I'm pretty bullish on fediverse-like concepts in the long term even if US social becomes a dystopian hellscape.

In the modern world, chips are how you fight wars.

What are some common examples of complex numbers in these sorts of applications?

Here complex numbers are used for an eloquent gradient calculation - you can express all sorts of operations through just the 3 functions: `exp`, `log` and `add` defined over complex plane. Simplifies the code!

The added benefit is that all the variables become complex. As long as your loss is real-valued you should be able to backprop through your net and update the parameters.

PyTorch docs mention that complex variables may be used "in audio and other fields": https://pytorch.org/docs/stable/notes/autograd.html#how-is-w...

Amazon, one of the founders of Matter, is the first to add support for Matter casting, a feature of the Matter smart home standard that allows you to control TVs and streaming devices straight from a connected app. At CES this week, Amazon announced that Matter casting support is coming to its Prime Video app so that you can cast content to Amazon hardware devices.

Any effort to change this has to be centered around open, crowd-sourced datasets.

So we have an admission that folks were putting other people's names on the lists?

If anybody's interested in learning more about Lean, he's been posting his experiences with the project over at @tao@mathstodon.xyz

> Is there some sense in which this isn't obvious to the point of triviality?

This is maybe a pedantic "yes", but is also extremely relevant to the outstanding performance we see in tasks like programming. The issue is primarily the size of the correct output space (that is, the output space we are trying to model) and how that relates to the number of parameters. Basically, there is a fixed upper bound on the amount of complexity that can be encoded by a given number of parameters (obvious in principle, but we're starting to get some theory about how this works). Simple systems or rather systems with simple rules may be below that upper bound, and correctness is achievable. For more complex systems (relative to parameters) it will still learn an approximation, but error is guaranteed.

I am speculating now, but I seriously suspect the size of the space of not only one or more human language but also every fact that we would want to encode into one of these models is far too big a space for correctness to ever be possible without RAG. At least without some massive pooling of compute, which long term may not be out of the question but likely never intended for individual use.

If you're interested, I highly recommend checking out some of the recent work around monosemanticity for what fleshing out the relationship between model-size and complexity looks like in the near term.

Folks should be incredibly skeptical of videos like this. The major players working to capitalize on this technology have demonstrated time and again a complete willingness to lie to us about the capabilities of their models. It's become almost a requirement to appear competitive.

Crowdsourced datasets like this and the ones produced by the OpenAssistant project could easily become the ONLY way to build foundational models if the courts decide that what OpenAI and co are doing is not Fair-Use. I don't think I would call this scenario unlikely, either.