This is the important part. It's not guaranteed to be accurate. They claim it "delivers essentially the same correctness as the model it imitates -- sometimes even finer detail". But can you really trust that? Especially if each frame of the simulation derives from the previous one, then errors could compound.
It seems like a fantastic tool for quickly exploring hypotheses. But seems like once you find the result you want to publish, you'll still need the supercomputer to verify?
I have a thing where I immediately doubt any ML paper that imitates a process then claims that the model is sometimes “even better” than the original process. This almost always means that there is an overzealous experimenter or a PI who didn’t know what they were dealing with.
That 'finer detail' sounds suspiciously like inventing significant digits from less significant inputs. You can interpolate, for sure, but it isn't going to add any information.
Especially if each frame of the simulation derives from the previous one.
How do you think this universe works, to me that sounds exactly the same.
Every moment is derived from the previous instant.
Leaving aside the question of whether the universe is discrete or continuous, a simulation would still have lower "resolution" than the real world, and some information can be lost with each time step. To compensate for this, it can be helpful to have simulation step t+1 depend on both the step t and step t-1 states, even if this dependency seems "unphysical."
The universe evolves exactly under physical laws, but simulations only approximate those laws with limited data and finite precision. Each new frame builds on the last step’s slightly imperfect numbers, so errors can compound. Imagine trying to predict wind speeds with thermometers in the ocean — you can’t possibly measure every atom of water, so your starting picture is incomplete. As you advance the model forward in time, those small gaps and inaccuracies grow. That’s why “finer detail” from a coarse model usually isn’t new information, just interpolation or amplified noise.
Google has also a global weather model yielding by ten day predictions, and open street map runs local as well. Just today with GraphHopper and a map of Europe I can generate 2700 routes per second on my workstation. When I was young these were not things you could run at home!
Add to that Qwen3-Omni which can run on a well spec'd workstation, and will happily carry on natural voice spoken conversations with you, and can work intelligently with images and video as well as all the other stuff LLMs already do.
I don't think Paramount would look kindly on giving it Majel Barret's voice, but it sure feels like talking to the computer on the holodeck.
> The breakthrough combines neural networks...
This is the important part. It's not guaranteed to be accurate. They claim it "delivers essentially the same correctness as the model it imitates -- sometimes even finer detail". But can you really trust that? Especially if each frame of the simulation derives from the previous one, then errors could compound.
It seems like a fantastic tool for quickly exploring hypotheses. But seems like once you find the result you want to publish, you'll still need the supercomputer to verify?
I have a thing where I immediately doubt any ML paper that imitates a process then claims that the model is sometimes “even better” than the original process. This almost always means that there is an overzealous experimenter or a PI who didn’t know what they were dealing with.
That 'finer detail' sounds suspiciously like inventing significant digits from less significant inputs. You can interpolate, for sure, but it isn't going to add any information.
Especially if each frame of the simulation derives from the previous one. How do you think this universe works, to me that sounds exactly the same. Every moment is derived from the previous instant.
Leaving aside the question of whether the universe is discrete or continuous, a simulation would still have lower "resolution" than the real world, and some information can be lost with each time step. To compensate for this, it can be helpful to have simulation step t+1 depend on both the step t and step t-1 states, even if this dependency seems "unphysical."
The universe evolves exactly under physical laws, but simulations only approximate those laws with limited data and finite precision. Each new frame builds on the last step’s slightly imperfect numbers, so errors can compound. Imagine trying to predict wind speeds with thermometers in the ocean — you can’t possibly measure every atom of water, so your starting picture is incomplete. As you advance the model forward in time, those small gaps and inaccuracies grow. That’s why “finer detail” from a coarse model usually isn’t new information, just interpolation or amplified noise.
There are no frames in the real world, it literally does not work like that.
There are frames in simulations though! Typically measured as time steps. That the frame usually has N_d dimensions is insignificant.
There are frames in every digital signal, the world is not a digital as far as I know.
The emulator: https://github.com/CosmologicalEmulators/Effort.jl
Google has also a global weather model yielding by ten day predictions, and open street map runs local as well. Just today with GraphHopper and a map of Europe I can generate 2700 routes per second on my workstation. When I was young these were not things you could run at home!
Add to that Qwen3-Omni which can run on a well spec'd workstation, and will happily carry on natural voice spoken conversations with you, and can work intelligently with images and video as well as all the other stuff LLMs already do.
I don't think Paramount would look kindly on giving it Majel Barret's voice, but it sure feels like talking to the computer on the holodeck.
Moore's law surprises people way more than it should by now. This is an awesome project!
This has nothing to do with Moore's law. It's about neural networks.
Can we say that algorithmic speed-ups are sufficiently rate that they are usually impressive?
This isn't any algorithmic speedup.
It’s a new substrate.