Depends on how you use them, I think. Magic boxes that are tied into the cloud and just give you an app to control your lights, dumb. Setting up a system that automates your home electrical appliances in a thoughtful manner can actually deliver savings and maybe even improve your life. Even as someone getting into smart home stuff, though, mainly what I’m seeing is the ability to save power by having some kind of intelligent control over your devices. Everything else is flashing lights and surveillance.

Problem is, your average user isn’t going to be setting up a LAN-only, zigbee-to-homeassistant automation. They need the magic box type of interface. So as a mass market thing it seems kind of doomed.

Yeah, that’s fair, there are driven people, and people who are pushing for something, right, but in this case, look at the language used:

Progress will not happen until there is new maintainer. XZ for C has sparse commit log too. Dennis you are better off waiting until new maintainer happens or fork yourself. Submitting patches here has no purpose these days. The current maintainer lost interest or doesn’t care to maintain anymore. It is sad to see for a repo like this. [src]

Tons of emotional button-pushing and pressure, but not on technical grounds. Just trying to make the dev feel crappy about themselves.

I’ve always taken this attitude towards pushy people and tbh this is more or less why. Being pushy like this is inherently suspicious as fuck.

You’re basically completely wrong about how AI is going to scale. We’re not going to be stuck on, say tinkertoy models on our phones and gigantic mega-models exclusively in the cloud. That’s insane. We have good language models that will run on an ordinary laptop already. You can scale models to more or less any size and there is amazing research coming out constantly regarding how to do AI more efficiently. People are running tons of ML code on their PC’s already and the demand will only go up as companies – like Microsoft – bundle more and more features that rely on AI code into their software, more SDK’s and libraries come out that support it, etc.

Also, the feasibility of deploying AI code to more users goes up the more users have them in their devices.

Also, the main trick of NPU’s is efficient matrix math, especially the use-case of applying a single operation to entire matrices at once, which AIUI is foundational to tensor math. Plain old CPU’s are trash at this because they have to iterate over each individual entity in the matrix and apply the operation separately. NPU’s, as I guess they’re coming to be called, are designed to do those operations massively in parallel. There are likely tons of applications for this beyond just ML code that we haven’t even imagined yet.

It’s a bit like asking in 1995 what the use case for a graphics card is when you can go to an arcade and gameboys exist. At that exact moment in time, based on the exact cards that were available in literally 1995, it might have been hard to imagine that by 2024 we’d all have dedicated graphics chips of some kind in our computers – in fact, we’d be hard-pressed to imagine devices without them – and that some of the biggest computing companies in the world would be graphics card manufacturers. Yet here we are.

You have to pay attention to the research as it develops, and you have to realize that they don’t just show up to markets to satisfy pre-existing demands, they create markets and create new demand where none existed before. That’s how the tech industry works.

Who’s going to be buying and using Bitcoin if it’s not affordable?