The singularity, as defined by Hinton (and others) as RSI (Recursive Self Improvement) may actually be beginning already, as OpenAI has announced an AI acting as a "research intern" (!).

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jackb4040 3 hours ago | parent | next [-]

How is this different from arguing that Microsoft Clippy was RSI? An AI tool being involved in the process of work can't be the bar for RSI.

I don't think there can be a coherent definition of RSI unless people lay out their theory for how intelligence scales. LLM-assisted coding is great but respectfully optimizing pytorch features or whatever is not gonna lead to exponential improvements. That approach to scaling diminished years ago, leading all the labs to switch to reasoning.

Now it seems reasoning is also yielding diminishing returns, so all the labs are pivoting to specializing in particular fields like math / infosec / biology. They're improving due to accessing new proprietary training data and doing RL with human experts. Again I don't really see any amount of "AI research interns" leading to an exponential improvement to this strategy, they're not the bottleneck in the first place.

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pizza234 2 hours ago | parent [-]

> Now it seems reasoning is also yielding diminishing returns

Not true. On the contrary, LLMs are developing faster than predicted. They were expected to solve a Millennium Prize by 2030... and here we are in 2026. Release cycles are getting faster. Just compare the most recent GPT or Claude with what they were an year ago.

> How is this different from arguing that Microsoft Clippy was RSI?

We can argue about semantics, but that's not really the point. The point is that what started now - which no doubt is in its infancy - will result in full autonomy quite soon (they project an year or so), with the risk of RSI causing agent development to slip (long term) outside human cognitive control/capacity.

	▲	jackb4040 2 hours ago \| parent [-]
		Again, can you lay out your theory for how intelligence scales? You're using a lot of terms like "full autonomy" without definitions. Why do you think that just throwing more harnessed LLMs at (something?) will lead to an increase rate of improvement? I feel like I laid out several cases where other things were the limiting factor on improvement and more agents wouldn't have helped, and I didn't get a response to those cases. What "they project" (the labs) is of minor interest to me. Aside from their incentives and track record of lying, in recent months they are laying out a story that is pretty much just the plot of Terminator, and directly referencing rationalist beliefs that were published long before LLMs even existed.

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physicallyIllfr 3 hours ago | parent | prev [-]

How is it improving, that would require rearranging its weights and biases which it cannot do easily or quickly.

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pixl97 3 hours ago | parent | next [-]

Self improvement during training, and AI self training are already happening. Easily/quickly are seemingly a factor of how much power/hardware you want to use at once.

With the level of compute they have they aren't stuck with frozen models like you are.

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criddell 3 hours ago | parent | prev [-]

Is easily and quickly a requirement? Isn't it enough that over time it improves itself even if the process is complex and slow?

	▲	nozzlegear 3 hours ago \| parent [-]
		Do we know it's actually improving itself? Perhaps it's just opaquely sorting all ones and zeros for better lookup efficiency.