[minimaxir]

> But they are so scared of their chatbot saying a bad thing and the PR around that they've removed the possibility of doing anything useful with it.

It's not unreasonable to have checks-and-balances on AI content, and there should be.

However, in my testing of GPT-3's content filter when it was released (it could be improved now), it was very sensitive to the point that it had tons of false positives. Given that passing content filter checks is required for productionizing a GPT-3 app, it makes using the API too risky to use, and part of the reason I'm researching more with train-your-own GPT models.

[nradov]

Why should there be checks and balances on AI content? What most people label as "AI" today is literally just fancy statistics. Should there be checks and balances on the use of linear regression analysis and other statistical techniques? Where do we draw the line?

[minimaxir]

> Should there be checks and balances on the use of linear regression analysis and other statistical techniques?

That rhetorical question actually argues against your point: even in academic contexts, statistics can be used (intentionally or otherwise) to argue incorrect/misleading points, which is why reputable institutions have peer reviews/boards as a level of validation for papers.

The point I was making was more on general content moderation in response to user-generated content, which is required for every service that does so for legal reasons at minimum, as they're the ones who will get blamed if something goes wrong.

[mola]

Ofcourse statistical techniques need checks and balances, hence peer reviewed academic papers, meta analysis, etc. statistics is a major tool for science these days. science needs checks and balances otherwise it's a pretty idle effort. Without checks and balances, you could just imagine any theory and believe it's the truth because you want to.