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> I find it striking that there are still so many people downplaying the latest developments of AI. 5-7 years ago the progress with self driving cars seemed en

> I find it striking that there are still so many people downplaying the latest developments of AI. 5-7 years ago the progress with self driving cars seemed enormous, and the end of driving was just around the corner. And then all progress seemed to stall or recede, and it turned out that what seemed like huge progress was mostly hot air. Maybe that’s not what happening now, but I don’t think a cautious approach is unwarranted.

CuriouslyC 4y ago
Progress is sigmoidal approaching a limit. If the minimum acceptable performance is into the logarithmic portion of the curve, you'll see progress seem to stall out in that manner. Keep in mind that SoTA self driving AI has a better track record than people outside of rare corner cases, but the liability issue is crippling, so it won't see widespread adoption divorced from autopilot until it's "perfect." Unlike full self driving, I don't think applications of AI have a functional floor that is well into diminishing returns.
- mcphage 4y ago
  > Progress is sigmoidal approaching a limit. Yep, but a lot of the current hype for GPT is that we're in the near-vertical segment of the sigmoid, and so people are like "woah, if that's where we are now, where will we be in 5-10 years?!". And the answer to that might be, pretty much where we are now. And maybe not, maybe it'll be better, but you can't assume linear improvements on a sigmoid curve.
  - CuriouslyC 4y ago
    Considering that AI is just "taking off" now, I'd say we're still pretty early in the growth curve (the alternate hypothesis, that people have been oblivious for a while, seems unlikely). Regardless, I think even if we were well into the linear portion of the curve, we could still do a lot with layers of tooling on top of marginal improvements on current models and technology.
    - telotortium 4y ago
      We're in the hype cycle now at the moment, but the capabilities have been taking off for a few years. GPT-3 came out in 2020, and in retrospect, could have been trained several years before that, given that the 175 billion parameters of GPT-3 turned out to be way too many for its level of performance (as discovered by Chinchilla). I agree that there are a lot of refinements that can be made even with the current LLMs, but it's unclear how far LLMs will continue to improve.
pixl97 4y ago
When you go from doing 100 things to 10,000 things, it seems like a lot, until you realize you need to do 10,000,000,000 things to make the process work. In theory if we could make multi-modal GPT-4 go very fast, we could have it operate a car right now. It's too bad we don't have access to feed it images, because my guess is if you put a picture of an inside of the car and showed a kid chasing a ball into the road in front of it, it's first guess would be "hit the brakes".
- nuancebydefault 4y ago
  Indeed, the progress is exponential, but for things like full autonomous driving, we need near perfection, a lot better than the average driver. So does this mean 1k-fold better or 1M-fold better, we are not sure yet.

CuriouslyC 4y ago

Progress is sigmoidal approaching a limit. If the minimum acceptable performance is into the logarithmic portion of the curve, you'll see progress seem to stall out in that manner. Keep in mind that SoTA self driving AI has a better track record than people outside of rare corner cases, but the liability issue is crippling, so it won't see widespread adoption divorced from autopilot until it's "perfect." Unlike full self driving, I don't think applications of AI have a functional floor that is well into diminishing returns.

mcphage 4y ago
> Progress is sigmoidal approaching a limit. Yep, but a lot of the current hype for GPT is that we're in the near-vertical segment of the sigmoid, and so people are like "woah, if that's where we are now, where will we be in 5-10 years?!". And the answer to that might be, pretty much where we are now. And maybe not, maybe it'll be better, but you can't assume linear improvements on a sigmoid curve.
- CuriouslyC 4y ago
  Considering that AI is just "taking off" now, I'd say we're still pretty early in the growth curve (the alternate hypothesis, that people have been oblivious for a while, seems unlikely). Regardless, I think even if we were well into the linear portion of the curve, we could still do a lot with layers of tooling on top of marginal improvements on current models and technology.
  - telotortium 4y ago
    We're in the hype cycle now at the moment, but the capabilities have been taking off for a few years. GPT-3 came out in 2020, and in retrospect, could have been trained several years before that, given that the 175 billion parameters of GPT-3 turned out to be way too many for its level of performance (as discovered by Chinchilla). I agree that there are a lot of refinements that can be made even with the current LLMs, but it's unclear how far LLMs will continue to improve.

pixl97 4y ago

When you go from doing 100 things to 10,000 things, it seems like a lot, until you realize you need to do 10,000,000,000 things to make the process work. In theory if we could make multi-modal GPT-4 go very fast, we could have it operate a car right now. It's too bad we don't have access to feed it images, because my guess is if you put a picture of an inside of the car and showed a kid chasing a ball into the road in front of it, it's first guess would be "hit the brakes".

nuancebydefault 4y ago
Indeed, the progress is exponential, but for things like full autonomous driving, we need near perfection, a lot better than the average driver. So does this mean 1k-fold better or 1M-fold better, we are not sure yet.