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Hmm, I think you're right -- I should have phrased things differently. It's not that I don't think AGI won't require a very large model, it's more that I think
by mkaic 5y ago
Hmm, I think you're right -- I should have phrased things differently. It's not that I don't think AGI won't require a very large model, it's more that I think that we're already pretty close to the scale we need, so OpenAI's goal of scaling another 1000x isn't really the direction I think we should be heading. I honestly think 175B parameters could very well be enough for an AGI if they were organized efficiently.
It's definitely one of the weak points of the article, though, as it's more based on my own opinions, and isn't really empirical. My post is mostly just wild speculation for the sake of speculation anyway :)
Thank you for your comments!
- mountainriver 5y agoOkay yeah totally, that makes sense. We also have stopped seeing huge gains in scaling beyond GPT3 sized models which would indicate we have hit some maximum there. Although we thought we had hit a maximum with deep learning before the transformer came around so it could be misleading. Distributing the computation to many models like what Google did with GLaM could very well be the future of AGI. Economies of models rather than one big model.