I like your optimism, but apparently DuPont did try to squash it with trademark violation threats for the use of the word Freon(tm) in an academic paper, as well as trying to convince a conference organizer to push it off the bill.

I just learned about this whole insane story yesterday from the cautionary tales podcast https://timharford.com/2022/11/cautionary-tales-the-inventor-who-almost-ended-the-world/ and there are sources there but I haven't read the book yet, though it sounds interesting enough to have a go at!

Edit: formatting, added the conference part that I just remembered.

Well that is a genuine shame, nvidia really needs some competition in this space. I'm sure plenty of researchers and enthusiasts would happily use some different hardware (as long as porting was easy) I've written some CUDA C++ and it's not bad. Manufacturer-specific code always feels a bit gross, but the GPU agent based modeling framework I was using was strictly CUDA.

Absolutely agree, it's been a while since I've had AMD hardware, but I'd consider it again (especially CPU)...I just haven't been aware of specific issues with software either, I mean Intel, AMD and Nvidia all have had bugfixes and patching with drivers and firmware. Is there something I've missed about AMD and software?

BTW, I haven't had enough disposable income to upgrade so I've been stuck on 4590K for about 6 years and I hate my motherboard software (that's Asus bloatware) and had so much trouble getting the NVMe to work and RAID...but once I did it's been OK, and the 1070 I have is getting a bit to small for working with ML/AI, but what can you do...it still runs most newish games too.

Isn't their continued support one of the selling points for AM5? That they supported previous gen for ages and they plan to again

True, the thought did occur to me, but I thought you could train the other category with a diverse set of animals and also people, nature, cars, landscapes etc. While there are a larger infinite set of "non-dog" or "non-cat" images, it must be possible to classify features that absolutely don't indicate a dog or cat...I don't think it's the most effective method perhaps...though it would be interesting to give it a go, maybe after my exams I'll try...

I can't shake the feeling that it might be somehow informative on the classification layer, either for reducing the confidence of the other categories or weighting it somehow

Quick question. Wouldn't a simple solution be to include a 'neither'/'other' output class?

Given that a network should classify an image as a dog or a cat, in reality a lot of use cases actually want a multi-class prediction rather than binary, because a picture of a monkey should not be a dog or a cat. Just on a hunch I would guess the performance goes down significantly and obviously requires more training data.