Comment by SwellJoe | Hacker News Reader

Comment by SwellJoe | Hacker News Reader

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Sure, it's happening...but, is IT happening? By that, I mean, we can see that the models are able to iterate at a pace and scale that humans can't match, and that provides gains in model performance and efficiency. But, humans are still needed in the loop, and not just because it's necessary for safety/alignment reasons. I don't think any significant discovery has been made by models on their own, and I don't know that LLMs will ever have the capacity to invent. They can synthesize from known data amazingly well, and since they know everything "known data" is extremely broad. But, the leaps, so far, have all come from humans.

So far, I don't think the models are capable of running away on their own. Of course, it would be playing with fire to not at least consider the risks of such a runaway scenario and build in safeguards against it. But, there is no model that can build a better model on its own, thus far, to the best of my knowledge (which is far more limited than the models, so maybe I should ask them).

Recursive Self-Improvement isn't instant, it starts slow and accelerates.

It starts with what they already claim to be doing - increasingly relying on existing models in non-trivial work related to training, evaluating and optimizing the next, more capable generation of models. As long as the proportion of work keeps shifting towards agents doing more and more of it, and humans less and less, that's RSI at play.

It may be that it turns out LLMs lack some fundamental level of judgement and it plateaus, but frankly I find this notion absurd; LLMs already show better judgement than most people. The alternative is, at some point LLMs will show the ability to futz their way into improvement of the next generation of models even without humans in the loop - even if much less efficient at first, if generation N+1 is more capable than generation N, it'll either take off or burn out.

you are really just talking out of your ass here, no offense

just because more and more agents are doing human work, that in no way means the model somehow becomes magically more intelligent, it just means the work will stall and continue on at the same level forever

hell even if they hypothetically have an internal model that can output the entire training data set in a better format, there's no scientific evidence that the newer format has new information that is sufficient enough to train a better AI

as a matter of fact the scientific evidence is on the contrary

It's not the number of agents you should pay attention to, but the scope and nature of work they can perform effectively without being micromanaged by humans.

He talks out of his ass on a lot of topics on here especially those concerning economics and finance.

Even currently, there are clearly diminishing returns throughout the field. While not a skeptic myself, I think the age of LLM's is now over or close to plateauing. GPT-6.1 Sol seems almost the same as GPT-6 Sol(even if the benchmarks are slightly better). It doesn't help that we are running out of real training data, requiring new Synthetic data to be created for the next generation of Models. AI(or SI), has hit its peak, IMO, and now the focus shifts directly to Autonomous tasks. While Fable 5.1 is borderline okay at autonomous tasks, the amount of Slop it generates fundamentally requires a human to correct its mistakes. I was working on a website for a nonprofit with Fable, but the amount of slop and corrections I had to make was astounding/

> It may be that it turns out LLMs lack some fundamental level of judgement and it plateaus

All intelligence, LLM or not, is bound to plateau around the point where the need to operate within physical reality bottlenecks the speed of feedback. AI is progressing swiftly in the digital realm where feedback is nearly instantaneous, but it's unclear whether that would translate into improvements in the physical world where signals are much noisier and intelligence and judgment are less impactful.

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