Yes, you're right. You can't say your function operates in Z "but has solutions in Q". That's what people are doing when they take a real function and go "ooh look, secret complex solutions!"
If you say "this function f operates on the integers", you can't turn around and then go "ooh but it has solutions in the rationals!" No it doesn't, it doesn't exist in that space.
Complex numbers are just a field over 2D vectors, no? When you find "complex solutions to an equation", you're not working with a real equation anymore, you're working in C. I hate when people talk about complex zeroes like they're a "secret solution", because you're literally not talking about the same equation anymore.
There's this lack of rigor where people casually move "between" R and C as if a complex number without an imaginary component suddenly becomes a real number, and it's all because of this terrible "a + bi" notation. It's more like (a, b). You can't ever discard that second component, it's always there.
Great writeup! Looks like this is going to be relevant very soon.
> Tools do not make a culture; the people engaging on it do
Absolutely, but it's also important to keep in mind that the tool has a big impact on culture by virtue of what behaviors it encourages and what limitations it has. "The medium is the message" is very true here, so think carefully about which tool you hop onto.
Falling sand games bring back a lot of nostalgia. If you're interested, you should check out dan-ball's version which is one of the progenitors of the genre. Over the years he's added a ton of features, elements, and interactions, to the point where it now has a fluid mechanics simulation for air!
> People need to let go of this strange and erroneous idea that humans somehow have this privileged access to the 'real world'.
This is irrelevant, the point is that you do have access to a world which LLMs don't, at all. They only get the text we produce after we interact with the world. It is working with "compressed data" at all times, and have absolutely no idea what we subconsciously internalized that we decided not to write down or why.
You can't ask ChatGPT a question like that, because it cannot introspect. What it says has absolutely no bearing on how it may actually respond, it just tells you what it "should" say. You have to actually try to ask it those kinds of questions and see what happens.
Finally I understand the use case for Genie 3. All the talk about "you can make any videogame or movie" seems to have been pure distraction from real uses like this: limited, time-boxed simulated footage.
Because blind-deaf people interact with the world directly. LLMs do not, cannot, and have never seen the actual world. A better analogy would be a blind-deaf person born in Plato's Cave, reading text all day. They have no idea why these things were written, or what they actually represent.
There is zero modeling of the world going on inside, for the very simple reason that it has never seen the world. It's only been given text, which means it has no idea why that text was written. This is the fundamental limitation of all LLMs: they are only trained on text that humans have written after processing the world. You can't "uncompress" the text to get back what the world state was to understand what led to it being written.
Until we have world models, that is exactly what they are. They literally only understand text, and what text is likely given previous text. They are very good at this, because we've given it a metric ton of training data. Everything is "what does a response to this look like?"
This limitation is exactly why "reasoning models" work so well: if the "thinking" step is not persisted to text, it does not exist, and the LLM cannot act on it.
And then the rivulets disappear or change completely because you looked away. The reason this is a dead end is because computationally, there is absolutely no way for the model to keep track of everything that it decided. Everything is kept "in its head" rather than persisted. So what you get is a dream world, useless for training world models. It's great for prototyping, terrible for anything more durable.
We're talking about context here though. The first couple seconds of Genie are great, but over time it degrades. It will always degrade, because it's hallucinating a world and needs to keep track of too many things.
> 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 it gets, there will always be a limit. This is not a problem if you use a game engine and code it up instead.
But it's not simulating, is it? It's hallucinating videos with an input channel to guide what the video looks like. Why do that instead of just picking Unreal, Unity, etc and having it actually simulated for a fraction of the effort?
They don't though, they're hallucinated videos. They're feeding models tons and tons of 2D videos and hoping they figure out physics from them, instead of just using a game engine and having the LLM write something up that works 100% of the time.
That makes even less sense, because an AI agent cannot learn effectively from a hallucinated world without internal consistency guarantees. It's an even stronger case for leveraging standard game engines instead.
I have no idea why Google is wasting their time with this. Trying to hallucinate an entire world is a dead-end. There will never be enough predictability in the output for it to be cohesive in any meaningful way, by design. Why are they not training models to help write games instead? You wouldn't have to worry about permanence and consistency at all, since they would be enforced by the code, like all games today.
Look at how much prompting it takes to vibe code a prototype. And they want us to think we'll be able to prompt a whole world?
But nobody go to college specifically training to do CRUD apps. The point is to give you broad training so that you can do CRUD apps and other stuff too. It is a very bad idea to give extremely specific training at scale, because then you get a workforce that has difficulty adapting to changes. It's like trying to manage a planned economy: there is no point in trying to predict exactly what jobs you will get, so let's make sure you can handle whatever's thrown at you.
Both can be true. You're tapping into every line of code publicly available, and your day-to-day really isn't that unique. They're really good at this kind of work.
You didn't write sorting code or assembly code because you were going to need to write it on the job. It gave you a grounding for how datastructures and computers work on a fundamental level. That intuition is what makes picking up minecraft hack mods much easier.
That's a no-go for advertisers. They need to control their own brand and their image, there're not going to leave it up to chance like "ok so uh just mention us or something and then we'll pay you." They pay for very specific placements, and they need to provide the content.
I don't get that part either. That's like saying "the brightest minds have all come together to... build us another thing that requires money." OpenAI is trying to make more money than they use to run ChatGPT. They are getting that money from their users, and soon advertisers too. They still need their users to like ChatGPT
I do not understand why this is a big deal. There is no world in which ads are embedded in LLM answers, because you'd need another LLM to determine whether the "placement" was correct and included all the information that the advertiser wanted (and it still won't work 100%). They are putting ads on the side, like they've always done, leveraging all the tech that already exists to do this. This is pretty much a no-brainer for OpenAI and any AI company.
Literally the first thing you will learn in journalism school is that there is no such thing as "objective neutrality". Even deciding what story to cover includes bias.