It sounds like a big deal. What a tempting idea. And a colleague was mildly annoyed with me for how unimpressed I seemed.
But you have to understand, the use cases you mention are shallow and limited. The heart of GPT, the fine-tuning, is gone. And it looks like even OpenAI gave up on letting users fine-tune, because it means they essentially do build an entirely new, expensive model for each use case.
I wanted to make an HN Simulator, the way that https://www.reddit.com/r/SubSimulatorGPT2/ works. But that's far beyond the capabilities of metalearning (the idea that you describe).
I think the onus is on you to prove that the use cases are shallow and limited. I've seen GPT-3 already being used for diverse and interesting ideas that would not have occurred to me personally.
However, even if they are, the point stands: currently, there are teams of people at companies all over the world tuning models for these shallow and limited use-cases. GPT-3 can replace them all, without OpenAI needing to invest another cent in training for a particular customer's use-case. That is in fact game-changing for the ML/DL world and current applications thereof.
Is it AGI? Obviously not. But the vast majority of ML applications don't need to be.
For a more extensive rebuttal, I wrote one here. https://news.ycombinator.com/item?id=23346972 Though that was more a rebut of GPT in general as a path to AGI than metalearning in particular for generating memes.
>However, even if they are, the point stands: currently, there are teams of people at companies all over the world tuning models for these shallow and limited use-cases. GPT-3 can replace them all, without OpenAI needing to invest another cent in training for a particular customer's use-case. That is in fact game-changing for the ML/DL world and current applications thereof.
The counterpoint is that it would be significantly cheaper AND have better performance to fine-tune models to each customer's use case than it is to just run GPT-3 at inference.
But you have to understand, the use cases you mention are shallow and limited. The heart of GPT, the fine-tuning, is gone. And it looks like even OpenAI gave up on letting users fine-tune, because it means they essentially do build an entirely new, expensive model for each use case.
I wanted to make an HN Simulator, the way that https://www.reddit.com/r/SubSimulatorGPT2/ works. But that's far beyond the capabilities of metalearning (the idea that you describe).
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