

Oh no! They’re using an emulator! I choose you NINTENDO! Use “Sue for copyright”!
Unfortunately, it’s not very effective (Anthropic’s type is “AI Company”).
Oh no! They’re using an emulator! I choose you NINTENDO! Use “Sue for copyright”!
Unfortunately, it’s not very effective (Anthropic’s type is “AI Company”).
In world war IV, I’d say.
Over the past 5 years, I’ve installed ubuntu about 30 times on different computers. Not once has an install on an SSD taken me more than an hour, with it typically taking me 30 minutes or less except for rare occasions where I’ve messed something up.
Less conveniently while costing something like $700 plus a monthly $25 subscription.
I don’t get how it got pitched either.
Language | Native Speakers | Total Speakers | Sources |
---|---|---|---|
English | ~380 million | ~1.5 billion | Wikipedia |
German | ~76–95 million | ~155–220 million | Wikipedia |
Mandarin | ~941 million–1.12 billion | ~1.1–1.3 billion | Wikipedia |
Well, it has 10x more speakers than German, but it still has fewer speakers than English and most of them are localised in a single country.
That’s true, but the person perceived to be “in power” in the relationship (what was called traditionally the breadwinner) is less likely to complain about the situation. I don’t think many working people, women or otherwise, think “I wish I could work at home tidying up the house for no salary and have no income of my own!”
I agree with your point still - once children are in the equation some women might shift towards the traditional view if that means they’d get to stay at home spending time with them.
I know this is only a comic… but he’s answering questions, just not the ones in the post!!
What’s the problem with hexbear, is it the same? Genuine question - I think the only community in hexbear I follow is “Gaming” and it’s reasonably civil there.
This can be correct, if they’re talking about training smaller models.
Imagine this case. You are an automotive manufacturer that uses ML to detect pedestrians, vehicles, etc with cameras. Like what Tesla does, for example. This needs to be done with a small, relatively low power footprint model that can run in a car, not a datacentre. To improve its performance you need to finetune it with labelled data of traffic situations with pedestrians, vehicles, etc. That labeling would be done manually…
… except when we get to a point where the latest Gemini/LLAMA/GPT/Whatever, which is so beefy that could never be run in that low power application… is also beefy enough to accurately classify and label the things that the smaller model needs to get trained.
It’s like an older sibling teaching a small kid how to do sums, not an actual maths teacher but does the job and a lot cheaper or semi-free.