A ban on desktop PC components over a certain power threshold (300W) or PSUs would definitely affect local AI, and Europe is not a big enough market, nor has it's own internal supply chains to offer alternatives.
This has been done in the EU/UK in the past (hoovers/vacuum cleaners) so the mechanism exists.
And it is focussed on social networks, which require an email address, which usually implies a device.
But instead of inserting controls around email addresses (as with paid services) or devices (as with contraband), the requirement is pushed to the application layer. It really makes no sense from a technical POV.
Can anyone explain how this ultrasound can see through the skull?
I've worked on ultrasound devices and data, the shadows from bone, and distortions caused by tissue types were very difficult. If this device can deal with those distortions it would already be useful for lung imaging.
Yes, thank you. That's exactly where I am, and trying to gather some knowledge.
The power draw from the wall is especially important, because a spike across multiple devices at the same time can cause issues which are really difficult to debug.
Open weight and local hosting is far, far cheaper. In every respect. Even support is cheaper, over time.
However, it's difficult to sell this to businesses who want contracts and KPIs, not staff and commitments.
Regulated industries will favour the closed sources, either by choice or mandate. The interesting question is whether they will have better models, or worse models. History says they will receive a worse service, but continue anyway.
I've just made a milestone on my project, moving away from AWS (budget) to self-hosted and the local models are so much faster than in the past. Beyond LLMs, having embeddings, image, video, audio gen available is crazy.
Running locally is the bar; it's hard to make these things a service which scales.
HTTP content negotiation was a good idea which decouples content from form, but only as far as format selection.
Generative models are able to transform content between media types, and it feels like the original intention can be completed -- the server generates the appropriate form at request time, rather than serving a pre-rendered one.
A concrete example: an Image2Depth model estimates the depth of a scene from a standard image, encodes that information in the response, and returns it to clients capable of rendering depth -- 3D displays, VR headsets, and so on. The content is the same; the form is specialised to the client capabilities.
I've been thinking of LLM prompting as execution, which makes context building a compile step -- different build systems, different output quality. Chunking is the simplest compiler; artefact construction that infers schema and resolves contradictions is a much richer one. The article maps the current spectrum.