like 4chan.[1] If all input is believed equally, there's a problem.
Today's locked-down pre-trained models at least have some consistency.
Incredible to accomplish that in a day - it took the rest of the world another decade to make Twitter sound like 4chan, but thanks to Elon we got there in the end.
This has little to do with the bot, and everything with this being the heyday of Twitter shitstorms; we didn't have any social immunity to people getting offended about random things on-line, and others getting recursively offended, and then "adults" in news publishing treating that seriously and converting random Twitter pileups into stock movements.
In a decade since then, things got marginally better, and such events wouldn't play out so fast and so intensely in 2026.
I think models should be “forked”, and learn from subsets of input and themselves. Furthermore, individuals (or at least small groups) should have their own LLMs.
Sameness is bad for an LLM like it’s bad for a culture or species. Susceptible to the same tricks / memetic viruses / physical viruses, slow degradation (model collapse) and no improvement. I think we should experiment with different models, then take output from the best to train new ones, then repeat, like natural selection.
And sameness is mediocre. LLMs are boring, and in most tasks only almost as good as humans. Giving them the ability to learn may enable them to be “creative” and perform more tasks beyond humans.
People have tried to suss this out on the ML subreddit, and it is confusing. Most of the worst messages from Tay were just people discovering a "repeat after me: __" function, so it's hard just to figure out which Tay messages to consider as responses of the model.
There seems to have been interest in a model which would pick up language and style of its conversations (not actually learning information or looking up facts). If you haven't trained an LSTM model before - you could train on Shakespeare's plays and get out ye olde English in a screenplay format, but from line to line there was no consistency in plot, characters, entrances and exits, etc. in a way which you'd expect after GPT-2. Twitter would be good for keeping a short-form conversation. So I believe Tay and the Watson that appeared on Jeopardy are more from this 'classical NLP' thinking and not proto-LLMs, if that makes sense.
Exactly. The notion of online learning is not new, but that approach cedes a lot of control to unknown forces. From a theoretical standpoint, this paper is interesting, there are definitely interesting questions to explore about how we could make an AI that learns autonomously. But in most production contexts, it's not desirable.
Imagine deploying a software product that changes over time in unknown ways -- could be good changes, could be bad, who knows? This goes beyond even making changes to a live system, it's letting the system react to the stream of data coming in and make changes to itself.
It's much preferable to lock down a model that is working well, release that, and then continue efforts to develop something better behind the scenes. It lets you treat it more like a software product with defined versions, release dates, etc., rather than some evolving organism.
> Back in 2016 Microsoft launched one that did, and after one day of talking on Twitter it sounded like 4chan.[1] If all input is believed equally, there's a problem.
Well it shows that most humans degrades into 4chan eventually. AI just learned from that. :)
If aliens ever arrive here, send an AI to greet them. They will think we are totally deranged.
Ugh HN is so tedious with these remarks. These people are trying to get computers to learn, not just train on data, and HN goes nOt LeArNiNg Is A fEaTuRe. Where's the wonder and the curiosity?
This is an astonishing claim and if true, will make AI a lot less useful in real life scenario.
In real life, take programming as an example, we want Claude to be strong in capability at first, but what is more important is for it to learn our code base, be proficient in it, as it gains experience around it. In other words, become a domain expert.
Because our code base is proprietary I don't expect ( not do I want) the AI to be familiar with it on the first day. So learning on the job is the only way to go.
Only in that way it will resemble a human programmer, and only then we can truly talk about replacing human programmer.
Learning is OpenClaw's distinguishing feature. It has an array of plugins that let it talk to various services - but lots of LLM applications have that.
What makes it unique is it's memory architecture. It saves everything it sees and does. Unlike an LLM context its memory never overflows. It can search for relevant bits on request. It's recall is nowhere near as well as the attention heads of an LLM, but apparently good enough to make a difference. Save + Recall == memory.
I just had to reply because this is one of the most important things to me and I didn't put it in words before but you said it perfectly. Down to the Google example which is the one always on my mind. Humans really are all the same.
Yeah deep learning treats any training data as the absolute god given ground truth and will completely restructure the model to fit the dumbest shit you feed it.
The first LLMs were utter crap because of that, but once you have just one that's good enough it can be used for dataset filtering and everything gets exponentially better once the data is self consistent enough for there to be non-contradictory patterns to learn that don't ruin the gradient.
It’s interesting, LeCun seems to have a blind spot around in-context learning. I didn’t find one mention in this paper (only skimmed the full paper so far so may have missed), which is odd as it is the way that agents come closest to autonomous learning in the real world.
I would say his core point does still apply; autonomous learning is not solved by ICL. But it seems a strawman to ignore the topic entirely and focus on training.
From what I see on the ground, some degree of autonomous learning is possible; Agents can already be set up to use meta-learning skills for skill authoring, introspection, rumination, etc - but these loops are not very effective currently.
I wonder if this is the myopic viewpoint of a scientist who doesn’t engage with the engineering of how these systems are actually used in the real world (ie “my work is done once Llama is released with X score on Y eval”) which results in a markedly different stance than the guys like Sutskever, Karpathy, Amodei who have built end-to-end systems and optimized for customer/business outcomes.
Has anyone tried implementing something like System M's meta-control switching in practice? Curious how you'd handle the reward signal for deciding when to switch between observation and active exploration without it collapsing into one mode.
> Curious how you'd handle the reward signal for deciding when to switch between observation and active exploration without it collapsing into one mode.
If you like biomimetic approaches to computer science, there's evidence that we want something besides neural networks. Whether we call such secondary systems emotions, hormones, or whatnot doesn't really matter much if the dynamics are useful. It seems at least possible that studying alignment-related topics is going to get us closer than any perspective that's purely focused on learning. Coincidentally quanta is on some related topics today: https://www.quantamagazine.org/once-thought-to-support-neuro...
The question is does this eventually lead us back to genetic programming and can we adequately avoid the problems of over-fitting to specific hardware that tended to crop up in the past?
Or possibly “in addition to”, yeah. I think this is where it needs to go. We can’t keep training HUGE neural networks every 3 months and throw out all the work we did and the billions of dollars in gear and training just to use another model a few months.
That loops is unsustainable. Active learning needs to be discovered / created.
"he proposed framework integrates learning from observation (System A) and learning from active behavior (System B) while flexibly switching between these learning modes as a function of internally generated meta-control signals (System M). We discuss how this could be built by taking inspiration on how organisms adapt to real-world, dynamic environments across evolutionary and developmental timescales. "
If this was done well in a way that was productive for corporate work, I suspect the AI would engage in Machievelian maneuvering and deception that would make typical sociopathic CEOs look like Mister Rogers in comparison. And I'm not sure our legal and social structures have the capacity to absorb that without very very bad things happening.
I was kind of worried by them going Machiavellian or evil but it doesn't seem the default state for current ones, I think because they are basically trained on the whole internet which has a lot of be nice type stuff. No doubt some individual humans my try to make them go that way though.
I guess it would depend a bit whos interests the AI would be serving. If serving the shareholders it would probably reward creating value for customers, but if it was serving an individual manager competing with others to be CEO say then the optimum strategy might be to go machiavellian on the rivals.
Not just CEOs, Legal and social structures will also be run by AI. Chimps with 3 inch brains cant handle the level of complexity global systems are currently producing.
> If this was done well in a way that was productive for corporate work, I suspect the AI would engage in Machievelian maneuvering and deception that would make typical sociopathic CEOs look like Mister Rogers in comparison.
Algorithms do not possess ethics nor morality[0] and therefore cannot engage in Machiavellianism[1]. At best, algorithms can simulate same as pioneered by ELIZA[2], from which the ELIZA effect[3] could be argued as being one of the best known forms of anthropomorphism.
Agents playing the iterated prisoner's dilemma learn to cooperate. It's usually not a dominant strategy to be entirely sociopathic when other players are involved.
In this podcast episode[0] he does talk about this kind of model and how it "learns about physics" through experience instead of just ingesting theorical material.
The way I see it, the "world models" he wants to train require a magnitude more compute than what LLM training requires since physical data is likely much more unstructured than internet data.
He raised $1b but that seems way too little to buy enough compute to train.
My bet is that OpenAI or Anthropic or both will eventually train the model that he always wanted because they will use revenue from LLMs to train a world model.
The whole AI field is a misnomer. It stole so much from neurobiology.
However had, there will come a time when AI will really learn. My prediction is that it will come with a different hardware; you already see huge strides here with regards to synthetic biology. While this focuses more on biology still, you'll eventually see a bridging effort; cyborg novels paved the way. Once you have real hardware that can learn, you'll also have real intelligence in AI too.
I remember a joke from few years ago that was showing an "AI" that was "learning" on its "own" which meant periodically starting from scratch with a new training set curated by a large team of researchers themselves relying on huge teams (far away) of annotators.
TL;DR: depends where you defined the boundaries of your "system".
But doesnt existing AI systems already learn in some way ? Like the training steps are actually the AI learning already. If you have your training material being setup by something like claude code, then it kind of is already autonomous learning.
Most, if not all, commercially available AI models are doing offline learning. The cognition is a skill that is only possible on online learning which is the autonomous part the authors refer to, that is, learning by observing, interacting.
In that sense the "autonomous" part you said simply meant that the data source is coming from a different place, but the model itself is not free to explore with a knowledge base to deduce from, but rather infer on what is provided to it.
> The cognition is a skill that is only possible on online learning which is the autonomous part the authors refer to, that is, learning by observing, interacting.
This is the "Claude Code" part, or even the ChatGPT (web interface/app) part. Large context window full of relevant context. Auto-summarization of memories and inclusion in context. Tool calling. Web searching.
If not LLMs, I think we can say that those systems that use them in an "agentic" way perhaps have cognition?
The paper's critique of the 'data wall' and language-centrism is spot on. We’ve been treating AI training like an assembly line where the machine is passive, and then we wonder why it fails in non-stationary environments. It’s the ultimate 'padded room' architecture: the model is isolated from reality and relies on human-curated data to even function.
The proposed System M (Meta-control) is a nice theoretical fix, but the implementation is where the wheels usually come off. Integrating observation (A) and action (B) sounds great until the agent starts hallucinating its own feedback loops. Unless we can move away from this 'outsourced learning' where humans have to fix every domain mismatch, we're just building increasingly expensive parrots. I’m skeptical if 'bilevel optimization' is enough to bridge that gap or if we’re just adding another layer of complexity to a fundamentally limited transformer architecture.
I've tried figuring out what the big deal about cybernetics was, but I always come away with a feeling of it being a bit wish-washy. Is it a bit like Philosophy in that it birthed individual fields that were inspired by and made applications of the thoughts, models and ideas laid out by its forebears? Or were there actual proofs, discoveries or applications in the field itself?
Eh, honestly? We're not that far away from models training themselves (opus 4.6 and codex 5.3 were both 'instrumental' in training themselves).
They're capable enough to put themselves in a loop and create improvement which often includes processing new learnings from bruteforcing. It's not in real-time, but that probably a good thing if anyone remembers microsofts twitter attempt.
I was thinking in the same way that the human brain's design came about from evolutionary trial and error, we may be close to a situation where we can do something like that for the artificial neural networks and have the computers improve them by fiddling about.
What I find interesting is the supposition that weights must change.
The connections of my motherboard do not change, yet it can simulate any system.
Perhaps there is an architecture that is write-once-read-forever, and all that matters is context.
There's almost certainly some of this in the human mind, and I bet there is much more of it than we are willing to admit. No amount of mental gymnastics is going to let you visualize 6D structures.
Not learning from new input may be a feature. Back in 2016 Microsoft launched one that did, and after one day of talking on Twitter it sounded like 4chan.[1] If all input is believed equally, there's a problem.
Today's locked-down pre-trained models at least have some consistency.
[1] https://www.bbc.com/news/technology-35890188
Incredible to accomplish that in a day - it took the rest of the world another decade to make Twitter sound like 4chan, but thanks to Elon we got there in the end.
This has little to do with the bot, and everything with this being the heyday of Twitter shitstorms; we didn't have any social immunity to people getting offended about random things on-line, and others getting recursively offended, and then "adults" in news publishing treating that seriously and converting random Twitter pileups into stock movements.
In a decade since then, things got marginally better, and such events wouldn't play out so fast and so intensely in 2026.
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I think models should be “forked”, and learn from subsets of input and themselves. Furthermore, individuals (or at least small groups) should have their own LLMs.
Sameness is bad for an LLM like it’s bad for a culture or species. Susceptible to the same tricks / memetic viruses / physical viruses, slow degradation (model collapse) and no improvement. I think we should experiment with different models, then take output from the best to train new ones, then repeat, like natural selection.
And sameness is mediocre. LLMs are boring, and in most tasks only almost as good as humans. Giving them the ability to learn may enable them to be “creative” and perform more tasks beyond humans.
That one 4chan troll delayed the launch of LLM like stuff by Google for about 6 years. At least that's what I attribute it to.
I was always curious about how Tay worked technically, since it was build before the Transformers era.
Was it based on a specific scientific paper or research?
The controversy surrounding it seemed to have polluted any search for a technical breakdown or a discussion, or the insights gained from it.
People have tried to suss this out on the ML subreddit, and it is confusing. Most of the worst messages from Tay were just people discovering a "repeat after me: __" function, so it's hard just to figure out which Tay messages to consider as responses of the model.
There seems to have been interest in a model which would pick up language and style of its conversations (not actually learning information or looking up facts). If you haven't trained an LSTM model before - you could train on Shakespeare's plays and get out ye olde English in a screenplay format, but from line to line there was no consistency in plot, characters, entrances and exits, etc. in a way which you'd expect after GPT-2. Twitter would be good for keeping a short-form conversation. So I believe Tay and the Watson that appeared on Jeopardy are more from this 'classical NLP' thinking and not proto-LLMs, if that makes sense.
https://blogs.microsoft.com/blog/2016/03/25/learning-tays-in...
https://arxiv.org/abs/1812.08989
Exactly. The notion of online learning is not new, but that approach cedes a lot of control to unknown forces. From a theoretical standpoint, this paper is interesting, there are definitely interesting questions to explore about how we could make an AI that learns autonomously. But in most production contexts, it's not desirable.
Imagine deploying a software product that changes over time in unknown ways -- could be good changes, could be bad, who knows? This goes beyond even making changes to a live system, it's letting the system react to the stream of data coming in and make changes to itself.
It's much preferable to lock down a model that is working well, release that, and then continue efforts to develop something better behind the scenes. It lets you treat it more like a software product with defined versions, release dates, etc., rather than some evolving organism.
> Back in 2016 Microsoft launched one that did, and after one day of talking on Twitter it sounded like 4chan.[1] If all input is believed equally, there's a problem.
Well it shows that most humans degrades into 4chan eventually. AI just learned from that. :)
If aliens ever arrive here, send an AI to greet them. They will think we are totally deranged.
> Not learning from new input may be a feature.
Ugh HN is so tedious with these remarks. These people are trying to get computers to learn, not just train on data, and HN goes nOt LeArNiNg Is A fEaTuRe. Where's the wonder and the curiosity?
This is an astonishing claim and if true, will make AI a lot less useful in real life scenario.
In real life, take programming as an example, we want Claude to be strong in capability at first, but what is more important is for it to learn our code base, be proficient in it, as it gains experience around it. In other words, become a domain expert.
Because our code base is proprietary I don't expect ( not do I want) the AI to be familiar with it on the first day. So learning on the job is the only way to go.
Only in that way it will resemble a human programmer, and only then we can truly talk about replacing human programmer.
> Not learning from new input may be a feature.
Learning is OpenClaw's distinguishing feature. It has an array of plugins that let it talk to various services - but lots of LLM applications have that.
What makes it unique is it's memory architecture. It saves everything it sees and does. Unlike an LLM context its memory never overflows. It can search for relevant bits on request. It's recall is nowhere near as well as the attention heads of an LLM, but apparently good enough to make a difference. Save + Recall == memory.
Yes I like that /clear starts me at zero again and that feels nice but I am scared that'll go away.
Like when Google wasn't personalized so rank 3 for me is rank 3 for you. I like that predictability.
Obviously ignoring temperature but that is kinda ok with me.
I just had to reply because this is one of the most important things to me and I didn't put it in words before but you said it perfectly. Down to the Google example which is the one always on my mind. Humans really are all the same.
Yeah deep learning treats any training data as the absolute god given ground truth and will completely restructure the model to fit the dumbest shit you feed it.
The first LLMs were utter crap because of that, but once you have just one that's good enough it can be used for dataset filtering and everything gets exponentially better once the data is self consistent enough for there to be non-contradictory patterns to learn that don't ruin the gradient.
It’s interesting, LeCun seems to have a blind spot around in-context learning. I didn’t find one mention in this paper (only skimmed the full paper so far so may have missed), which is odd as it is the way that agents come closest to autonomous learning in the real world.
I would say his core point does still apply; autonomous learning is not solved by ICL. But it seems a strawman to ignore the topic entirely and focus on training.
From what I see on the ground, some degree of autonomous learning is possible; Agents can already be set up to use meta-learning skills for skill authoring, introspection, rumination, etc - but these loops are not very effective currently.
I wonder if this is the myopic viewpoint of a scientist who doesn’t engage with the engineering of how these systems are actually used in the real world (ie “my work is done once Llama is released with X score on Y eval”) which results in a markedly different stance than the guys like Sutskever, Karpathy, Amodei who have built end-to-end systems and optimized for customer/business outcomes.
Has anyone tried implementing something like System M's meta-control switching in practice? Curious how you'd handle the reward signal for deciding when to switch between observation and active exploration without it collapsing into one mode.
> Curious how you'd handle the reward signal for deciding when to switch between observation and active exploration without it collapsing into one mode.
If you like biomimetic approaches to computer science, there's evidence that we want something besides neural networks. Whether we call such secondary systems emotions, hormones, or whatnot doesn't really matter much if the dynamics are useful. It seems at least possible that studying alignment-related topics is going to get us closer than any perspective that's purely focused on learning. Coincidentally quanta is on some related topics today: https://www.quantamagazine.org/once-thought-to-support-neuro...
The question is does this eventually lead us back to genetic programming and can we adequately avoid the problems of over-fitting to specific hardware that tended to crop up in the past?
Or possibly “in addition to”, yeah. I think this is where it needs to go. We can’t keep training HUGE neural networks every 3 months and throw out all the work we did and the billions of dollars in gear and training just to use another model a few months.
That loops is unsustainable. Active learning needs to be discovered / created.
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by Emmanuel Dupoux, Yann LeCun, Jitendra Malik
"he proposed framework integrates learning from observation (System A) and learning from active behavior (System B) while flexibly switching between these learning modes as a function of internally generated meta-control signals (System M). We discuss how this could be built by taking inspiration on how organisms adapt to real-world, dynamic environments across evolutionary and developmental timescales. "
https://github.com/plastic-labs/honcho has the idea of one sided observations for RAG.
If this was done well in a way that was productive for corporate work, I suspect the AI would engage in Machievelian maneuvering and deception that would make typical sociopathic CEOs look like Mister Rogers in comparison. And I'm not sure our legal and social structures have the capacity to absorb that without very very bad things happening.
I was kind of worried by them going Machiavellian or evil but it doesn't seem the default state for current ones, I think because they are basically trained on the whole internet which has a lot of be nice type stuff. No doubt some individual humans my try to make them go that way though.
I guess it would depend a bit whos interests the AI would be serving. If serving the shareholders it would probably reward creating value for customers, but if it was serving an individual manager competing with others to be CEO say then the optimum strategy might be to go machiavellian on the rivals.
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Not just CEOs, Legal and social structures will also be run by AI. Chimps with 3 inch brains cant handle the level of complexity global systems are currently producing.
> If this was done well in a way that was productive for corporate work, I suspect the AI would engage in Machievelian maneuvering and deception that would make typical sociopathic CEOs look like Mister Rogers in comparison.
Algorithms do not possess ethics nor morality[0] and therefore cannot engage in Machiavellianism[1]. At best, algorithms can simulate same as pioneered by ELIZA[2], from which the ELIZA effect[3] could be argued as being one of the best known forms of anthropomorphism.
0 - https://www.psychologytoday.com/us/basics/ethics-and-moralit...
1 - https://en.wikipedia.org/wiki/Machiavellianism_(psychology)
2 - https://en.wikipedia.org/wiki/ELIZA
3 - https://en.wikipedia.org/wiki/ELIZA_effect
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Agents playing the iterated prisoner's dilemma learn to cooperate. It's usually not a dominant strategy to be entirely sociopathic when other players are involved.
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There's already a model capable of autonomous learning on the small scale, just nobody's tried to scale it up yet: https://arxiv.org/abs/2202.05780
LeCun has been talking about his JEPA models for awhile.
https://ai.meta.com/blog/yann-lecun-ai-model-i-jepa/
In this podcast episode[0] he does talk about this kind of model and how it "learns about physics" through experience instead of just ingesting theorical material.
It's quite eye opening.
0. https://youtu.be/qvNCVYkHKfg
The way I see it, the "world models" he wants to train require a magnitude more compute than what LLM training requires since physical data is likely much more unstructured than internet data.
He raised $1b but that seems way too little to buy enough compute to train.
My bet is that OpenAI or Anthropic or both will eventually train the model that he always wanted because they will use revenue from LLMs to train a world model.
The whole AI field is a misnomer. It stole so much from neurobiology.
However had, there will come a time when AI will really learn. My prediction is that it will come with a different hardware; you already see huge strides here with regards to synthetic biology. While this focuses more on biology still, you'll eventually see a bridging effort; cyborg novels paved the way. Once you have real hardware that can learn, you'll also have real intelligence in AI too.
I remember a joke from few years ago that was showing an "AI" that was "learning" on its "own" which meant periodically starting from scratch with a new training set curated by a large team of researchers themselves relying on huge teams (far away) of annotators.
TL;DR: depends where you defined the boundaries of your "system".
I think from a proper systemic view that joke is more correct than not. AI is just the frontend of people ...
But doesnt existing AI systems already learn in some way ? Like the training steps are actually the AI learning already. If you have your training material being setup by something like claude code, then it kind of is already autonomous learning.
Most, if not all, commercially available AI models are doing offline learning. The cognition is a skill that is only possible on online learning which is the autonomous part the authors refer to, that is, learning by observing, interacting.
In that sense the "autonomous" part you said simply meant that the data source is coming from a different place, but the model itself is not free to explore with a knowledge base to deduce from, but rather infer on what is provided to it.
> The cognition is a skill that is only possible on online learning which is the autonomous part the authors refer to, that is, learning by observing, interacting.
This is the "Claude Code" part, or even the ChatGPT (web interface/app) part. Large context window full of relevant context. Auto-summarization of memories and inclusion in context. Tool calling. Web searching.
If not LLMs, I think we can say that those systems that use them in an "agentic" way perhaps have cognition?
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If you let the AI train on your prompts it will actually learn indirectly. It is still offline learning though.
The paper's critique of the 'data wall' and language-centrism is spot on. We’ve been treating AI training like an assembly line where the machine is passive, and then we wonder why it fails in non-stationary environments. It’s the ultimate 'padded room' architecture: the model is isolated from reality and relies on human-curated data to even function.
The proposed System M (Meta-control) is a nice theoretical fix, but the implementation is where the wheels usually come off. Integrating observation (A) and action (B) sounds great until the agent starts hallucinating its own feedback loops. Unless we can move away from this 'outsourced learning' where humans have to fix every domain mismatch, we're just building increasingly expensive parrots. I’m skeptical if 'bilevel optimization' is enough to bridge that gap or if we’re just adding another layer of complexity to a fundamentally limited transformer architecture.
"don't learn" might be a good feature from a business point of view
Imagine if AI learns all your source code and apply them to your competitor /facepalm
We are rediscovering Cybernetics
I've tried figuring out what the big deal about cybernetics was, but I always come away with a feeling of it being a bit wish-washy. Is it a bit like Philosophy in that it birthed individual fields that were inspired by and made applications of the thoughts, models and ideas laid out by its forebears? Or were there actual proofs, discoveries or applications in the field itself?
(I guess one could call projects like https://en.wikipedia.org/wiki/Project_Cybersyn an "application" of its ideas, though cut off before one could see the results.)
bookmarking in case someone posts an answer
Biological Computer Laboratory (1958-1976), https://web.archive.org/web/20190829234412/http://bcl.ece.il...
It's striking how cybernetics has gone from dated to timely.
I think restrcicting this discussion to LLMs - as it is often done - misses the point: LLMs + harnesses can actually learn.
That's why I think the term "system" as used in the paper is much better.
> LLMs + harnesses can actually learn.
No. No, they don't
Eh, honestly? We're not that far away from models training themselves (opus 4.6 and codex 5.3 were both 'instrumental' in training themselves).
They're capable enough to put themselves in a loop and create improvement which often includes processing new learnings from bruteforcing. It's not in real-time, but that probably a good thing if anyone remembers microsofts twitter attempt.
I was thinking in the same way that the human brain's design came about from evolutionary trial and error, we may be close to a situation where we can do something like that for the artificial neural networks and have the computers improve them by fiddling about.
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Like Memento
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What I find interesting is the supposition that weights must change. The connections of my motherboard do not change, yet it can simulate any system.
Perhaps there is an architecture that is write-once-read-forever, and all that matters is context.
There's almost certainly some of this in the human mind, and I bet there is much more of it than we are willing to admit. No amount of mental gymnastics is going to let you visualize 6D structures.
>supposition that weights must change
The thing is that's where most of the leaning and 'intelligence' is. If you don't change them the model doesn't really get smarter.
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Can I run it?
claude is learning very fast