Are we in an AI Overhang?
I&#x27;ve been wondering about something:
(I only know the basics of AI, this might be kinda incoherent)<p>Right now if you look at GTP-3&#x27;s output it seems like it&#x27;s approaching a convincing approximation of a bluffing college student writing a bad paper, correct sentences and stuff but very &#x27;cocky&#x27;. It cannot tell right from wrong, and it will just make up convincing rubbish, &#x27;hoping&#x27; to fool the reader. (I know I&#x27;m anthropomorphizing but bear with me).<p>Current models are being trained on a huge amount of internet text. As smarmy denizens of hackernews we know that people are very often wrong (or &#x27;not even wrong&#x27;) on the internet. It seems to me that anything trained on internet data is kinda doomed to poison itself on the high ratio of garbage floating around here?<p>We&#x27;ve seen with a lot of machine-learning stuff that biased data will create biased models, so you have to be really careful what you train it on. The dataset on which GTP-n has to be trained has to be pretty huge(?); and moderation is hard(?) and doesn&#x27;t scale; it&#x27;s easier to generate falsehood than truth; and the further we go along the more of internet data will be (weaponized?) output of GTP-(n-1); So won&#x27;t the arrival of AGI just be sabotaged by the arrival of AGI?<p>Has anyone written something about the process of building AGI that deals with this?