>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.
Clearly that is not true for the commenter that started this thread.
Re:Yann LeCun on GPT-3
by sillysaurusx on Tuesday October 27, 2020 @12:55PM
(#24909294)
What other proof would you like, other than an example of what I wanted to do and can't?
(https://www.reddit.com/r/SubSimulatorGPT2/ but for HN.)
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.
GPT-3 not being suitable for your particular use case does not mean that all use cases are shallow and limited?
That being said, I'm not sure I understand why you can't use GPT-3 to make an HN simulator.
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.