A massive technological leap for graphics - and we've been hands-on with four games.
I’m completely speechless. This looks so terrible I thought it was a joke, but apparently Nvidia released these demos to impress people. DLSS 5 runs the entire game through an AI filter, making every character look like it’s running through an ultra realistic beauty filter.
The photo above is used as the promo image for the official blog post by the way. It completely ignores artistic intent and makes Grace’s face look “sexier” because apparently that’s what realism looks like now.
I wouldn’t be so baffled if this was some experimental setting they were testing, but they’re advertising this as the next gen DLSS. As in, this is their image of what the future of gaming should be. A massive F U to every artist in the industry. Well done, Nvidia.
Though depending on how much context they pull in from neighbours, you can get overfitting, like a character might end up with a different face depending on what’s in the background. Vague unreadable text could be replaced with random readable text that also heavily depends on what is seen elsewhere in the frame (assuming it’s trained on enough text that if doesn’t just make legible text illegible).
So while it can be deterministic, it can still be highly chaotic, depending on how exactly it gets implemented. And I’d guess that it’s a double edged sword where sometimes more context gives better results and other times less context gives better results. And I’d also guess that if you want to avoid overfitting, the amount of training data needs to increase exponentially with the number of inputs, assuming there’s enough neurons in the network to encode the necessary complexity.
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NNs are deterministic. Chatbot and image generator implementations just purposely add randomization to make them seem more intelligent.
Though depending on how much context they pull in from neighbours, you can get overfitting, like a character might end up with a different face depending on what’s in the background. Vague unreadable text could be replaced with random readable text that also heavily depends on what is seen elsewhere in the frame (assuming it’s trained on enough text that if doesn’t just make legible text illegible).
So while it can be deterministic, it can still be highly chaotic, depending on how exactly it gets implemented. And I’d guess that it’s a double edged sword where sometimes more context gives better results and other times less context gives better results. And I’d also guess that if you want to avoid overfitting, the amount of training data needs to increase exponentially with the number of inputs, assuming there’s enough neurons in the network to encode the necessary complexity.