I imagine it will be the de-facto way AI generates video in the next few years, once huge models are smart enough to use tools and work in high breadth and depth jobs like humans do.
I imagine it will be the de-facto way AI generates video in the next few years, once huge models are smart enough to use tools and work in high breadth and depth jobs like humans do.
Similarly, neural networks have already been shown to be able to directly simulate physics simulations (smoke, particles) with suprising accuracy.
I agree that logically your statement makes sense, however I'm not sure it matches what has been successful in the space so far. Tools may make sense for these NN's to use, however they probably won't look anything like the tools we use today.
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')?
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.
But video has much stricter physical constraints than image, so it's not clear we can ignore the problem at all.
A major topic of research is to make differentiable renderers, which can then have their parameters tuned via gradient descent, much like how AIs are trained.
The problem is that most renderers are very discrete, and hence aren't differentiable. For the same reason that reverse rendering is difficult on such systems, it's hard to train an AI to use them also.
Having said that, I can imagine it happening. Look at Google's Alpha Star and related AIs, which can play games with discrete events at grandmaster level. You could train an AI to operate a 3D design program similarly. The input training could be recordings of actions taken by human designers, or simply train the AI by making it match target images. Start out simple, and then crank up the complexity over time.