[ben_w]

Once the model is trained, it doesn't need to keep the training data around.

GPT-3 is about 175 billion parameters (though I have no idea how many bits per parameter OpenAI uses at inference-time), and is apparently trained on 45 TB of data[0]

[0] Caution: citation was first hit on google, YMMV — https://www.springboard.com/blog/data-science/machine-learni...

[hadlock]

Presumably if you are training a robot to use a new/different tool you'll want the ability to train on site. If you buy an iHop restaurant the pancake robot in the kitchen ought to be able to be repurposed as a hamburger robot for your cheeseburger business. Omlette scrambling robots could be trained to mix small batches of cookie dough. Etc etc. Toyota is working on developing a framework for this already.

[ben_w]

Some robot firms are integrating LLMs to make the robots more general: https://www.youtube.com/watch?v=Vq_DcZ_xc_E&t=2s

On-site training is… not really solved yet. Not efficiently, at any rate: any task can be trained with sufficient compute and/or examples, but probably more than most companies would care to bother with, and certainly more than we'd get onto one of the chips in the article.

That's not to diss the chips: As I understand it, one of the biggest issues is the power envelope of mobile units, which means making the computations more energy efficient is going to help massively, it's just that "training" and "inference" are currently very distinct tasks with very different hardware requirements.

(Also, I'm not sure if you mean those examples as illustrations or are serious about them: if you're serious, I suspect an old-fashioned robot arm bolted to the ground and following a pre-programmed path will probably cover your needs — GOFAI is great in restricted domains, the more modern AI models are more appropriate when the environment is more chaotic and less predictable, such as collaborating in a kitchen that also has humans or being asked on the fly to do a new recipe it's never encountered before).