Idk if you've seen this already but Taalas does this interesting thing where they embed the model directly onto the chip, this leads to super-fast speeds (https://chatjimmy.ai) but the model they're using is an old small Llama model so the quality is pretty bad. But they say that it can scale, so if that's really true that'd be pretty insane and unlock the inference you're talking about.

Taalas showed that you could make LLMs faster by turning them into ASICs and get 10k+ token generation. It's a matter of time now.

Is emulating human behavior really a valuable end goal though? Humans exist as the evolutionary endpoint of exhaustion hunting large pray and organic tool-making. We've built loads of industrial and residential automation tools in the last 100 years and none of them are humanoid. I'd imagine a household robot butler would be more like R2D2 with lots and lots of arms.

What if we put slop images into slop machines and got slop^2 back out

Yeah I'm not sure how that's currently working out. https://proofofcorn.com/

In my quick image recognition testing on AI studio, it's performance seems similar to 3.1 pro, but is much much faster. It "thinks" but only for a few seconds.

Of course this is for counting animal legs while giving coordinates and reading analog clocks. Not coding or or solving puzzles. I imagine the image performance to model weight of this model is very high.

I'm pretty sure claude will one shot this for you, including making you a home assistant dashboard item if you ask it.

frigate can be setup to do this I believe, but its overkill. Openclaw could do it, slightly less overkill.

Look into opencv.

I have broken dishes loading and unloading the dishwasher. Am I a massive failure?

My non-AI dishwasher can't even always keep the water inside. Nothing is perfect.

I ended up watching Bicentennial Man (1999) with Robin Williams over the weekend. If you haven't seen I thought it was a good and timely thing to watch and is kid friendly. Without giving away the plot, the scene where it was unloading the dishwasher...take my money!

> If a robot unloading your dishwasher breaks one of your dishes once, this is a massive failure.

That's a bit exaggerated, no? Early roombas would get tangled in socks, drag pet poop all over the floor, break glass stuff and so on, and yet the market accepted that, evolved, and now we have plenty of cleaning robots from various companies, including cheap spying ones from china.

I actually think that there's a lot of value in being the first to deploy bots into homes, even if they aren't perfect. The amount of data you'd collect is invaluable, and by the looks of it, can't be synth generated in a lab.

I think the "safer" option is still the "bring them to factories first, offices next and homes last", but anyway I'm sure someone will jump straight to home deployments.

It's called "VLA" (vision-language-action) models: https://huggingface.co/models?pipeline_tag=robotics

VLA models essentially take a webcam screenshot + some text (think "put the red block in the right box") and output motor control instructions to achieve that.

Note: "Gemini Robotics-ER" is not a VLA, though Gemini does have a VLA model too: "Gemini Robotics".

A demo: https://www.youtube.com/watch?v=DeBLc2D6bvg

I think that where this gets interesting is when you can just drop these robotic systems into an environment that wasn't necessarily set up specifically to handle them. The $50 for your gauge isn't really the cost: it's engineering time to go through the whole environment and set it up so that the robotic system can deal with each of the specific tasks, each of which will require some bespoke setup.

Agree. I'm unclear what's the highlight of this post. Is the multimodality of the model (that can replace computer vision), is it the reasoning part, is it the overall wrapper that makes it very easy to develop on top?