Eventually, I hope we'll see many more specialised small models that run nicely on consumer and prosumer hardware. Like LLMs specific to income tax law for UK residents, or LLMs specific to building power delivery circuits for PD USB C devices. There probably is a point where the niche-ness does become to niche to justify the post training, but again, small models means much less training required.

Kind of begs the question if this even needs new/better data? Could you take the foundation model that's trained on everything, and then run a final fine tuning training run on just the set of foundation training data that you want the model to specialise in? Eg, extract just the training data that is about UK income tax law and then run a few epochos on just that? Any ML people know if this is a thing or if not, why it doesnt work?

I can't get a read only API key for my YouTube, Gmail, Outlook etc etc accounts. Only full admin access via a browser.

I can get r/o creds for things like GitLab and AWS and K8s, which means I'm very happy to let my agents run wild at work because I know for certain they actually can't do any damage, they can read all they want but can only go as far as opening and MR that I can review and merge.

Even for domestic robots people don't care, see just about every cloud connected smart vacuum robot and the numerous data leaks

And for companies, most of them are fine using SaaS and cloud based hosting platforms, so why would it be any different for robotics?

Sure there is the latency argument, so at least something doing very short term decision making needs to run on device or at the edge, but for most task the end to end time is in the single didit seconds to minuites, so the extra 100ms or whatever that you get going out to some model in the cloud isn't a deal breaker.

Don't know if that's ever possible to know though unless you train a model from scratch but remove all bike product photography and adjacent materials from the training data?

Put it in front of a common-ish Python or NPM package and see what it finds, likely going to be a lot more.

Sure it's kinda cool as a gimmick, but it should be like 600-800ms at most if you do want one

They won't ever squash or stretch video though, so this means the original uploader stretched the 4:3 content to 16:9 at some point before upload