I think that it’s the opposite. This algorithm requires many examples of text on the specific topic. Probably more than most humans would require. > While typi

I think that it’s the opposite. This algorithm requires many examples of text on the specific topic. Probably more than most humans would require. > 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/ 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 https://arxiv.org/abs/2005.14165 [1]https://www.quantamagazine.org/to-build-truly-intelligent-machines-teach-them-cause-and-effect-20180515/ https://www.quantamagazine.org/to-build-truly-intelligent-ma...