The best angle of attack here I think, is synthesising knowledge from multiple domains. I was able to get GPT-3 to write and then translate a Japanese poem about a (fictional) ancient language model into Chinese, Hungarian, and Swahili and annotate all of its translations with stylistic notes and historical references. I don’t think any humans have the knowledge required to do that, but unsurprisingly GPT-3 does, and performed better when I used the premise of multiple humans collaborating. It’s said that getting different university departments to collaborate tends to be very productive wrt new papers being published. The only bottleneck is whether its dataset includes scientific publications and the extent to which it can draw upon memorised knowledge (parameter count).
The best angle of attack here I think, is synthesising knowledge from multiple domains. I was able to get GPT-3 to write and then translate a Japanese poem about a (fictional) ancient language model into Chinese, Hungarian, and Swahili and annotate all of its translations with stylistic notes and historical references. I don’t think any humans have the knowledge required to do that, but unsurprisingly GPT-3 does, and performed better when I used the premise of multiple humans collaborating. It’s said that getting different university departments to collaborate tends to be very productive wrt new papers being published. The only bottleneck is whether its dataset includes scientific publications and the extent to which it can draw upon memorised knowledge (parameter count).
Awesome example!