Just another Space Monkey

  • 0 Posts
  • 22 Comments
Joined 26 days ago
Cake day: August 22nd, 2026
  • Wakko@hell.cloudtoTechnology@lemmy.world•‘Too little, too late’: critics perplexed and suspicious of AI leaders’ call for a slowdownEnglish
    0·
    10 hours ago

    Once again, you’re trying to analogize your way into something you haven’t studied.

    What exactly is your purpose here? You still can’t even articulate the fundamental flaws LLMs have with tasks not fully within the training data. Right now, today, there is no frontier model that can operate on tasks without resorting to reward hacking once its outside the small class of problems its training data covers. The solution for this is to invest significant time from domain experts to meticulously define how to solve tasks in other domains.

    Here’s a trivial example: Try getting Claude to generate coherent COBOL. Or TCL. Or even Powershell. Any language that has low representation on StackOverflow is a language that Claude can’t speak until someone teaches it how. Even the Python it generates has limited expressiveness or extensibility.

    Everywhere you look, the AI is limited by the fact that it can’t generate its own new information. Navier-Stokes and statements in pure mathematics like it are the absolute best case scenario for agentic work against rigorous specification. The theorem statement itself is already a rigorous specification. It has undergone decades of auditing by the mathematical community and its rendering in Lean is a straightforward translation defined in terms of battle-tested mathematical objects from mathlib. The verifier, the Lean theorem prover, has been extensively audited and specifically designed to avoid the types of unsoundness that would make it vulnerable to reward hacks.

    No other domains outside of mathematics have such rigorous specifications. Yet, somehow, you believe there’s magic pixie dust somewhere within the LLM that will help it achieve something without human interventions and that, somehow, we’re “close” to that accomplishment. Don’t quit your day job.

  • Wakko@hell.cloudtoTechnology@lemmy.world•‘Too little, too late’: critics perplexed and suspicious of AI leaders’ call for a slowdownEnglish
    1·
    22 hours ago

    You’re trying to analogize your way into a subject you clearly haven’t studied.

    There’s pre-existing research here. Godel’s Incompleteness Theorem holds, plus others.

    There’s already a known upper bound here that you’re clearly unaware of.

    There’s as yet been zero LLM-based architectures that have created new information. Everything they produce is somewhere within the training data. LLMs are a very specialized data compression algorithm, in a fashion.

    The stall is around whether Recursive Self-Improvement is achievable. Recent papers out of China are trying to chart a course to it. But, until someone succeeds, The current pace of improvement is already slowing signs of slowing. It’s not about where the finish line is placed, it’s about how fast they get there.

  • Wakko@hell.cloudtoTechnology@lemmy.world•‘Too little, too late’: critics perplexed and suspicious of AI leaders’ call for a slowdownEnglish
    5·
    2 days ago

    Do you have any benchmarks or data to back this “reckoning”

    I work with LLMs daily. I read papers as they hit arxiv. Also daily. You clearly don’t.

    I’m not interested in convincing anyone, which is why I’m speaking non-technically.

    The benchmarks being cited aren’t as interesting as you appear to believe they are. You’ve not fully grasped the fact that solving pre-made problems where the solutions are known or knowable isn’t anywhere close to the same thing as asking truly novel research questions independent of a human prompt. For OpenAI to also be embroiled in allegations of plagiarism only serves to underscore the gap between the two concepts.

  • Wakko@hell.cloudtoTechnology@lemmy.world•‘Too little, too late’: critics perplexed and suspicious of AI leaders’ call for a slowdownEnglish
    201·
    2 days ago

    I’m aware of what the pace is. You might want to check up on the current controversy surrounding OpenAI’s math “achievements”.

    By my reckoning, the difference between Mythos and Opus is smaller than the difference between Opus and Sonnet. Same with the difference between GPT 5.5 to 5.6 is smaller than the difference between GPT 4 to GPT 5.

    The size of improvements over time is diminishing. We’re not in “big bang” territory anymore and we’re about two years into the “incremental refinement” period. We’re about to enter the next AI Winter unless somebody comes up with a new architectural component as revolutionary as transformers have been for ML models.

    The core problem is that LLMs do not create. Full stop. All creativity is borne by the human inputs. Until that changes - until the model gains the capability to truly create new information, we’ve hit the limits in raw capability.