4 ms·

I think it's pretty surprising, and counterintuitive, how powerful generative language models are. It turns out that a lot of intellectual tasks are sort of wea

by md_ 3y ago

I think it's pretty surprising, and counterintuitive, how powerful generative language models are. It turns out that a lot of intellectual tasks are sort of weakly simulatable by just doing a great job at next-token prediction. I don't think that means that "good next-token prediction" is identical to "AGI". As Yan LeCun says, I think LLMs are an off-ramp on the highway to AGI. But they are powerful at specific tasks. Unfortunately, instead of evaluating their actual utility at a specific task, people just seem to think "if we throw AI at the problem, we will solve it," which is sort of like every other tech bubble ever.

version_five 3y ago
It turns out that a lot of intellectual tasks are sort of weakly simulatable by just doing a great job at next-token prediction. Yes, this is a good way of putting it. I've been saying for years, it's less that we're making big discoveries about what "AI" can do, and more that we're showing that many things humans do that appear complex actually reduce to something pretty simple. But that simple thing is still just fitting a pattern. It's the cases where it doesn't work, even if it only fails 1% of the time, that define the difference between pattern matching and actual human intelligence. One thing that follows from that (that people don't like) is that we actually need to move goalposts about how intelligence is defined. "Pass a turing test" is not very valuable now. And as mode tasks are shown to be possible with pattern matching / next token prediction, we need to further refine tests away from these tasks to settle on a good definition of what separates human intelligence. It should be obvious that the distinction is there, but it's still tough to nail down. (I'd argue that by defining a "task" you've already done most of the work to solving it, so it's not to exciting to learn that AI can finish the job)
- jackmott42 3y ago
  I am not convinced actual human intelligence is all that different. Not all people do the kind of reasoning we talk about when we try to distinguish between GPT and humans, and even those of us humans that CAN do it often don't bother. We may find that augmenting something as simple or nearly as simple as ChatGPT with a few extra tools to handle step by step reasoning (see the Wolfram plugin) may get you there.
  - md_ 3y ago
    I see these takes a lot, but I struggle to take them seriously. As a trivial example: LLMs don't learn new skills on the fly. A key aspect of human behavior--implicit memory--simply doesn't exist for these models. That seems like a pretty huge gap! Like, if ChatGPT is mediocre at your job today, it's going to be mediocre at it tomorrow and the next day, until a new model is trained!
    - pixl97 3y ago
      With current computing power limits training a new model takes a long time. Seems like months at this point. But lets say Nvidia pulls a rabbit out of its magic hat, and comes out with a training chip that is a million times more powerful. Now instead of months training that drops down to 24 hours. Would you still say that is a pretty huge gap? And I ask this because this is Nvidia's goal within a decade. Where, for you, does a big gap turn into 'that is dangerously close'?
      - md_ 3y ago
        I don't think the difference between human memory and retraining a DNN is purely about the latency to retraining. They seem to be fundamentally different things, no?
        pixl97 3y ago
        We would both agree that birds and planes are fundamentally different things, but we would both agree that they fly, correct? Both humans and LLMs have 'short term' and 'long term' memory. The process of turning short term to long term is significantly different. In LLMs this is taking the history data given to it and putting it in the training corpus then recalculating the weights. In humans this involves falling to sleep for some number of hours. I think in the long term here AI will actually have the benefit of forming long term memories. You have to sleep to form them, it just has to run reweighting on another cluster while the primary cluster goes about its business.

by md_ 3y ago

version_five 3y ago

It turns out that a lot of intellectual tasks are sort of weakly simulatable by just doing a great job at next-token prediction. Yes, this is a good way of putting it. I've been saying for years, it's less that we're making big discoveries about what "AI" can do, and more that we're showing that many things humans do that appear complex actually reduce to something pretty simple. But that simple thing is still just fitting a pattern. It's the cases where it doesn't work, even if it only fails 1% of the time, that define the difference between pattern matching and actual human intelligence. One thing that follows from that (that people don't like) is that we actually need to move goalposts about how intelligence is defined. "Pass a turing test" is not very valuable now. And as mode tasks are shown to be possible with pattern matching / next token prediction, we need to further refine tests away from these tasks to settle on a good definition of what separates human intelligence. It should be obvious that the distinction is there, but it's still tough to nail down. (I'd argue that by defining a "task" you've already done most of the work to solving it, so it's not to exciting to learn that AI can finish the job)

jackmott42 3y ago
I am not convinced actual human intelligence is all that different. Not all people do the kind of reasoning we talk about when we try to distinguish between GPT and humans, and even those of us humans that CAN do it often don't bother. We may find that augmenting something as simple or nearly as simple as ChatGPT with a few extra tools to handle step by step reasoning (see the Wolfram plugin) may get you there.
- md_ 3y ago
  I see these takes a lot, but I struggle to take them seriously. As a trivial example: LLMs don't learn new skills on the fly. A key aspect of human behavior--implicit memory--simply doesn't exist for these models. That seems like a pretty huge gap! Like, if ChatGPT is mediocre at your job today, it's going to be mediocre at it tomorrow and the next day, until a new model is trained!
  - pixl97 3y ago
    With current computing power limits training a new model takes a long time. Seems like months at this point. But lets say Nvidia pulls a rabbit out of its magic hat, and comes out with a training chip that is a million times more powerful. Now instead of months training that drops down to 24 hours. Would you still say that is a pretty huge gap? And I ask this because this is Nvidia's goal within a decade. Where, for you, does a big gap turn into 'that is dangerously close'?
    - md_ 3y ago
      I don't think the difference between human memory and retraining a DNN is purely about the latency to retraining. They seem to be fundamentally different things, no?
      - pixl97 3y ago
        We would both agree that birds and planes are fundamentally different things, but we would both agree that they fly, correct? Both humans and LLMs have 'short term' and 'long term' memory. The process of turning short term to long term is significantly different. In LLMs this is taking the history data given to it and putting it in the training corpus then recalculating the weights. In humans this involves falling to sleep for some number of hours. I think in the long term here AI will actually have the benefit of forming long term memories. You have to sleep to form them, it just has to run reweighting on another cluster while the primary cluster goes about its business.