[HN Gopher] Why AI systems don't learn - On autonomous learning ...
___________________________________________________________________
Why AI systems don't learn - On autonomous learning from cognitive
science
Author : aanet
Score : 169 points
Date : 2026-03-17 21:42 UTC (1 days ago)
(HTM) web link (arxiv.org)
(TXT) w3m dump (arxiv.org)
| aanet wrote:
| 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. "
| dasil003 wrote:
| 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.
| marsten wrote:
| 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.
| ehnto wrote:
| You don't get that many iterations in the real world
| though, and if one of your first iterations is particularly
| bad you don't get any more iterations.
| cortesoft wrote:
| But AI will train in the artificial world
| ehnto wrote:
| They still fail in the real world, where a single failure
| can be highly consequential. AI coding is lucky it has
| early failure modes, pretty low consequence. But I don't
| see how that looks for an autonomous management agent
| with arbitrary metrics as goals.
|
| Anyone doing AI coding can tell you once an agent gets on
| the wrong path, it can get very confused and is usually
| irrecoverable. What does that look like in other
| contexts? Is restarting the process from scratch even
| possible in other types of work, or is that unique to
| only some kinds of work?
| naasking wrote:
| > You don't get that many iterations in the real world
| though
|
| True, for iterations between the same two players, but
| humans evolved the ability to communicate and so can
| share the results of past interactions through a network
| with other agents, aka a reputation. Thus any interaction
| with a new person doesn't start from a neutral prior.
| gotwaz wrote:
| 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.
| AdieuToLogic wrote:
| > 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
| qsera wrote:
| https://en.wikipedia.org/wiki/ELIZA_effect
|
| >As Weizenbaum later wrote, "I had not realized ... that
| extremely short exposures to a relatively simple computer
| program could induce powerful delusional thinking in quite
| normal people."...
|
| That pretty much explain the AI Hysteria that we observe
| today.
| reverius42 wrote:
| ELIZA couldn't write working code from an English-
| language prompt though.
|
| I think the "AI Hysteria" comes more from current LLMs
| being actually good at replacing a lot of activity that
| coders are used to doing regularly. I wonder what
| Weizenbaum would think of Claude or ChatGPT.
| qsera wrote:
| >ELIZA couldn't write working code from an English-
| language prompt though.
|
| Yea, that is kind of the point. Even such a system could
| trick people into delusional thinking.
|
| > actually good at replacing a lot of activity that
| coders are used to...
|
| I think even that is unrealistic. But that is not what I
| was thinking. I was thinking when people say that current
| LLMs will go on improving and reach some kind of real
| human like intelligence. And ELIZA effect provides a
| prefect explanation for this.
|
| It is very curious that this effect is the perfect thing
| for scamming investors who are typically bought into such
| claims, but under ELIZA effect with this, they will do
| 10x or 100x investment....
| ACCount37 wrote:
| https://en.wikipedia.org/wiki/AI_effect
|
| >It's part of the history of the field of artificial
| intelligence that every time somebody figured out how to
| make a computer do something--play good checkers, solve
| simple but relatively informal problems--there was a
| chorus of critics to say, 'that's not thinking'.
|
| That pretty much explains the "it's not real AI" hysteria
| that we observe today.
|
| And what is "AI effect", really? It's a coping mechanism.
| A way for silly humans to keep pretending like they are
| unique and special - the only thing in the whole world
| that can be truly intelligent. Rejecting an ever-growing
| pile of evidence pointing otherwise.
| qsera wrote:
| >there was a chorus of critics to say, 'that's not
| thinking'.
|
| And they were always right...and the other guys..always
| wrong..
|
| See, the questions is not if something is the "real ai".
| The questions is, what can this thing realistically
| achieve.
|
| The "AI is here" crowd is always wrong because they
| assign a much, or should I say a "delusionaly" optimistic
| answer to that question. I think this happens because
| they don't care to understand how it works, and just go
| by its behavior (which is often cherry-pickly optimized
| and hyped to the limit to rake in maximum investments).
| ACCount37 wrote:
| Anyone who says "I understand how it works" is completely
| full of shit.
|
| Modern production grade LLMs are entangled messes of
| neural connectivity, produced by inhuman optimization
| pressures more than intelligent design. Understanding the
| general shape of the transformer architecture does NOT
| automatically allow one to understand a modern 1T LLM
| built on the top of it.
|
| We can't predict the capabilities of an AI just by
| looking at the architecture and the weights - scaling
| laws only go so far. That's why we use evals. "Just go by
| behavior" is the industry standard of AI evaluation, and
| for a good damn reason. Mechanistic interpretability is
| in the gutters, and every little glimpse of insight we
| get from it we have to fight for uphill. We don't
| understand AI. We can only observe it.
|
| "What can this thing realistically achieve?" Beat an
| average human on a good 90% of all tasks that were once
| thought to "require intelligence". Including tasks like
| NLP/NLU, tasks that were once nigh impossible for a
| machine because "they require context and understanding".
| Surely it was the other 10% that actually required "real
| intelligence", surely.
|
| The gaps that remain are: online learning, spatial
| reasoning and manipulation, long horizon tasks and
| agentic behavior.
|
| The fact that everything listed has mitigations (i.e.
| long context + in-context learning + agentic context
| management = dollar store online learning) or training
| improvements (multimodal training improves spatial
| reasoning, RLVR improves agentic behavior), and the
| performance on every metric rises release to release?
| That sure doesn't favor "those are fundamental
| limitations".
|
| Doesn't guarantee that those be solved in LLMs, no, but
| goes to show that it's a possibility that cannot be
| dismissed. So far, the evidence looks more like "the
| limitations of LLMs are not fundamental" than "the
| current mainstream AI paradigm is fundamentally flawed
| and will run into a hard capability wall".
| qsera wrote:
| Mm..You seem to be consider this to be some mystical
| entity and I think that kind of delusional idea might be
| a good indication that you are having the ELIZA effect...
|
| >We don't understand AI. We can only observe it.
|
| Lol what? Height of delusion!
|
| > Beat an average human on a good 90% of all tasks that
| were once thought to "require intelligence".
|
| This is done by mapping those tasks to some
| representation that an non-intelligent automation can
| process. That is essentially what part of unsupervised
| learning does.
| qsera wrote:
| Do yourself a favor and watch this video podcast shared
| by the following comment very carefully..
|
| https://news.ycombinator.com/item?id=47421522
| ACCount37 wrote:
| Frankly, I don't buy that LeCun has that much of use to
| say about modern AI. Certainly not enough to justify an
| hour long podcast.
|
| Don't get me wrong, he has some banger prior work, and
| the recent SIGReg did go into my toolbox of dirty ML
| tricks. But JEPA line is rather disappointing overall,
| and his distaste of LLMs seems to be a product of his
| personal aesthetic preference on research direction
| rather than any fundamental limitations of transformers.
| There's a reason why he got booted out of Meta - and it's
| his failure to demonstrate results.
|
| That talk of "true understanding" (define true) that he's
| so fond of seems to be a flimsy cover for "I don't like
| the LLM direction and that's all everyone wants to do
| those days". He kind of has to say "LLMs are
| fundamentally broken", because if they aren't, if better
| training is all it takes to fix them, then, why the fuck
| would anyone invest money into his pet non-LLM research
| projects?
|
| It is an uncharitable read, I admit. But I have very
| little charity left for anyone who says "LLMs are
| useless" in year 2026. Come on. Look outside. Get a
| reality check.
| qsera wrote:
| My opinions on the matter does not come from any experts
| and is coming from my own reason. I didn't see that video
| before I came across that comment.
|
| >"LLMs are useless" in year 2026
|
| Literally no one is saying this. It is just that those
| words are put into the mouths of the people that does not
| share the delusional wishful thinking of the "true
| believers" of LLM AI.
| ACCount37 wrote:
| To be honest, I would prefer "I over-index on experts who
| were top of the line in the past but didn't stay that
| way" over "my bad takes are entirely my own and I am
| proud of it". The former has so much more room for
| improvement.
|
| >Literally no one is saying this.
|
| Did you not just advise me to go watch a podcast full of
| "LLMs are literally incapable of inventing new things"
| and "LLMs are literally incapable of solving new
| problems"?
|
| I did skim the transcript. There are some very bold
| claims made there - especially when LLMs out there roll
| novel math and come up with novel optimizations.
|
| No, not reliably. But the bar we hold human intelligence
| to isn't that high either.
| qsera wrote:
| >my bad takes are entirely my own and I am proud of it"
|
| Sure, but the same could apply to you as well.
|
| >"LLMs are literally incapable of inventing new things"
| and "LLMs are literally incapable of solving new
| problems"?
|
| You keep proving that you have trouble resolving closely
| related ideas. Those two things that you mention does not
| imply that they are "useless". They are a better search
| and for software development, they are useful for reviews
| (at least for a while). But it seems that people like you
| can only think in binary. It is either LLMs are god like
| AI, or they are useless.
| naasking wrote:
| > Algorithms do not possess ethics nor morality[0] and
| therefore cannot engage in Machiavellianism[1].
|
| Conjecture. There are plenty of ethical frameworks grounded
| in pure logic (Kant), or game theory (morality as evolved
| co-operation). These are both amenable to algorithmic
| implementations.
| tim333 wrote:
| 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.
| estearum wrote:
| > I think because they are basically trained on the whole
| internet which has a lot of be nice type stuff.
|
| Is this not just because their goals are currently to be
| seen as "nice"?
|
| Surely they can be not-nice if directed to, and then the
| question is just whether someone can accidentally direct
| them to do that by e.g. setting up goals that can be more
| readily achieved by being not-nice. Which... is how many
| goals in the real world are, which is why the very concept
| and danger of Machiavellianism exists.
| iFire wrote:
| https://github.com/plastic-labs/honcho has the idea of one
| sided observations for RAG.
| beernet wrote:
| 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.
| jdkee wrote:
| LeCun has been talking about his JEPA models for awhile.
|
| https://ai.meta.com/blog/yann-lecun-ai-model-i-jepa/
| Xunjin wrote:
| 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
| aurareturn wrote:
| 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.
| zhangchen wrote:
| 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.
| robot-wrangler wrote:
| > 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...
| t-writescode wrote:
| 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.
| exe34 wrote:
| if that's the arguement for active learning, wouldn't it
| also apply in that case? it learns something and 5 minutes
| later my old prompts are useless.
| t-writescode wrote:
| That depends on the goals of the prompts you use with the
| LLM:
|
| * as a glorified natural language processor (like I have
| done), you'll probably be fine, maybe
|
| * as someone to communicate with, you'll also probably be
| fine
|
| * as a *very* basic prompt-follower? Like, natural
| language processing-level of prompt "find me the
| important words", etc. Probably fine, or close enough.
|
| * as a robust prompt system with complicated logic each
| prompt? Yes, it will begin to fail catastrophically,
| especially if you're wanting to be repeatable.
|
| I'm not sure that the general public is that interested
| in perfectly repeatable work, though. I think they're
| looking for consistent and improving work.
| naasking wrote:
| I don't think old prompts would become useless. A few
| studies have shown that prompt crafting is important
| because LLMs often misidentify the user's _intent_.
| Presumably an AI that is learning continuously will
| simply get better at inferring intent, therefore any
| prompts that were effective before will continue to be
| effective, it will simply grow its ability to infer
| intent from a larger class of prompts.
| fallous wrote:
| 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?
| tranchms wrote:
| We are rediscovering Cybernetics
| walterbell wrote:
| Biological Computer Laboratory (1958-1976),
| https://web.archive.org/web/20190829234412/http://bcl.ece.il...
| QuesnayJr wrote:
| It's striking how cybernetics has gone from dated to timely.
| internet_points wrote:
| 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.)
| tryauuum wrote:
| bookmarking in case someone posts an answer
| Frannky wrote:
| Can I run it?
| Animats wrote:
| 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
| Earw0rm wrote:
| 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.
| TeMPOraL wrote:
| 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.
| giancarlostoro wrote:
| > In a decade since then, things got marginally better, and
| such events wouldn't play out so fast and so intensely in
| 2026.
|
| Are you saying the internet would not do it again, or
| Microsoft would not do the same approach? Because I think
| the internet would absolutely do it again.
| TeMPOraL wrote:
| I'm saying that the Internet would try, but it would be
| less of a deal, because idiots feigning offense on the
| Internet are not new anymore, people got a bit bored of
| it over the past decade, so it doesn't command as much
| attention anymore.
| giancarlostoro wrote:
| Ah I understand now. Thanks for the clarification! I
| agree.
| vasco wrote:
| 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.
| bsjshshsb wrote:
| 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.
| armchairhacker wrote:
| 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.
| moffkalast wrote:
| 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.
| shevy-java wrote:
| > 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.
| InfiniteLoup wrote:
| 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.
| Kye wrote:
| https://blogs.microsoft.com/blog/2016/03/25/learning-tays-
| in...
|
| https://arxiv.org/abs/1812.08989
| mapmeld wrote:
| 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.
| armoredkitten wrote:
| 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.
| est wrote:
| "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
| utopiah wrote:
| 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".
| p_v_doom wrote:
| I think from a proper systemic view that joke is more correct
| than not. AI is just the frontend of people ...
| krinne wrote:
| 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.
| LovelyButterfly wrote:
| 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.
| reverius42 wrote:
| > 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?
| troupo wrote:
| No, no they don't. Actual learning _survives_ beyond
| "sufficient context window".
|
| Start a new chat, and the "agentic" system will be as
| clueless as before
| reverius42 wrote:
| They can write to the filesystem, and future instances
| can read it (and write more). The agentic system does not
| remain as clueless as its LLM's first instantiation.
| nullpoint420 wrote:
| Do you need a notebook to remember who you are? The point
| is to update the model weights so it learns.
| troupo wrote:
| _Memento_ notebooks are not learning
| LovelyButterfly wrote:
| > 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.
|
| From what I've been learning in my uni, this is said pre-
| programmed. Cognition is really the ability from, out of no
| context, no knowledge of what you are capable of, to learn
| something. These tool calling and web searching are, in the
| end, MCP functions provided by the LLM provider themselves.
|
| It's an entire academic discussion, about how things start.
| For example babies: they someone have a knowledge base on
| how to breath, how to cry, but they have absolute no
| knowledge on how to speak and it learns by the interactions
| with the parents.
|
| LLMs try as much as they can create this by inference and
| pre-programmed functions, but they don't have a graph of
| memories with utility to weight their relevance in the
| context. As others said, the context window dies as soon as
| you close the session.
|
| They also don't have the epistemic approach that is to know
| that another agent knows about something just by observing
| the environment they were all put in.
| imtringued wrote:
| If you let the AI train on your prompts it will actually learn
| indirectly. It is still offline learning though.
| logicchains wrote:
| 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
| lovebite4u_ai wrote:
| claude is learning very fast
| followin_io82 wrote:
| good read. thanks for sharing
| himata4113 wrote:
| 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.
| tim333 wrote:
| 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.
| shevy-java wrote:
| 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.
| Garlef wrote:
| 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.
| troupo wrote:
| > LLMs + harnesses can actually learn.
|
| No. No, they don't
| theptip wrote:
| 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.
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