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Det var du som introducerade halmgubben "huvudsyftet".Sedan när du skulle beskriva vad du tror var huvudsyftet, då beskrev du kommunikation som handlade om dataintrång.
Du är ju en obstinat dåre. Autist eller?
![DAGGER[X]s avatar](https://static.flashback.org/img/avatar/3/12696.jpg)
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Senast redigerad av troligengud 2026-10-10 kl. 23:19.
Senast redigerad av troligengud 2026-10-10 kl. 23:19.
Så här går snacket just nu. Men det är helt klart möjligt att alla dom här hens som har ägnat sitt liv åt AI/SI saknar din expertis på området
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Rumors suggest Google, Anthropic, and OpenAI all are already using recursive self-improvement (RSI). Google’s rapid progress and OpenAI’s recent math results are allegedly linked to RSI, while Anthropic has reportedly begun training a larger model to catch up ("Fable 6"). If that's true, two questions arise for me: 1) What about China? We're hearing very little about it at the moment. And we're still waiting for significant releases from the East. 2) When RSI arrives, the importance of compute and energy will undoubtedly increase even further. Self-improvement requires a necessary prerequisite, which must first be met. And the biggest beneficiary will continue to be NVIDIA. So Dario Amodei will likely be right in his rough assessment that 2027 is the year everything changes.
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Some rumors I've heard. I'll start with Google, then Anthropic and OpenAI. Google: Insiders say Google has either cracked RSI (recursive self-improvement) or is very close. Their pace has picked up fast in recent months. They went from GPT-5 level to Opus 5.5 level in months, and speculation is that RSI made that possible. Anthropic: About a week ago, Anthropic started a new pretraining run. It is not Fable 5.5 but what would become Fable 6, or something bigger, to catch up to OpenAI's Bel. OpenAI: Bel (it may have another codename) is the model behind the math results OpenAI published a few days ago, and RSI apparently helped there too. From what I'm hearing, OpenAI, Anthropic, and Google are all very close to cracking RSI or already have. It will get scary and exciting at the same time.
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3.4.2 Researcher survey For each frontier model from Claude Opus 4.5 through Claude Mythos Preview, we surveyed a sample of Anthropic technical staff on (a) the productivity uplift they experienced from the model relative to no AI assistance; and (b) whether they felt the model qualified as a drop-in replacement for an entry-level Research Scientist or Engineer. We did not run a new survey for Claude Mythos 5. The most recent survey is the one reported for Claude Mythos Preview.
In that survey, the geometric mean self-reported uplift was on the order of 4×; 1 of 18 respondents thought we already had a drop-in replacement for entry-level Research Scientists or Engineers; 4 of 18 thought there was a ≥50% chance of reaching that bar with three months of iteration on the model’s scaffolding. Reported weaknesses relative to an entry-level researcher included the model’s skill at self-managing week-long ambiguous tasks, understanding organizational priorities, taste, verification, instruction-following, and epistemics.
In that survey, the geometric mean self-reported uplift was on the order of 4×; 1 of 18 respondents thought we already had a drop-in replacement for entry-level Research Scientists or Engineers; 4 of 18 thought there was a ≥50% chance of reaching that bar with three months of iteration on the model’s scaffolding. Reported weaknesses relative to an entry-level researcher included the model’s skill at self-managing week-long ambiguous tasks, understanding organizational priorities, taste, verification, instruction-following, and epistemics.
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