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Based on my experience trying to get LLMs to write useful code, I don't think LLMs are the right tool to optimize a novel technical problem. The best they can r

• SV_BubbleTime 3y ago
  Brainstorming has been my approach. It’s the rubber ducky method, except the duck writes back.
• teaearlgraycold 3y ago
  It’s like self driving cars 10 years ago. We were almost there! Just another couple years and the driver is extinct! It turns out that a nice demo doesn’t mean you’re about to revolutionize the world.
  • parineum 3y ago
    It's like the 90/10 problem, right? LLMs today do things that are pretty amazing things but they are missing something that seems incredibly simple to laymen but is and has always been the actual hard problem. I think the comparison to self driving cars in incredibly apt. A self driving car can do incredible things at superhuman speeds but the simple things it can't figure out are actually really hard problem. For instance, seeing a floating grocery bag and knowing that it's floating in the wind with negligible mass that doesn't need to be slowed down for or avoided. Something that actually requires an actual understanding of the world, rather than just being trained on the past. A similar situation for LLMs seems to be these "hallucinations". The laymen see the vast knowledge that they seem to hold and thinks, just tell it to stop lying and it's fixed. The problem is that telling the model not to lie is really only simple if it has some understanding which LLMs don't seem to possess. Their hallucinations, to them, are just as true as when they happen to tell the the true. They have no method of discerning them.
    • daveguy 3y ago
      It's more like a 60/40 problem right now. Generating reasonable sounding text was a huge problem for decades. That seems to be a relatively minor part of understanding what those words mean in more context than "the likely sequence that follows a prompt".
      • teaearlgraycold 3y ago
        Although I am impressed with how well the models perform when trained purely in a Chinese Room format. I think they have gleaned some understanding of some systems beyond just a super powered Markov chain.
        • daveguy 3y ago
          I'll admit that's possible. But if it's coded in a set of weights that happen to generate a good answer, how can we tell? I honestly think explainability is the most important problem in AI right now. And not explainability in terms of the model generating a series of words, but introspective explainability. If the models get to superhuman levels, maybe we don't have to have that, but until one or the other I don't know how we can demonstrate anything beyond super Markov chain.
  • rsynnott 3y ago
    The whole AI space seems to suffer from this. Remember in the late 90s, big tech companies (particularly Microsoft and IBM) confidently declaring that conversing naturally with a computer via voice recognition would be the primary way people use computers within years? Meanwhile, 25 years later, voice recognition is finally getting into the realm of being good enough for unimportant things where accuracy doesn’t matter. The only mainstream AI-adjacent thing that I can think of which ever actually became “good enough” is OCR, and that took about 50 years. I’m somewhat surprised that people aren’t more cautious, honestly. We’ve had _two_ “this time it’s different” moments in a little over a decade, now (self-driving cars and generative AI); while self-driving cars are at this point squarely in the “okay, maybe it’s not so different” bucket, I think people underestimate the risk that generative AI will, ultimately, also underwhelm.
    • chasd00 3y ago
      > I think people underestimate the risk that generative AI will, ultimately, also underwhelm That’s the true danger of AI.
      • rsynnott 3y ago
        I mean, joking aside, it is one; while I don't think we're quite there yet, there's the potential for a bigger version of the dot-com bubble.
  • seanhunter 3y ago
    Yes, and space elevators and fusion power are also just around the corner. 10 years from now is going to be great. I've been thinking that for the last 40 years.
    • daveguy 3y ago
      I get the fusion power thing. There has been serious research going into fusion power. But with the space elevator... I don't think even serious materials research scientists and engineers think we are anywhere close to a space elevator. Yeah, graphene / carbon nanotubes are exciting, but no viable manufacturing technique for the long braided strands required has been anywhere close. Our materials science is just nowhere near and people doing the research understand how far off we are.
      • teaearlgraycold 3y ago
        I’d believe space elevators could be around the corner if we had smaller analogues in production right now. Like a 2 mile tall building supported with miles of carbon nanotube wires.
        • seanhunter 3y ago
          Well yeah exactly. And that's not considering boring stuff like weather, wear and tear, and what happens if the cable snaps and the bottom half comes crashing down to earth. Even a super-light wire would presumably cause a catastrophic impact (and that's not considering if whatever payload came crashing down with it).
          • deleted 3y ago
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      • seanhunter 3y ago
        People were talking about space elevators before graphene or carbon nanotubes existed. I think the original idea came from Heinlein and when I was a kid there was no material that was even remotely feasible but it was still a tech that people talked about being possible in the "near" future.