[hosh]

Adding an embedded LLM as a human interface for every appliance is a huge win— for consumers at least.

For example, I have a dishwasher with a bunch of settings, can sense load, etc. It’s got a touch interface that works with wet hands. Or I can tell it to start with the usual settings, or that a particular load is a bit different. Same with the laundry, the pressure cooker.

It is less mind bandwidth when you got kids.

What I don’t want, is for my appliances to do is to phone home to the makers.

LLMs (if you don’t somehow trigger its insanity) can be far more capable than Siri. How do you get that into something more energy efficient than a high end gaming rig?

Something more hidden is using LLMs to reprogram machine-to-machine protocols. That might extend the lifetime of machines that have to talk with other machines, but it breaks planned obsolescence.

There are plenty of exciting product ideas. Whether they are exciting revenue generators are another thing entirely.

[marcosdumay]

> Adding an embedded LLM as a human interface for every appliance is a huge win— for consumers at least.

So, appliances get even harder to understand settings, that are actually illogical, instead of just having hidden logic? That's not a clear win.

[bckr]

No, there’s an underlying logic and underlying settings that are still accessible.

But transparently wrapped around that there’s a “good Clippy” who can teach, interpret, and orchestrate those settings with a CUI (conversational UI, pronounced “koo-ee”).

[marcosdumay]

Oh, a settings assistant is much easier to get right.

It is just completely against the modernly accepted "best practices" for devices and interface development. So I don't see how we can get it. But yeah, it could be good.

[calibas]

Adding hardware capable of running an LLM would significantly increase the price of appliances, not sure that's a win for consumers.

In the context of the article, an LLM is kind of the opposite of "TinyML" and not something most IoT devices could even handle.

[hosh]

Not if you can condense the LLM into being able to run on the embedded hardware.

Article aside, reducing energy use for models is one of the research areas for TinyML.

[calibas]

I'm skeptical that an LLM with billions of parameters can be compressed down into something that runs on embedded hardware and still remain useful.

[bckr]

Skeptical you should be, but I’m optimistic. We have papers showing that knowledge in these models can be edited and deleted. Sam Altman makes the point that too much compute is being spent on using the LLM as a database.

Thinking about how few things any of these CUIs need to know about, I’m optimistic that we can distill them down to a workable size while maintaining the LLM magic.

“Fridge, what is the meaning of life?”

‘Sorry, I don’t know about that. Ask me something about what’s in your fridge.’

“Okay how many eggs do I have.”

“I see 3 eggs.”

When I can have that conversation by proxy through my phone’s onboard CUI while at the store, I’m going to get a lot of value out of that.

[JohnFen]

> It is less mind bandwidth when you got kids.

If it works like ChatGPT does, then I would find it a greater mental burden. You'd have to carefully craft what you're telling it, or engage in a conversation of some sort, instead of just hitting a couple of buttons or turning a dial.