>> it has been easier (so far) to directly compute the images than it has been to train them to use photoshop or illustrator.
possible because training models are fed by an abundance of existing human-generated content, which is ever increasing in quality, dirt-cheap, and easily accessible; which doesn't make AI Generated Content disruptive in this respect (my view).
not sure what you mean by "easier", since computers are better at following rules and people better at reasoning.
>> I agree that logically your statement makes sense ...
Isn't that the best case for AI generated content, logical-sense? or are you suggesting more (e.g 'source of meaning')?
> Isn't that the best case for AI generated content, logical-sense? or are you suggesting more (e.g 'source of meaning')?
I just mean that the approach sounds logical, although the best approach isn’t always the one that appears the most logical. This appears particularly true in AI - for instance diffusion models feel more like an incredibly surprising discovery rather than an approach that’s immediately obvious by applying logic.
> Not sure what you mean by "easier", since computers are better at following rules and people better at reasoning.
By easier I just mean we have had better results so far. Maybe easier wasn't the right word. On your second point - computers will be better at most reasoning tasks shortly.
possible because training models are fed by an abundance of existing human-generated content, which is ever increasing in quality, dirt-cheap, and easily accessible; which doesn't make AI Generated Content disruptive in this respect (my view).
not sure what you mean by "easier", since computers are better at following rules and people better at reasoning.
>> I agree that logically your statement makes sense ...
Isn't that the best case for AI generated content, logical-sense? or are you suggesting more (e.g 'source of meaning')?