201–210 of 1001 posts
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“A frame is worth a billion rays” The last production I worked on averaged 16 hours per frame for the final rendering. The amount of information encoded in lighting, models, texture, maps, etc is insane.
What were you working on? It took a month to render 2 seconds of video?
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The adage "a picture is worth a thousand words" has the nice corollary "A thousand words isn't enough to be precise about an image". Now expand that to movies and games and you can get why this whole generative-AI bubble is going to pop.
> Now expand that to movies and games and you can get why this whole generative-AI bubble is going to pop. What will save it is that, no matter how picky you are as a creator, your audience will never know what exactly was that you dreamed up, so any half-decent approximation will work. In other words, a corollary to your corollary is, "Fortunately, you don't need them to be, because no one cares about low-order bits…
Right now AI is more the latter, but many people want it to be the former
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What are the leading alternatives? (Open source or otherwise)
Minimax (from China) and Kling 1.5 from China. Recently Tencent launched its own. You can see more model samples heee https://youtu.be/bCAV_9O1ioc
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I don't work there so I'm certain there is no one with enough knowledge to make it work with Hamiltonian constraints because the idea is very obvious but they haven't done it because they don't have the wherewithal to do so. In other words, no one at OpenAI understands enough basic physics to incorporate conservation principles into the generative network so that objects with random masses don't appear and disappear…
> the idea is very obvious but they haven't done it because they don't have the wherewithal to do so Fascinating! I wish I had the knowledge and wherewithal to do that and become rich instead of wasting my time on HN.
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The adage "a picture is worth a thousand words" has the nice corollary "A thousand words isn't enough to be precise about an image". Now expand that to movies and games and you can get why this whole generative-AI bubble is going to pop.
> Now expand that to movies and games and you can get why this whole generative-AI bubble is going to pop. What will save it is that, no matter how picky you are as a creator, your audience will never know what exactly was that you dreamed up, so any half-decent approximation will work. In other words, a corollary to your corollary is, "Fortunately, you don't need them to be, because no one cares about low-order bits…
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What were you working on? It took a month to render 2 seconds of video?
I would guess there is more than one computer :) Pixar's stuff famously takes days per frame.
Do you have a citation for this? My guess would be much closer to a couple of hours per frame.
I got lucky and got in moments after it launched, managed to get a video of "A pelican riding a bicycle along a coastal path overlooking a harbor" and then the queue times jumped up (my second video has been in the queue for 20+ minutes already) and the https://sora.com site now says "account creation currently unavailable" Here's my pelican video: https://simonwillison.net/2024/Dec/9/sora/
I got lucky and got in moments after it launched, managed to get a video of "A pelican riding a bicycle along a coastal path overlooking a harbor" and then the queue times jumped up (my second video has been in the queue for 20+ minutes already) and the https://sora.com site now says "account creation currently unavailable" Here's my pelican video: https://simonwillison.net/2024/Dec/9/sora/
Verdict 4/10
I got lucky and got in moments after it launched, managed to get a video of "A pelican riding a bicycle along a coastal path overlooking a harbor" and then the queue times jumped up (my second video has been in the queue for 20+ minutes already) and the https://sora.com site now says "account creation currently unavailable" Here's my pelican video: https://simonwillison.net/2024/Dec/9/sora/
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Neural networks use smooth manifolds as their underlying inductive bias so in theory it should be possible to incorporate smooth kinematic and Hamiltonian constraints but I am certain no one at OpenAI actually understands enough of the theory to figure out how to do that.
How does your conclusion follow from your statement? Neural networks are largely black box piles of linear algebra which are massaged to minimize a loss function. How would you incorporate smooth kinematic motion in such an environment? The fact that you discount the knowledge of literally every single employee at OpenAI is a big signal that you have no idea what you’re talking about. I don’t even really like OpenAI…