181–190 of 1001 posts
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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…
I've found using these and similar tools that the amount of prompts and iteration required to create my vision (image or video in my mind) is very large and often is not able to create what I had originally wanted. A way to test this is to take a piece of footage or an image which is the ground truth, and test how much prompting and editing it takes to get the same or similar ground truth starting from scratch. It is…
AI isn't trying to sell to you: a precise artist with real vision in your brain. It is selling to managers who want to shit out something in an evening that approximates anything, that writes ads that no one wants to see anyway, that produces surface level examples of how you can pay employees less because "their job is so easy"
I feel like there is a sweet spot for AI generation of images and videos that I would describe as "charmingly bad", like the stuff we got from the old CLIP+VQGAN models. I feel like Sora has jumped past that into the valley of "unappealingly bad".
Not impressive compare to the opensource video models out there, I anticipated some physics/VR capabilities, but it's basically just a marketing promotion to "stay in the game"...
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It just plain isn't possible if you mean a prompt the size of what most people have been using lately, in the couple hundred character range. By sheer information theory, the number of possible interpretations of "a zoom in on a happy dog catching a frisbee" means that you can not match a particular clip out of the set with just that much text. You will need vastly more content; information about the breed, informati…
something like a white paper with a mood board, color scheme, and concept art as the input might work. This could be sent into an LLM "expander" that increases the words and speficity. Then multiple reviews to tap things in the right direction.
The real trick is that the AI needs to be able to participate in iteration cycles, where the human can say "okay this is all mostly good, but I've circled some areas that don't look quite right and described what needs to be different about them." As far as I've played with it, current AIs aren't very good at revisiting their own work— you're basically just tweaking the original inputs and otherwise starting over from scratch each time.
Wow this is bad. And by bad i mean worse than leading open source and existing alternatives. Is it me or does it seem like OpenAI revolutionized with both chatGPT and Sora, but they've completely hit the ceiling? Honestly a bit surprised it happened so fast!