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But those skeptics are initially responding to the constant AI hype claims that we are exponentially growing to AGI. So this article is in fact just a (very poo

by devmor 5mo ago

But those skeptics are initially responding to the constant AI hype claims that we are exponentially growing to AGI. So this article is in fact just a (very poorly thought through) attempt at saying “nuh uh, the hype might be true, you can’t prove it’s not yet!

aspenmartin 5mo ago
Yet the evidence is on the side of the hype? We don’t see any mechanism or cogent framework for what limits exist here theoretically that I’m aware of, are you? Epoch had a great article a year ago looking at several bottlenecks in terms of scale and back then we were about 4 orders of magnitude away from hitting them. We’re probably now closer to 3. Yet scale is only part of the performance equation, a fairly big chunk of progress is from algorithmic or curation related contributions. The point of the article is: > But those skeptics are initially responding to the constant AI hype claims that we are exponentially growing to AGI. This is a meaningless statement or at best just strawmanning.
- t43562 5mo ago
  Why is the evidence on the side of the hype? Why do you assume something is size X just because nobody has proved it's smaller yet? The evidence is just whatever it is - we cannot make predictions with it.
  - aspenmartin 5mo ago
    We have very stable trends in observed performance in a LOT of different measurements and stable scaling laws that continue to hold true across at the largest scales we have built. These scaling laws have a reasonable theoretical justification and make predictions which continue to hold true. Pretraining perplexity does seem related to downstream measures of performance but those too show very stable trends in performance gains as well, even if you look at these in isolation. Look at the epoch capability index as a good summary statistic across a number of benchmarks. So: yes we can and do make predictions with it and that’s how we get funding internally and externally to build at these scales. Bottlenecks arise about 3 orders of magnitude from now. On the other hand, any particular justification you have in mind for your point?
    - t43562 5mo ago
      > So: yes we can and do make predictions with it and that’s how we get funding internally and externally to build at these scales

by devmor 5mo ago

aspenmartin 5mo ago

Yet the evidence is on the side of the hype? We don’t see any mechanism or cogent framework for what limits exist here theoretically that I’m aware of, are you? Epoch had a great article a year ago looking at several bottlenecks in terms of scale and back then we were about 4 orders of magnitude away from hitting them. We’re probably now closer to 3. Yet scale is only part of the performance equation, a fairly big chunk of progress is from algorithmic or curation related contributions. The point of the article is: > But those skeptics are initially responding to the constant AI hype claims that we are exponentially growing to AGI. This is a meaningless statement or at best just strawmanning.

t43562 5mo ago
Why is the evidence on the side of the hype? Why do you assume something is size X just because nobody has proved it's smaller yet? The evidence is just whatever it is - we cannot make predictions with it.
- aspenmartin 5mo ago
  We have very stable trends in observed performance in a LOT of different measurements and stable scaling laws that continue to hold true across at the largest scales we have built. These scaling laws have a reasonable theoretical justification and make predictions which continue to hold true. Pretraining perplexity does seem related to downstream measures of performance but those too show very stable trends in performance gains as well, even if you look at these in isolation. Look at the epoch capability index as a good summary statistic across a number of benchmarks. So: yes we can and do make predictions with it and that’s how we get funding internally and externally to build at these scales. Bottlenecks arise about 3 orders of magnitude from now. On the other hand, any particular justification you have in mind for your point?
  - t43562 5mo ago
    > So: yes we can and do make predictions with it and that’s how we get funding internally and externally to build at these scales