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Comment by chilmers

21 hours ago

The implication from their last couple of published articles[1][2] is that they think they’ve achieved “recursive self improvement”.

[1] https://openai.com/index/research-acceleration-view-inside-o... [2] https://openai.com/index/an-alien-mind/

59 comments

chilmers

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noir_lord  16 hours ago

Recursive self improvement of their upcoming IPO value maybe.

They are fluffy PR pieces otherwise.

  • Dlemlo  4 hours ago

    I tried to build a procedural 3d asset pipeline for a specific use case.

    Before the Opus upgrade in November it was basically no way of doing this. I gave up very quickly.

    After November i tried again, and no model could build me anything relevant.

    Now it just works. Took me an hour to progress to a point were i'm happy.

    Whatever they do, progress is still real, still way faster than I assumed

    The list of Ubuntus 2404 LTS CVEs is HUGE. Another indicator that a lot of stuff got a lot better fast.

    Feel free to be as dismissive as you want, but if you are not careful, you might be 'suddenly' surprised and you might not be prepared for the conclusion of AGI level agents.

  • piloto_ciego  15 hours ago

    How can you possible say this sort of thing in context of what looks like a millenium prize being solved.

    I swear there's nobody blinder than those who won't see.

sensitivekt9q3  2 hours ago

Is it? The first article says they’re on-track to building an automated AI researcher by March 2028. So they haven’t achieved RSI yet, I would think?

10xDev  21 hours ago

Compute will always be the bottleneck even if this were true.

  • hgoel  19 hours ago

    As a statement of fact divorced from context, this is of course true, but it's worth putting it in context of what small-medium scale models have been achieving recently. Many of the most recent releases from Chinese labs are almost on par with trillion parameter models from less than a year ago (edit: despite being small enough to usably run on prosumer hardware). It seems clear parameter efficiency can still be improved dramatically.

    In which case, maybe we don't need as much compute as we might expect. I hesitate to say "to reach a singularity" because it's kind of hard to define how that works out. Even intelligence probably hits some scaling limits eventually (e.g. speed of light related restrictions on how far it can scale, or how quickly it can expand).

  • Fordec  20 hours ago

    If humans can figure out to optimize to circumvent bottlenecks, I have no doubt each new bottleneck will also get routed around, just now automated.

  • monster_truck  19 hours ago

    Based on the leaps in local inference speed in the past month, which have been absurd, I'm p confident we're going to whiplash from compute constrained to storage constrained.

    Bit apples to oranges, but it reminds me of all the fiber we installed in the late 90s, certain that per-strand capacity increases were years or decades out, only to get massively rugged

    • Fordec  19 hours ago

      I expect the investments into AI driven mathematic discoveries that underpin compression efficiency will be a key investment area. Particularly at the data center scale rather than per device or per file level.

      3 replies →

  • Miner49er  21 hours ago

    Eventually recursive self-improvement includes reducing bottlenecks.

    • glenstein  19 hours ago

      Which is to say, scalable and open-ended capability of ramping up physical infrastructure.

      I don't know that that's achievable yet. Though the era of increasingly advanced and automated robotics seems to be around the corner which could create a cycle, vicious or virtuous depending on how you feel about it.