spadufed

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?

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 @[email protected]

> 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.

I mean, asking them to plop their cli docker setup into a yaml file isn't exactly a difficult task. I think it's probably more correct to consider docker-compose an integral part of docker rather than a superfluous abstraction layer.

They absolutely do not. This is more of a state of the ecosystem sort of problem. I think a lot of developers were wary of getting into how some of the more esoteric AP objects should work. It's important to note too that while we have a standard in the form of AP a lot of the decisions about norms for these less common message types simply have not been made, but we are definitely moving in that direction as more services add AP integration.