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slickytail

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yael [at] yaelwalker.com slickytail on github

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The idea of Simulacrum comes from Baudrillard. His essay "Simulation and Simulacra" is highly recommended for understanding what is so strange about the modern economy.

I think it would, for all practical purposes, be impossible to determine an optimal warrior, even at very small core sizes. Not only is the search space huge but the evaluation function can take unbounded time to resolve. We should consider the halting problem embedded inside the optimization target as a clue to the problem's difficulty.

The site has a little game where you click to dismiss as many cookie-consent popups as possible, in 30 seconds. I suppose if you can't see the cookie consent popups, then at the end of the timer you just have zero points.

In a discrete Fourier transform, the number of frequencies you get out is the number of datapoints you have as input. This is because any frequencies higher than that are impossible to know (ie, they are above the sampling frequency).

But in the continuous Fourier transform, the output you get is a continuous function that's defined on the entire real line.

The transformer was a major breakthrough in NLP, and it was clear at the time of publishing that it would have a major impact. But I will add that it is common in the Deep Learning field to give papers catchy titles (see, off the top of my head: all the YOLO papers, ViT, DiT, textual inversion). The transformer paper is one in a long line of seminal papers with funny names.

All of this only really applies to LLMs though. LLMs are memory bound (due to higher param counts, KV caching, and causal attention) whereas diffusion models are compute bound (because of full self attention that can't be cached). So even if the memory bandwidth of an M3 ultra is close to an Nvidia card, the generation will be much faster on a dedicated GPU.

Gemini Diffusion 1 year ago

The relative unimportance of the exact SDPA attention in use in modern transformers is already known: https://arxiv.org/abs/2111.11418

The FFN, normalization, and residual connections are absolutely irreplaceable -- but attention can be replaced with almost any other layer that shares information between tokens, such as pooling, convolution, random mixing, etc.