Artificial intelligence has proved to be even more valuable as a writer of computer code than as a writer of words.

I call bullshit.

the LLM’s dataset uses only public domain and openly licensed material.

I’m curious about the specifics of all this. Probably the most well-known “openly licensed” sort of licenses (aside from licenses specifically intended only for software) are the Creative Commons family of licenses, all of which require attribution. So then the question would become “if you’ve used any of my CC-licensed content in training this model, am I attributed somewhere?” If so, surely the list is extremely long. Or maybe Creative Commons wasn’t “openly”-enough licensed and they excluded all CC-licensed content from the training set.

Also, the public domain is definitely strongly biased toward very old content. You’d think a lot of the answers you got from that LLM would be based on some very outdated information. Maybe they specifically limited it to (or at least adjusted weights or something to make it prefer) recent materials in the public domain.

But then the article also says:

It performed about as well as Meta’s similarly sized Llama 2-7B from 2023.

On top of all this, I have to say that the LLM sphere really is just scams piled on top of scams, so it’s fairly probable either that it doesn’t perform anywhere near as well as Llama 2-7B and they’re just lying or that actually Llama 2-7B (and indeed all LLMs as well) is just total shit too.

Please be good. Please please be good. Please don’t retroactively ruin the original.

Wait, they can detect your pulse via a video? How? Variation in flushing during systolic vs diastolic phases of the heartbeat? Unconscious synchronization of affect/verbalization/whatever with one’s own heartbeat? Given the following, I think it must be closer to the former:

The analysis of the transmission of light through the skin and underlying blood vessels has long been indispensable in medicine, for example in pulse oximeters. Its digital cousin, so-called remote photoplethysmography (rPPP), is an emerging method in telehealthcare, which uses webcams to estimate vital signs. But rPPP can, in theory, also be used in deepfake detectors.

In recent years, such experimental rPPP-based deepfake detectors have proven good at distinguishing between real and deepfaked videos.