I think what they meant by that is “is this different wrt antitrust compared to Intel and x86?”
Intel both owns the x86 ISA and designs processors for it, though the situation is more favorable in that AMD owns x86-64 and obviously also designs their own processors.
I would say that in comparison to the standards used for top ML conferences, the paper is relatively light on the details. But nonetheless some folks have been able to reimplement portions of their techniques.
ML in general has a reproducibility crisis. Lots of papers are extremely hard to reproduce, even if they’re open source, since the optimization process is partly random (ordering of batches, augmentations, nondeterminism in GPUs etc.), and unfortunately even with seeding, the randomness is not guaranteed to be consistent across platforms.
No, the server is on the github account linked above as well. The repo is here.
Signal however doesn’t federate and does not generally support third-party clients.
I used to do something like this before Signal became a thing. We used to use OTR via the Pidgin OTR plugin to send encrypted messages over Google Hangouts. Funnily enough, I’m pretty sure Pidgin supports Discord, so you could use the exact same setup to achieve what you described.
It was pretty funny to check the official Hangouts web client and see nonsensical text being sent.
This was also one of my concerns with the hype surrounding low cost SLS printers like Micronics, especially if they weren’t super well designed. The powder is incredibly dangerous to inhale so I wouldn’t want a home hobbyist buying that type of machine without realizing how harmful it could be. My understanding is even commercial SLS machines like HP’s MJF and FormLab’s Fuse need substantial ventilation (HEPA filters, full room ventilation, etc.) in order to be operated safely.
Metal is of course even worse. It has all the same respiratory hazards (the fine particles will likely cause all sorts of long-term lung damage) but it also presents a massive fire and explosion risk.
I can’t see these technologies making it into the home hobbyist sphere anytime soon as a result, unfortunately.
I’m fairly certain blockchain GPUs have very different requirements than those used for ML, especially not LLMs. In particular they don’t need anywhere as much VRAM and generally don’t require floating point math, nor do they need features like tensor cores. Those “blockchain GPUs” likely didn’t turn into ML GPUs.
ML has been around for a long time. People have been using GPUs in ML since AlexNet in 2012, not just after blockchain hype started to die down.