The conventional wisdom, well captured recently by Ethan Mollick, is that LLMs are advancing exponentially. A few days ago, in very popular blog post, Mollick claimed that “the current best estimates of the rate of improvement in Large Language models show capabilities doubling every 5 to 14 months”:
  • @kromem@lemmy.worldEnglish
    8•1 month ago

    In truth, we are still a long way from machines that can genuinely understand human language. […]

    Indeed, we may already be running into scaling limits in deep learning, perhaps already approaching a point of diminishing returns. In the last several months, research from DeepMind and elsewhere on models even larger than GPT-3 have shown that scaling starts to falter on some measures, such as toxicity, truthfulness, reasoning, and common sense.

    I’ve rarely seen anyone so committed to being a broken clock in the hope of being right at least once a day.

    Of course, given he built a career on claiming a different path was needed to get where we are today, including a failed startup in that direction, it’s a bit like the Upton Sinclair quote about not expecting someone to understand a thing their paycheck depends on them not understanding.

    But I’d be wary of giving Gary Marcus much consideration.

    Generally as a futurist if you bungle a prediction so badly that four days after you were talking about diminishing returns in reasoning a product comes out exceeding even ambitious expectations for reasoning capabilities in an n+1 product, you’d go back to the drawing board to figure out where your thinking went wrong and how to correct it in the future.

    Not Gary though. He just doubled down on being a broken record. Surely if we didn’t hit diminishing returns then, we’ll hit them eventually, right? Just keep chugging along until one day those predictions are right…