6 ms·

I think the implications go much further than just the image/video considerations. This model shows a very good (albeit not perfect) understanding of the physi

I think the implications go much further than just the image/video considerations. This model shows a very good (albeit not perfect) understanding of the physics of objects and relationships between them. The announcement mentions this several times. The OpenAI blog post lists "Archeologists discover a generic plastic chair in the desert, excavating and dusting it with great care." as one of the "failed" cases. But this (and "Reflections in the window of a train traveling through the Tokyo suburbs.") seem to me to be 2 of the most important examples. - In the Tokyo one, the model is smart enough to figure out that on a train, the reflection would be of a passenger, and the passenger has Asian traits since this is Tokyo. - In the chair one, OpenAI says the model failed to model the physics of the object (which hints that it did try to, which is not how the early diffusion models worked ; they just tried to generate "plausible" images). And we can see one of the archeologists basically chasing the chair down to grab it, which does correctly model the interaction with a floating object. I think we can't underestimate how crucial that is to the building of a general model that has a strong model of the world. Not just a "theory of mind", but a litteral understanding of "what will happen next", independently of "what would a human say would happen next" (which is what the usual text-based models seem to do). This is going to be much more important, IMO, than the video aspect.

seydor 3y ago
Facebook released something in that direction today https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/ https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-jo...
- sebastiennight 3y ago
  Wow this is a huge announcement too, I can't believe this hasn't made the front page yet.
  - deleted 3y ago
    [deleted]
gspetr 3y ago
This seems to be completely in line with the previous "AI is good when it's not news" type of work: Non-news: Dog bites a man. News: Man bites a dog. Non-news: "People riding Tokyo train" - completely ordinary, tons of similar content. News: "Archaeologists dust off a plastic chair" - bizarre, (virtually) no similar content exists.
RhysU 3y ago
> very good... understanding of the physics of objects and relationships between them I am always torn here. A real physics engine has a better "understanding" but I suspect that word applies to neither Sora nor a physics engine: https://www.wikipedia.org/wiki/Chinese_room https://www.wikipedia.org/wiki/Chinese_room An understanding of physics would entail asking this generative network to invert gravity, change the density or energy output of something, or atypically reduce a coefficient of friction partway through a video. Perhaps Sora can handle these, but I suspect it is mimicking the usual world rather than understanding physics in any strong sense. None of which is to say their accomplishment isn't impressive. Only that "understand" merits particularly careful use these days.
- mewpmewp2 3y ago
  Question is - how much do you need to understand something in order to mimick it? The Chinese Room seems to however point to some sort of prewritten if-else type of algorithm type of situation. E.g. someone following scripted algorithmic procedures might not understand the content, but obviously this simplification is not the case with LLMs or this video generation, as the algorithmic scripting requires pre-written scripts. Chinese Room seems to more refer to cases like "if someone tells me "xyz", then respond with "abc" - of course then you don't understand what xyz or abc mean, but it's not referring to neural networks training on ton of material to build this model representation of things.
  - RhysU 3y ago
    Good points. Perhaps building the representation is building understanding. But humans did that for Sora and for all the other architectures too (if you'll allow a little meta-building). But evaluation alone is not understanding. Evaluation is merely following a rote sequence of operations, just like the physics engine or the Chinese room. People recognize this distinction all the time when kids memorize mathematical steps in elementary school but they do not yet know which specific steps to apply for a particular problem. This kid does not yet understand because this kid guesses. Sora just happens to guess with an incredibly complicated set of steps. (I guess.)
sva_ 3y ago
I found the one about the people in Lagos pretty funny. The camera does about a 360deg spin in total, in the beginning there are markets, then suddenly there are skyscrapers in the background. So there's only very limited object permanence. > A beautiful homemade video showing the people of Lagos, Nigeria in the year 2056. Shot with a mobile phone camera. > https://cdn.openai.com/sora/videos/lagos.mp4 https://cdn.openai.com/sora/videos/lagos.mp4
- PoignardAzur 3y ago
  Yeah, some of the continuity errors in that one feel horrifying.
- vingt_regards 3y ago
  There are also perspective issues: the relative sizes of the foreground (the people sitting at the café) and the background (the market) are incoherent. Same with the "snowy Tokyo with cherry blossoms" video.
- po 3y ago
  In the video of the girl walking down the Tokyo city street, she's wearing a leather jacket. After the closeup on her face they pull back and the leather jacket has hilariously large lapels that weren't there before.
  - ketzo 3y ago
    Object permanence (just from images/video) seems like a particularly hard problem for a super-smart prediction engine. Is it the old thing, or a new thing?
- bamboozled 3y ago
  Also the women in red next to the people is very tiny and the market stall is also a mini market stall, and the table is made out of a bike. For everyone that's carrying on about this thing understanding physics and has a model of the world...it's an odd world.
XCSme 3y ago
It doesn't understand physics. It just computes next frame based on current one and what it learned before, it's a plausible continuation. In the same way, ChatGPT struggles with math without code interpreter, Sora won't have accurate physics without a physics engine and rendering 3d objects. Now it's just a "what is the next frame of this 2D image" model plus some textual context.
- yberreby 3y ago
  > It just computes next frame based on current one and what it learned before, it's a plausible continuation. ... > Now it's just a "what is the next frame of this 2D image" model plus some textual context. This is incorrect. Sora is not an autoregressive model like GPT, but a diffusion transformer. From the technical report[1], it is clear that it predicts the entire sequence of spatiotemporal patches at once. [1]: https://openai.com/research/video-generation-models-as-world-simulators https://openai.com/research/video-generation-models-as-world...
  - XCSme 3y ago
    Good link. But, even there it says: > Sora currently exhibits numerous limitations as a simulator. For example, it does not accurately model the physics of many basic interactions, like glass shattering. Other interactions, like eating food, do not always yield correct changes in object states Regardless whether all the frames are generated at once, or one by one, you can see in their examples it's still just pixel based. See the first example with the dog with blue hat, the woman has a blue thing suddenly spawn into her hand because her hand went over another blue area of the image.
    - yberreby 3y ago
      I'm not denying that there are obvious limitations. However, attributing them to being "pixel-based" seems misguided. First off, the model acts in latent space, not directly on pixels. Secondly, there is no fundamental limitation here. The model has already acquired limited-yet-impressive ability to understand movement, texture, social behavior, etc., just from watching videos. I learned to understand reality by interpreting photons and various sensory inputs. Does that make my model of reality fundamentally flawed? In the sense that I only have a partial intuitive understanding of it, yes. But I don't need to know Maxwell's equations to get a sense of what happens when I open the blinds or turn on my phone. I think many of the limitations we are seeing here - poor glass physics, flawed object permanence - will be overcome given enough training data and compute. We will most likely need to incorporate exploration, but we can get really far with astute observation.
shostack 3y ago
I'm hoping to see progress towards consistent characters, objects, scenes etc. So much of what I'd want to do creatively hinges on needing persisting characters who don't change appearance/clothing/accessories from usage to usage. Or creating a "set" for a scene to take place in repeatedly. I know with stable diffusion there's things like lora and controlnet, but they are clunky. We still seem to have a long way to go towards scene and story composition. Once we do, it will be a game changer for redefining how we think about things like movies and television when you can effectively have them created on demand.
bamboozled 3y ago
Wouldn't having a good understanding of physics mean you know that a women doesn't slide down the road when she walks? Wouldn't it know that a woolly mammoth doesn't emit profuse amounts steam when walking on frozen snow? Wouldn't the model know that legs are solid objects in which other object cannot pass through? Maybe I'm missing the big picture here, but the above and all the weird spatial errors, like miniaturization of people make me think you're wrong. Clearly the model is an achievement and doing something interesting to produce these videos, and they are pretty cool, but understanding physics seems like quite a stretch? I also don't really get the excitement about the girl on the train in Tokyo: In the Tokyo one, the model is smart enough to figure out that on a train, the reflection would be of a passenger, and the passenger has Asian traits since this is Tokyo I don't know a lot about how this model works personally, but I'm guessing in the training data the vast majority of people riding trains in Tokyo featured asian people in them, assuming this model works on statistics like all of the other models I've seen recently from Open AI, then why is it interesting the girl in the reflection was Asian? Did you not expect that?
- pests 3y ago
  They could test this by trying to generate the same image but set in New York, etc. I bet it would still be asain.
- pera 3y ago
  I agree, to me the most clear example is how the rocks in the sea vanish/transform after the wave: The generated frames are hyperreal for sure, but the represented space looks as consistent as a dream.
- csomar 3y ago
  > Wouldn't having a good understanding of physics mean you know that a women doesn't slide down the road when she walks? Wouldn't it know that a woolly mammoth doesn't emit profuse amounts steam when walking on frozen snow? Wouldn't the model know that legs are solid objects in which other object cannot pass through? This just hit me but humans do not have a good understanding of physics; or maybe most of humans have no understanding of physics. We just observe and recognize whether it's familiar or not. AI will need to be, that being the case, way more powerful than a human mind. Maybe orders of magnitude more "neural networks" than a human brain has.
timdiggerm 3y ago
> In the Tokyo one, the model is smart enough to figure out that on a train, the reflection would be of a passenger, and the passenger has Asian traits since this is Tokyo. How is this any more accurate than saying that the model has mostly seen Asian people in footage of Tokyo, and thus it is most likely to generate Asian-features for a video labelled "Tokyo"? Similarly, how many videos looking out a train window do you think it's seen where there was not a reflection of a person in the window when it's dark?

seydor 3y ago

Facebook released something in that direction today https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/ https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-jo...

sebastiennight 3y ago
Wow this is a huge announcement too, I can't believe this hasn't made the front page yet.
- deleted 3y ago
  [deleted]

gspetr 3y ago

This seems to be completely in line with the previous "AI is good when it's not news" type of work: Non-news: Dog bites a man. News: Man bites a dog. Non-news: "People riding Tokyo train" - completely ordinary, tons of similar content. News: "Archaeologists dust off a plastic chair" - bizarre, (virtually) no similar content exists.

RhysU 3y ago

> very good... understanding of the physics of objects and relationships between them I am always torn here. A real physics engine has a better "understanding" but I suspect that word applies to neither Sora nor a physics engine: https://www.wikipedia.org/wiki/Chinese_room https://www.wikipedia.org/wiki/Chinese_room An understanding of physics would entail asking this generative network to invert gravity, change the density or energy output of something, or atypically reduce a coefficient of friction partway through a video. Perhaps Sora can handle these, but I suspect it is mimicking the usual world rather than understanding physics in any strong sense. None of which is to say their accomplishment isn't impressive. Only that "understand" merits particularly careful use these days.

mewpmewp2 3y ago
Question is - how much do you need to understand something in order to mimick it? The Chinese Room seems to however point to some sort of prewritten if-else type of algorithm type of situation. E.g. someone following scripted algorithmic procedures might not understand the content, but obviously this simplification is not the case with LLMs or this video generation, as the algorithmic scripting requires pre-written scripts. Chinese Room seems to more refer to cases like "if someone tells me "xyz", then respond with "abc" - of course then you don't understand what xyz or abc mean, but it's not referring to neural networks training on ton of material to build this model representation of things.
- RhysU 3y ago
  Good points. Perhaps building the representation is building understanding. But humans did that for Sora and for all the other architectures too (if you'll allow a little meta-building). But evaluation alone is not understanding. Evaluation is merely following a rote sequence of operations, just like the physics engine or the Chinese room. People recognize this distinction all the time when kids memorize mathematical steps in elementary school but they do not yet know which specific steps to apply for a particular problem. This kid does not yet understand because this kid guesses. Sora just happens to guess with an incredibly complicated set of steps. (I guess.)

sva_ 3y ago

I found the one about the people in Lagos pretty funny. The camera does about a 360deg spin in total, in the beginning there are markets, then suddenly there are skyscrapers in the background. So there's only very limited object permanence. > A beautiful homemade video showing the people of Lagos, Nigeria in the year 2056. Shot with a mobile phone camera. > https://cdn.openai.com/sora/videos/lagos.mp4 https://cdn.openai.com/sora/videos/lagos.mp4

PoignardAzur 3y ago
Yeah, some of the continuity errors in that one feel horrifying.
vingt_regards 3y ago
There are also perspective issues: the relative sizes of the foreground (the people sitting at the café) and the background (the market) are incoherent. Same with the "snowy Tokyo with cherry blossoms" video.
po 3y ago
In the video of the girl walking down the Tokyo city street, she's wearing a leather jacket. After the closeup on her face they pull back and the leather jacket has hilariously large lapels that weren't there before.
- ketzo 3y ago
  Object permanence (just from images/video) seems like a particularly hard problem for a super-smart prediction engine. Is it the old thing, or a new thing?
bamboozled 3y ago
Also the women in red next to the people is very tiny and the market stall is also a mini market stall, and the table is made out of a bike. For everyone that's carrying on about this thing understanding physics and has a model of the world...it's an odd world.

XCSme 3y ago

It doesn't understand physics. It just computes next frame based on current one and what it learned before, it's a plausible continuation. In the same way, ChatGPT struggles with math without code interpreter, Sora won't have accurate physics without a physics engine and rendering 3d objects. Now it's just a "what is the next frame of this 2D image" model plus some textual context.

yberreby 3y ago
> It just computes next frame based on current one and what it learned before, it's a plausible continuation. ... > Now it's just a "what is the next frame of this 2D image" model plus some textual context. This is incorrect. Sora is not an autoregressive model like GPT, but a diffusion transformer. From the technical report[1], it is clear that it predicts the entire sequence of spatiotemporal patches at once. [1]: https://openai.com/research/video-generation-models-as-world-simulators https://openai.com/research/video-generation-models-as-world...
- XCSme 3y ago
  Good link. But, even there it says: > Sora currently exhibits numerous limitations as a simulator. For example, it does not accurately model the physics of many basic interactions, like glass shattering. Other interactions, like eating food, do not always yield correct changes in object states Regardless whether all the frames are generated at once, or one by one, you can see in their examples it's still just pixel based. See the first example with the dog with blue hat, the woman has a blue thing suddenly spawn into her hand because her hand went over another blue area of the image.
  - yberreby 3y ago
    I'm not denying that there are obvious limitations. However, attributing them to being "pixel-based" seems misguided. First off, the model acts in latent space, not directly on pixels. Secondly, there is no fundamental limitation here. The model has already acquired limited-yet-impressive ability to understand movement, texture, social behavior, etc., just from watching videos. I learned to understand reality by interpreting photons and various sensory inputs. Does that make my model of reality fundamentally flawed? In the sense that I only have a partial intuitive understanding of it, yes. But I don't need to know Maxwell's equations to get a sense of what happens when I open the blinds or turn on my phone. I think many of the limitations we are seeing here - poor glass physics, flawed object permanence - will be overcome given enough training data and compute. We will most likely need to incorporate exploration, but we can get really far with astute observation.

shostack 3y ago

I'm hoping to see progress towards consistent characters, objects, scenes etc. So much of what I'd want to do creatively hinges on needing persisting characters who don't change appearance/clothing/accessories from usage to usage. Or creating a "set" for a scene to take place in repeatedly. I know with stable diffusion there's things like lora and controlnet, but they are clunky. We still seem to have a long way to go towards scene and story composition. Once we do, it will be a game changer for redefining how we think about things like movies and television when you can effectively have them created on demand.

bamboozled 3y ago

Wouldn't having a good understanding of physics mean you know that a women doesn't slide down the road when she walks? Wouldn't it know that a woolly mammoth doesn't emit profuse amounts steam when walking on frozen snow? Wouldn't the model know that legs are solid objects in which other object cannot pass through? Maybe I'm missing the big picture here, but the above and all the weird spatial errors, like miniaturization of people make me think you're wrong. Clearly the model is an achievement and doing something interesting to produce these videos, and they are pretty cool, but understanding physics seems like quite a stretch? I also don't really get the excitement about the girl on the train in Tokyo: In the Tokyo one, the model is smart enough to figure out that on a train, the reflection would be of a passenger, and the passenger has Asian traits since this is Tokyo I don't know a lot about how this model works personally, but I'm guessing in the training data the vast majority of people riding trains in Tokyo featured asian people in them, assuming this model works on statistics like all of the other models I've seen recently from Open AI, then why is it interesting the girl in the reflection was Asian? Did you not expect that?

pests 3y ago
They could test this by trying to generate the same image but set in New York, etc. I bet it would still be asain.
pera 3y ago
I agree, to me the most clear example is how the rocks in the sea vanish/transform after the wave: The generated frames are hyperreal for sure, but the represented space looks as consistent as a dream.
csomar 3y ago
> Wouldn't having a good understanding of physics mean you know that a women doesn't slide down the road when she walks? Wouldn't it know that a woolly mammoth doesn't emit profuse amounts steam when walking on frozen snow? Wouldn't the model know that legs are solid objects in which other object cannot pass through? This just hit me but humans do not have a good understanding of physics; or maybe most of humans have no understanding of physics. We just observe and recognize whether it's familiar or not. AI will need to be, that being the case, way more powerful than a human mind. Maybe orders of magnitude more "neural networks" than a human brain has.

timdiggerm 3y ago

> In the Tokyo one, the model is smart enough to figure out that on a train, the reflection would be of a passenger, and the passenger has Asian traits since this is Tokyo. How is this any more accurate than saying that the model has mostly seen Asian people in footage of Tokyo, and thus it is most likely to generate Asian-features for a video labelled "Tokyo"? Similarly, how many videos looking out a train window do you think it's seen where there was not a reflection of a person in the window when it's dark?