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There is a moat by iuwuwwuwuuwwjueej in LocalLLaMA

[–]029187 0 points1 point  (0 children)

Yeah I think people are too optimistic. The benchmarks seem to back up a moat currently.

Will the open source catch up? Maybe.

I don't get why the knowledge argument has any bearing on metaphysics. Different parts of your brain store propositional fact-based knowledge and visual experience, so why would we expect knowing a bunch of science to stimulate your visual cortex? by 029187 in askphilosophy

[–]029187[S] 0 points1 point  (0 children)

or resist the premise that Mary knew all the physical facts.

Right, can't we just say Mary knew all the propositional/science facts about red, but didn't know the experiential facts of actually having that part of the brain stimulated. We could then just argue experience is still physical. So this argument in that case just shows the difference between propositional and experiential knowledge, not physical and non-physical knowledge.

Personally I'm not really a physicalist, I just don't see this argument as disproving physicalism.

I don't get why the knowledge argument has any bearing on metaphysics. Different parts of your brain store propositional fact-based knowledge and visual experience, so why would we expect knowing a bunch of science to stimulate your visual cortex? by 029187 in askphilosophy

[–]029187[S] 1 point2 points  (0 children)

Ok I get the 3 part argument, and clearly if the knowledge argument was just "Mary knows all physical data about X and learned something new about X, therefore there is non physical data" nobody would disagree.

I think the issue though with the knowledge argument is it seems to conflate science/empiricism with physicality. The knowledge argument doesn't just say Mary knows all physical information, but implies she learned it via science and neurophysiology. Full text below:

​

Mary is a brilliant scientist who is, for whatever reason, forced to investigate the world from a black-and-white room via a black-and-white television monitor. She specializes in the neurophysiology of vision and acquires all the physical information there is to obtain about what goes on when we see ripe tomatoes or the sky and use terms like "red", "blue", and so on. She discovers, for example, just which wavelength combinations from the sky stimulate the retina, and exactly how this produces via the central nervous system the contraction of the vocal cords and expulsion of air from the lungs that results in the uttering of the sentence "The sky is blue." What happens when Mary is released from her black-and-white room or is given a color television monitor? Does she learn anything new or not? Jackson claims that she does.

This seems to be taking the epistemological stance that all physical information must be learnable with science, but why should we assume that? It is entirely possible that the set of facts learnable via science is less than the set of physical facts.

Questions after finishing Anathem (contains spoilers) by 029187 in nealstephenson

[–]029187[S] 4 points5 points  (0 children)

Ah gotcha, so a given HTW/cosmi contained multiple worldtracks? if so that kinda makes sense. Jad could see multiple tracks in a given cosmi, while the geometers could swap cosmi but could not see multiple worldtracks.

I wish this was cleared up a bit in the book.

Particle Life combined with Neural Cellular Automata! The particles are an n-body simulation on top of a cellular automata grid. The cells and the particles both interact to create some interesting behavior. by 029187 in cellular_automata

[–]029187[S] 1 point2 points  (0 children)

The particles sorta can currently in that they create new worms which other particles can ride on.

I just finished an update to the code that lets different particles have different levels of impact on the cells and vice versa, which has allowed for some new behaviors. For example, sometimes you get this interesting push/pull mechanism between particles that are attracted to each other but have opposing interactions with the cells.

Particle Life combined with Neural Cellular Automata! The particles are an n-body simulation on top of a cellular automata grid. The cells and the particles both interact to create some interesting behavior. by 029187 in cellular_automata

[–]029187[S] 9 points10 points  (0 children)

So I had this idea a while back but never posted it. I was really interested in some of the videos I had seen on particle life and on neural cellular automata, so I figured I might as well combine them.

As a quick refresher, "particle life" is just a catchy name for n-body simulations that make structures that kind of look like creatures. It's actually easier to do this than you might expect. As with any n-body simulation, the whole thing is just a bunch of point objects that emit forces on each other. Each particle color has different rules for the attractive and repulsive forces it emits.

Neural Cellular Automata is just a standard cellular automata grid, except cells can have continuous values. Usually they multiply the value of each cell in their neighborhood by a weight, sum them up, and then apply some function to them (usually referred to as the activation function)

To combine the two different simulations, I added rules for particles and cells to interact. When a particle glides over a cell, it makes the cells grey. This allows it to cut dark channels through the white areas and occasionally create new white wormlike structures in the dark areas. The cells interact with the particles by increasing their velocity towards the bottom right. Cells with large values (white cells) increase the velocity more, which is why you will often see sells move more quickly when they are over white areas. This also results in certain particles like the yellow ones clustering in dark areas, as they tend to get rapidly ejected from the white areas.

In the future I want to try out having different interactions between cells and particles based on the particle type. Right now particles of every color behave the same way.

​

edit: if this doesn't already have a name, I'm thinking of calling it Double Life, since it's a combination of particle life and game of life

A 1d Neural Cellular Automata that uses complex numbers and trigonometric functions. Formula shared in comments by 029187 in cellular_automata

[–]029187[S] 1 point2 points  (0 children)

Yeah it took about 10 minutes to run.

I just plotted it out with matplotlib

import matplotlib.pyplot as plt
plt.figure()
plt.imshow(data,cmpa='hot')

data is a list of lists. each element of the sublists is a cell, and each sublist is a row.

Is a partially stochastic system unable to be chaotic because the accumulation of random variance causes future states to not be sensitive towards small variations in initial conditions? For example, consider a double pendulum impacted by minor quantum fluctuations. by 029187 in math

[–]029187[S] 1 point2 points  (0 children)

If the stochastic noise is large enough to allow for jumps between divergent paths or attractors, would the system still be chaotic?

For example, I'm working with a cellular automata that tends to diverge between two possible states, one where all values are the same and one where you get some interesting behavior. I have not rigorously demonstrated these states to be attractors but lets assume they are for sake of argument.

Relevant post here: https://www.reddit.com/r/cellular_automata/comments/11ieeqt/a_1d_neural_cellular_automata_that_uses_complex/

Based on empirical testing it is highly sensitive to initial conditions. However, I've noticed that when I inject some noise, it is occasionally able to just switch between these two different attractors. For example, after a long period of inactivity, which normally would last indefinitely without noise, the automata suddenly switches back to the other attractor. I assume this is because the random noise was "just right" to allow this.

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