> While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples [0]
I don’t know what constitutes an example in this case but let’s assume it means 1 blog article. I don’t know many humans that read thousands or tens of thousands of blog articles on a specific topic. And if I did I’d expect that human to write a much more interesting article.
To me, this and other similar generated texts from OpenAI feel bland / generic.
Take a listen to the generated music from OpenAI - https://openai.com/blog/jukebox/. It’s pretty bad, but in a weird way. It’s technically correct - in key, on beat, ect. And even some of the music it generates is technically hard to do, but it sounds so painfully generic.
> All the impressive achievements of deep learning amount to just curve fitting Judea Perl [1]
This comment was written by a human :)
[0]https://arxiv.org/abs/2005.14165 [1]https://www.quantamagazine.org/to-build-truly-intelligent-ma...