301–310 of 347 posts
Notice we didn’t see the cat go behind the couch. Maladaptive cut.
They also don’t mention that the longer it runs over around 15 seconds the more hallucination until it’s a garbled mess.
Earlier quoted context omitted.
“ This is one of my fundamental beliefs about the nature of consciousness. We are never able to interact with the physical world directly, we first perceive it and then interpret those perceptions. More often than not, our interpretation ignores and modifies those perceptions, so we really are just living in a world created by our own mental chatter.” This is an orthodox position in modern philosophy, dating back to…
It’s the Allegory of the Cave, isn’t it?
The idea that there's an objective but imperceivable world (except by philosophers) is... a slippery slope to philosophical excess.
It's easy to spin whatever fancy you want when nobody can falsify it.
Everyone here seems too caught up in the idea that Genie is the product, and that its purpose is to be a video game, movie, or VR environment. That is not the goal. The purpose of world models like Genie is to be the "imagination" of next-generation AI and robotics systems: a way for them to simulate the outcomes of potential actions in order to inform decisions.
Agreed; everyone complained that LLMs have no world model, so here we go. Next logical step is to backfill the weights with encoded video from the real world at some reasonable frame rate to ground the imagination and then branch the inference on possible interventions (actions) in the near future of the simulation, throw the results into a goal evaluator and then send the winning action-predictions to motors. Gettin…
Everyone here seems too caught up in the idea that Genie is the product, and that its purpose is to be a video game, movie, or VR environment. That is not the goal. The purpose of world models like Genie is to be the "imagination" of next-generation AI and robotics systems: a way for them to simulate the outcomes of potential actions in order to inform decisions.
This is a video model, not a world model. Start learning on this, and cascading errors will inevitably creep into all downstream products. You cannot invent data.
Besides, we already know that agents can be trained with these world models successfully. See[1]:
> By learning behaviors in imagination, Dreamer 4 is the first agent to obtain diamonds in Minecraft purely from offline data, without environment interaction. Our work provides a scalable recipe for imagination training, marking a step towards intelligent agents
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> You really need to be obstinate in your convictions if you can dismiss LLMs at the time when everyone's job is being turned around by them. I'm factual. You are the one with the extraordinary claim that LLMs will find new substantial markets/go through transformative breakthrough. > Everywhere I look, everyone I talk to, is using LLMs And everywhere I look, I don't. It might be the case that you stand right in the…
> things that have nothing to do with LLMs/AI These are things that have to do with intelligence . Human or LLM doesn't matter. > things that you should NOT use LLMs for / parroting existing code / not in their training data/cut-off window, it's non-public information, they don't have the computing abilities to produce meaningful results Sorry, but I just get the picture that you have no clue of what you're talking a…
Being enthusiastic about a technology isn't incompatible with objective scrutiny. Throwing-up an ill-defined "intelligence" in the air certainly doesn't help with that.
Where I stand is where measured and fact-driven (aka. scientists) people do, operating with the knowledge (derived from practical evidence¹) that LLMs have no inherent ability to reason, while making a convincing illusion of it as long as the training data contains the answer.
> Sorry, but I just get the picture that you have no clue of what you're talking about- though most probably you're just in denial.
This isn't a rebuttal. So, what is it? An insult? Surely that won't help make your case stronger.
You call me clueless, but at least I don't have to live with the same cognitive dissonances as you, just to cite a few:
- "LLMs are intelligent, but when given a trivially impossible task, they happily make stuff up instead of using their `intelligence` to tell you it's impossible"
- "LLMs are intelligent because they can solve complex highly-specific tasks from their training data alone, but when provided with the algorithm extending their reach to generic answers, they are incapable of using their `intelligence` and the supplemented knowledge to generate new answers"
¹: https://arstechnica.com/ai/2025/06/new-apple-study-challenge...
Earlier quoted context omitted.
> things that have nothing to do with LLMs/AI These are things that have to do with intelligence . Human or LLM doesn't matter. > things that you should NOT use LLMs for / parroting existing code / not in their training data/cut-off window, it's non-public information, they don't have the computing abilities to produce meaningful results Sorry, but I just get the picture that you have no clue of what you're talking a…
> intelligence. Human or LLM doesn't matter. Being enthusiastic about a technology isn't incompatible with objective scrutiny. Throwing-up an ill-defined "intelligence" in the air certainly doesn't help with that. Where I stand is where measured and fact-driven (aka. scientists) people do, operating with the knowledge (derived from practical evidence¹) that LLMs have no inherent ability to reason, while making a conv…
I don't really think it's possible to convince you. Basically everyone I talk to is using LLMs for work, and in some cases- like mine- I know for a fact that they do produce enormous amounts of value- to the point that I would pay quite some money to keep using them if my company stopped paying for them.
Yes LLMs have well known limitations, but at they're still a brand new technology in its very early stages. ChatGPT appeared little more than three years ago, and in the meantime it went from barely useful autocomplete to writing autonomously whole features. There's already plenty of software that has been 100% coded by LLMs.
"Intelligence", "understanding", "reasoning".. nobody has clear definitions for these terms, but it's a fact that LLMs in many situations act as if they understood questions, problems and context, and provide excellent answers (better than the average human). The most obvious is when you ask an LLM to analyse some original artwork or poem (or some very recent online comic, why not?)- something that can't be in its training data- and they come up with perfectly relevant and insightful analyses and remarks. We don't have an algorithm for that, we don't even begin to understand how those questions can be answered in any "mechanical" sense, and yet it works. This is intelligence.
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The entertainment industry, as big as it is, just doesn't have as much profit potential as robots and AI agents that can replace human labor. Just look at how Nvidia has pivoted from gaming and rendering to AI. The other examples you've given are neat, but for players like Google they are mostly an afterthought.
Robotics: $88B TAM Gaming: $350B TAM All media and entertainment: $3T TAM Manufacturing: $5T TAM Roughly the same story. This tech is going to revolutionize "films" and gaming. The entire entertainment industry is going to transform around it. When people aren't buying physical things, they're distracting themselves with media. Humans spend more time and money on that than anything else. Machines or otherwise. AI imp…
They would try it once, think its cool and stop there. You would probably have a niche group of "world surfers" that would keep playing with it.
Most people do not have an idea on what they would want to play and how it would look like - they want a curated experience. As games adapted to the mass market, they became more and more curated experiences with lots of hand-holding the player.
Yeah, a holodeck would be popular, but that's a whole different technology ballpark and akin to talking about flying cars in this context.
This will have a giant impact on robotics and general models tho, as now they can simulate action/reaction inside a world in parallel, choosing the best course, by just having a picture of the world and probably a generated image of the end result or "validators" to check if task is accomplished.
And while robotics is $88B TAM nowadays, expect it to hit $888B in the next 5-10 years, with world simulators like this being one of the reasons.
From the team side, gotta be cool to build this, feels like one of those things all devs dream about.
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> Problem is, that's not what we've observed to happen as these models get better Eh? Context rot is extremely well known. The longer you let the context grow, the worse LLMs perform. Many coding agents will pre-emptively compact the context or force you to start a new session altogether because of this. For Genie to create a consistent world, it needs to maintain context of everything , forever . No matter how good…
The models, not the context. When it comes to weights, "quantity has a quality all its own" doesn't even begin to describe what happens. Once you hit a billion or so parameters, rocks suddenly start to think.
Earlier quoted context omitted.
> intelligence. Human or LLM doesn't matter. Being enthusiastic about a technology isn't incompatible with objective scrutiny. Throwing-up an ill-defined "intelligence" in the air certainly doesn't help with that. Where I stand is where measured and fact-driven (aka. scientists) people do, operating with the knowledge (derived from practical evidence¹) that LLMs have no inherent ability to reason, while making a conv…
> This isn't a rebuttal. I don't really think it's possible to convince you. Basically everyone I talk to is using LLMs for work, and in some cases- like mine- I know for a fact that they do produce enormous amounts of value- to the point that I would pay quite some money to keep using them if my company stopped paying for them. Yes LLMs have well known limitations, but at they're still a brand new technology in its…
And other people try it - really sincerely try it - and they don't "get it". It doesn't work for them. And those who "get it" tell those who don't that they just need to really try it, and keep trying it until they get it. And some people never get it, and are told that they didn't try enough (and also it gets implied that they are stupid if they really can't get it).
But I think that at least part of it is in how peoples' brains work. People think in different ways. Some languages just work for some people, and really don't work very well for other people. If a language doesn't work for you, it doesn't mean either that it's a bad language or that you're stupid (or just haven't tried). It can just be a bad fit. And that's fine. Find a language that fits you better.
Well, I wonder if that applies to LLMs, and especially to LLMs doing coding. It's a tool. It has capabilities, and it has limitations. If it works for you, it can really work for you. And if it doesn't, then it doesn't, and that doesn't mean that it's a bad tool, or that you are stupid, or that you haven't tried. It can just be a bad fit for how you think or for what you're trying to do.