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stormfather

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Just go to Perplexity or something like that, its well trodden ground. And essentially, what you're doing is discretizing the relevant differential equations and getting that to run in a 2D or 3D cellular automaton.

I'll give you a simple example. For diffusion of heat between 2 points, the rate of change (first derivative) is proportional to the difference in temp between them. So you make an update rule for points on a grid that says "calc the average difference of a cell's temp with that of its neighbors, multiply by some constant, and that is the amount to update this cell at this time. Run that for every cell in parallel, many times." Then you tack on a visualization and you can watch the heat diffuse. A fun example would be the cooling of the proto-Earth. You can watch the crust form.

Heat diffusion is a good starter problem. So is gravitational interaction.

The entire model of sea-borne power projection has changed. What does it mean when hypersonic missiles can be fitted with optical sensors to autonomously target ships? It means carriers now are about as useful as battleships in WW2. Nobody knows who can dominate which parts of the world ocean anymore.

I find for auto turn detection, models work better if you put in the system prompt "if it seems the user hasnt completed their thought yet, output silence". This hack works around their compulsive need to output something.

This makes me think the first to make a really successful model will be the ones to pony up to get human feedback at scale from the smartest humans instead of what they do now.

I think his point is that things very rarely experience purely compressive forces. Just being compressed induces tension in other directions, like water being squished out between your clapping hands. So even though water has great compressive strength, in practice this isn't very useful.

Its a time capsule, among other things. I want to take many, many videos of my grandpa's farm, and be able to walk around in it in VR using something like this in the future.

What you're saying just isn't true.

I can get an AI to generate an image of a bear wearing a sombrero. There are no images of this in its training data, but there are bears, and there are images of sombreros, and other things wearing sombreros. It can combine the distributions in a plausible way.

If I am trying to train a small model to fit into the optical sensor of a warhead to target bears wearing sombreros, this synthetic training set would be very useful.

Same thing with artillery in bushes. Or artillery in different lighting conditions. This stuff is useful to saturate the input space with synthetic examples.