Who cares at this point? No one is stopping ML sets from being primarily pirated. The current power is effectively dismantling copyright for AI related work.
My "lawyer" (gpt4o) claims that since YouTube is merely a non-exclusive licensee of the user content upload to their service, even if they have such restrictions in their ToS (they do), they likely would not hold up in court, citing [0]. Something about that non-exclusivity meaning they cannot constrain the copyright further on their own terms. Which I guess makes sense?
And since scraping of publicly available data is not illegal (in the US, according to the aforementioned "lawyer"), it seems like it's okay?
Per HiQ vs. LinkedIn, it doesn't matter what their ToS says if the scraper didn't have to agree to the ToS to scrape the data. YouTube will serve videos to someone who isn't logged in. So if you've never agreed to YouTube's ToS, you can scrape the videos. If YT forced everyone to log in before they could watch a video, then anyone who wants to scrape videos would have had to agree to the ToS at some point.
This is interesting for generalized problems ("make me a sandwich") but not useful for most real world functions ("perform x within y space at z cost/speed"). I think the number of people on the humanoid bandwagon trying to implement generalized applications is staggering right now. The physics tells you they will never be as fast as purpose-built devices, nor as small, nor as cheap. That's not to say there's zero value there, but really we're - uh - grasping at straws...
I wonder if a generalized machine would have an advantage from scale, and then putting all the specialized stuff into software. We have seen this play out before.
Well, there’s a middle ground, kinda. Using more specialized hardware (ex: cobots) but deploy state-of-art Physical AI (ML/Computer Vision) on them. We’re building one such startup at ko-br (https://ko-br.com/) :))
Very good point! This area faces a similar misalignment of goals in that it tries to be a generic fit-all solution that is rampant with today's LLMs.
We made a sandwich but it cost you 10x more than it would a human and slower might slowly become faster and more efficient but by the time you get really good at it, its simply not transferable unless the model is genuinely able to make the leap across into other domains that humans naturally do.
I'm afraid this is where the barrier of general intelligence and human intelligence lies and with enough of these geospatial motor skill database, we might get something that mimics humans very well but still run into problems at the edge, and this last mile problem really is a hinderance to so many domains where we come close but never complete.
I wonder if this will change with some sort of computing changes as well as how we interface with digital systems (without mouse or keyboard), then this might be able to close that 'last mile gap'.
For a single example, in any factory watch how humans are added as ad-hoc machines wherever a problem occurs. Machine N outputting faster than machine N+1 can accept? Have a human stack, and destack, the product between them. No matter the size, shape, it within reason the weight of the product. But most importantly: the process can begin within seconds of the problem occurring. No need for a programmer, developer, or maintenance worker to get involved. Just a clear order from the shift manager.
A general purpose robot with physical interfaces similar to a human would be very valuable for such environments. If it had the software to be as easy to instruct as a human.
Your assumption set: conventional factory space, idle humans, traditional management, ad-hoc process with skilled managers. This is similar to the "job shop" mentality in (dying) manufacturing. You additionally assume general purpose magic hardware that can usefully do anything.
Reality: Most value is in shrinking things, excluding humans, automating management, carefully designed process, and specialist hardware that does a subset of things very well. Relying on human(oid)s is a sure-fire way to suck.
This writing style is prominent on Twitter and niche Discords. It's funny how much I've come to be able to cut right through it, but if you haven't seen much of it it's really hard to parse. That's by design, too. The vibe of this writing style is to project an air of confidence so strong that the author doesn't care if you get it or not. It's a sort of humblebrag where the writing is supposed to flex the author's understanding of the subject while also not caring if you get it or not.
As others have already covered, there's also some heavy stretching of the truth and rewriting of history going on in this post. That's also common of the extreme bravado in this style of semi-impenetrable writing: The vagueness and ambiguities allow the author to make grandiose claims but then wiggle out of them later if someone is astute enough to catch on.
For example: The blog post is written as “We…” but is the author part of the team? Or is he using “we” meaning society in general?
It would also be good if the perspective of the article would stay put. This "we" and "they" thing was at best confusing and at worst possibly a way to get more clicks or pretend the author had something to do with the work.
Yeah, wake me up when they have a robot that can wash, peel, cut fruit and vegetables; unwrap, cut, cook meat; measure salt and spices; whip cream; knead and shape dough; and clean up the resulting mess from all of these. Then they will have "solved" part of robotics.
>> Yeah, wake me up when they have a robot that can wash, peel, cut fruit and vegetables; unwrap, cut, cook meat; measure salt and spices; whip cream; knead and shape dough; and clean up the resulting mess from all of these.
Looks like it was trained on Shaolin Drunken Fist videos. Does it look drunk because of the videos or because there's a discrepancy between videos and it not accounting for gravity and physics in general?
Hello there! As a fellow gen-z douchebag, the article looks authentic, albeit a bit slim on Discord screencaps. Will be fun(?) to be proven wrong though.
> the core insight: predict in representation space, not pixels
We've been doing this since 2014? Not only that, others have been doing it at a similar scale. e.g. Nvidia's world foundation models (although those are generative).
> zero-shot generalization (aka the money shot)
This is easily beaten by flow-matching imitation learning models like what Pi has.
> accidentally solved robotics
They're doing 65% success on very simple tasks.
The research is good. This article however misses a lot of other work in the literature. I would recommend you don't read it as an authoritative source.
I just wrote a reply to a comment talking about the AI tells this writing has, but it got flagged so my comment disappeared when I hit post. I'll rephrase out of spite:
My first thought upon reading this was that an LLM had been instructed to add a pithy meme joke to each paragraph. They don't make sense in context, and while some terminally online people do speak in memes, those people aren't quoting doge in 2025.
There's also a sense of incoherence in the whole piece. For instance, this section:
"- after: 22 million videos + 1 million images (now we're talking)
they basically hoovered up everything: something-something v2, kinetics, howto100m, and a billion youtube videos"
Was it a billion vids or 22m? It turns out the latter sentence is just rephrasing the list of sources in a cool casual way, and the last one is called YT-Temporal-1B. That's a billion frames of video, not a billion videos.
Also, the author of the blog "Ksagar Atharva" doesn't appear anywhere in the list of authors on the linked FB research paper with Yann LeCun as a co-author. Unless the blog author is using a heavily modified pseudonym.
The research is very real but the blog post appears to be very fake.
> some terminally online people do speak in memes, those people aren't quoting doge in 2025.
You may be surprised to find out how incorrect this.
I can think of two popular conservative sites likely to quote Doge people off hand that do this. I read all news in order not be an insufferable ideologue. So again, off the top of my head, NotTheBee (I think affiliated to BabylonBee (conservative The Onion)) and Twitchy. Among YouTubers, I think Asmond Gold, and I’m sure others like Steven Crowder who himself is in a famous meme.
They are referring to the original doge meme of the dog, not the government initiative today. I guess "quote" isn't really the right word, more like "doing"
There was a clear attempt at the doge meme format, yes:
> very scientific. much engineering.
Emphasis on attempt because you're supposed to use words with grammatically incorrect modifiers, and the first one doesn't. (Even the second one doesn't seem entirely incorrect to me? I'm not a native speaker though.) "many scientific, so engineering" for example would have worked.
I assume they, or most likely their LLM, tried too hard to follow the most popular sequence (very, much, wow) and failed at it.
I don’t know, 400k people are listening to the White House streaming lo-fi hip hop on X right now with cutesy videos of Trump on one side and his executive orders streaming on the other at 4am. I think there’s plenty of people quoting doge in 2025.
If you’re in the US, you likely work with them and they have learned to studiously avoid talking about politics except in vagaries to avoid conflict.
I'm using eigenrobot's (X user) prompt for ChatGPT and the style is very recognizable. Everything lowercase, tone, zoomer abbreviations, esotheric style of jokes.
>> They don't make sense in context, and while some terminally online people do speak in memes, those people aren't quoting doge in 2025.
Cringely, they are. Nobody who isn't desperate to appear cool would write in that terminally grating register, including when using an LLM to do the writing.
My mom said I was throwing away my life watching YouTube all day and clearly I just haven’t been watching YouTube enough. 1 million YouTube videos here I come!
"why didn't we think of this sooner?", asks the article. Not sure who the "we" is supposed to be, but the robotics community has definitely thought of this before. https://robo-affordances.github.io/ from 2023 is one pretty relevant example that comes to mind, but I have recollections of similar ideas going back to at least 2016 or so (many of which are cited in the V-JEPA2 paper). If you think data-driven approaches are a good idea for manipulation, then the idea of trying to use Youtube as a source of data (an extremely popular data source in computer vision for the past decade) isn't exactly a huge leap. Of course, the "how" is the hard part, for all sorts of reasons. And the "how" is what makes this paper (and prior research in the area) interesting.
I definitely saw somebody at Actuate last year talking about supplementing training videos for VLA with Youtube, but I think they actually found that "any" video of the real world helped give a better physics "understanding" to the model.
I don't know. I'm not the expert, but if you've ever tried to a backflip or anything where your toes are above your head, then you'll know that spatial awareness goes well beyond vision. Or if you throw a frisbee for the dog to catch, they don't actually look at it while running; they look, predict position, then move in. Veni, vidi, vici. So any model that "learns physics" just through vision seems flawed from the start. What's your thought there?
Pure vision will never be enough because it does not contain information about the physical feedback like pressure and touch, or the strength required to perform a task.
For example, so that you don't crush a human when doing massage (but still need to press hard), or apply the right amount of force (and finesse?) to skin a fish fillet without cutting the skin itself.
Practically in the near term, it's hard to sample from failure examples with videos on Youtube, such as when food spills out of the pot accidentally. Studying simple tasks through the happy path makes it hard to get the robot to figure out how to do something until it succeeds, which can appear even in relatively simple jobs like shuffling garbage.
With that said, I suppose a robot can be made to practice in real life after learning something from vision.
> Pure vision will never be enough because it does not contain information about the physical feedback like pressure and touch, or the strength required to perform a task.
I'm not sure that's necessarily true for a lot of tasks.
A good way to measure this in your head is this:
"If you were given remote control of two robot arms, and just one camera to look through, how many different tasks do you think you could complete successfully?"
When you start thinking about it, you realize there are a lot of things you could do with just the arms and one camera, because you as a human have really good intuition about the world.
It therefore follows that robots should be able to learn with just RGB images too! Counterexamples would be things like grabbing an egg without crushing, perhaps. Though I suspect that could also be done with just vision.
Humans did not accumulate that intuition just using images. In the example you gave, you subconsciously augment the image information with a lifetime of interacting with the world using all the other senses.
counterpoint: think about all the tasks you could do with your hands and arms while your eyes are closed. i think its really a lot of stuff considering blind people can do the vast majority of things sighted people can do, and i suspect anything you could do with your eyes closed would be extremely difficult to do with a camera feed as the literal only sensory input
>"If you were given remote control of two robot arms, and just one camera to look through, how many different tasks do you think you could complete successfully?"
There are an infinite number of scenes that can be matched to one 2d picture. And what is a scene really? The last time I checked, RGB was not a good way of input in Computer Vision and rather relied on increasing levels of gradients via CNNs to build a compositional scene. None of that is paticularly translatable to how a LM works with text.
> because you as a human have really good intuition about the world.
This is the line that causes your logic to fail.
You introduced knowledge not obtained through observation. In fact, the knowledge you introduced is the whole chimichanga! It is an easy mistake to make, so don't feel embarrassed.
The claim is that one can learn a world model[0] through vision. The patent countered by saying "vision is not enough." Then you countered by saying "vision is enough if you already have a world model."
[0] I'll be more precise here. You can learn *A* world model, but it isn't the one we really care about and "a world" doesn't require being a self consistent world. We could say the same thing about "a physics", but let's be real, when we say "physics" we know which one is being discussed...
> Pure vision will never be enough because it does not contain information
Say it louder for those in the back!
But actually there's more to this that makes the problem even harder! Lack of sensors is just the beginning. There's well known results in physics that:
You cannot create causal models through observation alone.
This is a real pain point for these vision world models and most people I talk to (including a lot at the recent CVPR) just brush this off as "we're just care if it works." Guess what?! Everyone that is pointing this out also cares that it works! We need to stop these thought terminating cliches. We're fucking scientists.
Okay, so why isn't observation enough? It's because you can't differentiate alternative but valid hypotheses. You often have to intervene! We're all familiar with this part. You control variables and modify one or a limited set at a time. Experimental physics is no easy task, even for things that sound rather mundane. This is in fact why children and animals play (okay, I'm conjecturing here).
We need to mention chaos here, because it's the easiest way to understand this. There's many famous problems that fall into this category like the double pendulum, 3 Body Problem, or just fucking gas molecules moving around. Let's take the last one. Suppose you are observing some gas molecules moving inside a box. You measure their positions at t0 and at T. Can you predict their trajectories between those time points? Surprisingly, the answer is no. You can only do this statistically. There's probably paths but not deterministic (this same logic is what leads to multiverse theory btw). But now suppose I was watching the molecules too, but I was continuously recording between t0 and T. Can I predict the trajectories? Well, I don't need to, I just write it down.
Now I hear you, you're saying "Godelski, you observed!" But the problem with these set of problems is that if you don't observe the initial state you can't predict moving forwards and if you don't have very precise observation intervals you are hit with the same problem. I you turn around while I start a double pendulum you can have as much time as you want when you turn back around, you won't be able to model its trajectories.
But it gets worse still. There are confounding variables. There is coupling. Difficult to differentiate hypotheses via causal ordering. And so so much more. If you ever wonder why physicists do so much math it's because doing that is a fuck ton easier than doing the whole set of testing and then reverse engineering the equations from those observations. But in physics we care about counterfactual statements. In F=ma we can propose new masses and new accelerations and rederive the results. That's the what it is all about. Your brain does an amazing job at this too! You need counterfactual modeling to operate in real world environments. You have to be able to ask and answer "what happens if that kid runs into the street?"
I highly suggest people read The Relativity of Wrong [0]. Its a short essay by Isaac Asimov that can serve as a decent intro, though far from complete. I'm suggesting it because I don't want people to confuse "need counterfactual model" with "need the right answer." If you don't get into metaphysics, these results will be baffling.[1] It is also needed to answer any confusion you might have around the aforementioned distinction.
Tldr:
if you could do it from observation alone, physics would have been solved a thousand years ago
There's a lot of complexity and depth that is easy to miss with the excitement, but it still matters.
I'm just touching the surface here too, and we're just talking about mechanics. No quantum needed, just information loss
86 comments
[ 0.20 ms ] story [ 76.1 ms ] threadWho cares at this point? No one is stopping ML sets from being primarily pirated. The current power is effectively dismantling copyright for AI related work.
And since scraping of publicly available data is not illegal (in the US, according to the aforementioned "lawyer"), it seems like it's okay?
Not legal advice.
[0] https://www.skadden.com/insights/publications/2024/05/distri...
We made a sandwich but it cost you 10x more than it would a human and slower might slowly become faster and more efficient but by the time you get really good at it, its simply not transferable unless the model is genuinely able to make the leap across into other domains that humans naturally do.
I'm afraid this is where the barrier of general intelligence and human intelligence lies and with enough of these geospatial motor skill database, we might get something that mimics humans very well but still run into problems at the edge, and this last mile problem really is a hinderance to so many domains where we come close but never complete.
I wonder if this will change with some sort of computing changes as well as how we interface with digital systems (without mouse or keyboard), then this might be able to close that 'last mile gap'.
You can also seek investment without committing to an actual concrete business model.
For a single example, in any factory watch how humans are added as ad-hoc machines wherever a problem occurs. Machine N outputting faster than machine N+1 can accept? Have a human stack, and destack, the product between them. No matter the size, shape, it within reason the weight of the product. But most importantly: the process can begin within seconds of the problem occurring. No need for a programmer, developer, or maintenance worker to get involved. Just a clear order from the shift manager.
A general purpose robot with physical interfaces similar to a human would be very valuable for such environments. If it had the software to be as easy to instruct as a human.
Reality: Most value is in shrinking things, excluding humans, automating management, carefully designed process, and specialist hardware that does a subset of things very well. Relying on human(oid)s is a sure-fire way to suck.
https://www.youtube.com/watch?v=4xmckWVPRaI
Capitalia tantum.
This writing style is prominent on Twitter and niche Discords. It's funny how much I've come to be able to cut right through it, but if you haven't seen much of it it's really hard to parse. That's by design, too. The vibe of this writing style is to project an air of confidence so strong that the author doesn't care if you get it or not. It's a sort of humblebrag where the writing is supposed to flex the author's understanding of the subject while also not caring if you get it or not.
As others have already covered, there's also some heavy stretching of the truth and rewriting of history going on in this post. That's also common of the extreme bravado in this style of semi-impenetrable writing: The vagueness and ambiguities allow the author to make grandiose claims but then wiggle out of them later if someone is astute enough to catch on.
For example: The blog post is written as “We…” but is the author part of the team? Or is he using “we” meaning society in general?
Someone's getting peckish :P
https://www.videomimic.net/
https://www.videomimic.net/page1.html
Hello there! As a fellow gen-z douchebag, the article looks authentic, albeit a bit slim on Discord screencaps. Will be fun(?) to be proven wrong though.
if you havent - highly recommended.
> the core insight: predict in representation space, not pixels
We've been doing this since 2014? Not only that, others have been doing it at a similar scale. e.g. Nvidia's world foundation models (although those are generative).
> zero-shot generalization (aka the money shot)
This is easily beaten by flow-matching imitation learning models like what Pi has.
> accidentally solved robotics
They're doing 65% success on very simple tasks.
The research is good. This article however misses a lot of other work in the literature. I would recommend you don't read it as an authoritative source.
My first thought upon reading this was that an LLM had been instructed to add a pithy meme joke to each paragraph. They don't make sense in context, and while some terminally online people do speak in memes, those people aren't quoting doge in 2025.
There's also a sense of incoherence in the whole piece. For instance, this section:
"- after: 22 million videos + 1 million images (now we're talking)
they basically hoovered up everything: something-something v2, kinetics, howto100m, and a billion youtube videos"
Was it a billion vids or 22m? It turns out the latter sentence is just rephrasing the list of sources in a cool casual way, and the last one is called YT-Temporal-1B. That's a billion frames of video, not a billion videos.
The research is very real but the blog post appears to be very fake.
You may be surprised to find out how incorrect this.
I can think of two popular conservative sites likely to quote Doge people off hand that do this. I read all news in order not be an insufferable ideologue. So again, off the top of my head, NotTheBee (I think affiliated to BabylonBee (conservative The Onion)) and Twitchy. Among YouTubers, I think Asmond Gold, and I’m sure others like Steven Crowder who himself is in a famous meme.
That said… yea, you are probably right.
Could you explain what this means? Is this article quoting doge?
> very scientific. much engineering.
Emphasis on attempt because you're supposed to use words with grammatically incorrect modifiers, and the first one doesn't. (Even the second one doesn't seem entirely incorrect to me? I'm not a native speaker though.) "many scientific, so engineering" for example would have worked.
I assume they, or most likely their LLM, tried too hard to follow the most popular sequence (very, much, wow) and failed at it.
If you’re in the US, you likely work with them and they have learned to studiously avoid talking about politics except in vagaries to avoid conflict.
Cringely, they are. Nobody who isn't desperate to appear cool would write in that terminally grating register, including when using an LLM to do the writing.
For example, so that you don't crush a human when doing massage (but still need to press hard), or apply the right amount of force (and finesse?) to skin a fish fillet without cutting the skin itself.
Practically in the near term, it's hard to sample from failure examples with videos on Youtube, such as when food spills out of the pot accidentally. Studying simple tasks through the happy path makes it hard to get the robot to figure out how to do something until it succeeds, which can appear even in relatively simple jobs like shuffling garbage.
With that said, I suppose a robot can be made to practice in real life after learning something from vision.
I'm not sure that's necessarily true for a lot of tasks.
A good way to measure this in your head is this:
"If you were given remote control of two robot arms, and just one camera to look through, how many different tasks do you think you could complete successfully?"
When you start thinking about it, you realize there are a lot of things you could do with just the arms and one camera, because you as a human have really good intuition about the world.
It therefore follows that robots should be able to learn with just RGB images too! Counterexamples would be things like grabbing an egg without crushing, perhaps. Though I suspect that could also be done with just vision.
There are an infinite number of scenes that can be matched to one 2d picture. And what is a scene really? The last time I checked, RGB was not a good way of input in Computer Vision and rather relied on increasing levels of gradients via CNNs to build a compositional scene. None of that is paticularly translatable to how a LM works with text.
You introduced knowledge not obtained through observation. In fact, the knowledge you introduced is the whole chimichanga! It is an easy mistake to make, so don't feel embarrassed.
The claim is that one can learn a world model[0] through vision. The patent countered by saying "vision is not enough." Then you countered by saying "vision is enough if you already have a world model."
[0] I'll be more precise here. You can learn *A* world model, but it isn't the one we really care about and "a world" doesn't require being a self consistent world. We could say the same thing about "a physics", but let's be real, when we say "physics" we know which one is being discussed...
But actually there's more to this that makes the problem even harder! Lack of sensors is just the beginning. There's well known results in physics that:
This is a real pain point for these vision world models and most people I talk to (including a lot at the recent CVPR) just brush this off as "we're just care if it works." Guess what?! Everyone that is pointing this out also cares that it works! We need to stop these thought terminating cliches. We're fucking scientists.Okay, so why isn't observation enough? It's because you can't differentiate alternative but valid hypotheses. You often have to intervene! We're all familiar with this part. You control variables and modify one or a limited set at a time. Experimental physics is no easy task, even for things that sound rather mundane. This is in fact why children and animals play (okay, I'm conjecturing here).
We need to mention chaos here, because it's the easiest way to understand this. There's many famous problems that fall into this category like the double pendulum, 3 Body Problem, or just fucking gas molecules moving around. Let's take the last one. Suppose you are observing some gas molecules moving inside a box. You measure their positions at t0 and at T. Can you predict their trajectories between those time points? Surprisingly, the answer is no. You can only do this statistically. There's probably paths but not deterministic (this same logic is what leads to multiverse theory btw). But now suppose I was watching the molecules too, but I was continuously recording between t0 and T. Can I predict the trajectories? Well, I don't need to, I just write it down.
Now I hear you, you're saying "Godelski, you observed!" But the problem with these set of problems is that if you don't observe the initial state you can't predict moving forwards and if you don't have very precise observation intervals you are hit with the same problem. I you turn around while I start a double pendulum you can have as much time as you want when you turn back around, you won't be able to model its trajectories.
But it gets worse still. There are confounding variables. There is coupling. Difficult to differentiate hypotheses via causal ordering. And so so much more. If you ever wonder why physicists do so much math it's because doing that is a fuck ton easier than doing the whole set of testing and then reverse engineering the equations from those observations. But in physics we care about counterfactual statements. In F=ma we can propose new masses and new accelerations and rederive the results. That's the what it is all about. Your brain does an amazing job at this too! You need counterfactual modeling to operate in real world environments. You have to be able to ask and answer "what happens if that kid runs into the street?"
I highly suggest people read The Relativity of Wrong [0]. Its a short essay by Isaac Asimov that can serve as a decent intro, though far from complete. I'm suggesting it because I don't want people to confuse "need counterfactual model" with "need the right answer." If you don't get into metaphysics, these results will be baffling.[1] It is also needed to answer any confusion you might have around the aforementioned distinction.
Tldr:
There's a lot of complexity and depth that is easy to miss with the excitement, but it still matters.I'm just touching the surface here too, and we're just talking about mechanics. No quantum needed, just information loss
[0] m3kw9 ↗ Solving robotics is some claim. dfedbeef ↗ Spoiler: not solved amelius ↗ Betteridge not only applies to headlines with questions but it also works quite well with Twitter style headlines. moneywaters ↗ So video gen models basically can be extrapolated to control robotics ? How long until Veo3 robots take over?