Today's locked-down pre-trained models at least have some consistency.
In a decade since then, things got marginally better, and such events wouldn't play out so fast and so intensely in 2026.
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.
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.
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.
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.
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?
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.
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.
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.
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.
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.
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...
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. "
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.
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)
It's quite eye opening.
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.
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.
TL;DR: depends where you defined the boundaries of your "system".
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.
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 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.
Imagine if AI learns all your source code and apply them to your competitor /facepalm
(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.)
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.
That's why I think the term "system" as used in the paper is much better.
No. No, they don't
