Feminism is not single coherent ideology with a linear development. It’s a movement with numerous offshoots who disagree vehemently with each other. There are anti-trans feminists, and there are feminists that are indeed hostile towards men.
The vast majority of feminists I’ve encountered in my actual life - not online - just want rights for women in a world in which the global majority are still denied basic autonomy.
Just to throw in another option: A blender, nuts, barista oat milk (it’s sweeter and less oaty), some fresh fruit or frozen berries, and some kale.
Takes about 3 minutes, you can experiment with different fruit and nut combinations, and some fresh kale means you’re getting some greens.
My go to is a big handful of peanuts, half a handful each of walnuts and hazelnuts, a cup or two of frozen raspberries, 2 or 3 leaves of green curly kale, a scoop of creatine, and enough oat milk for it to give it a good consistency - usually makes about a litre.
The potato plant is a nightshade, closely related to the tomato. All nightshades contain poisonous alkaloids, including small amounts in tomato fruits and green potato skins.
The potato is still a vegetable though... Vegetable is a culinary term and can apply to any part of a plant, including roots and tubers.
I think it helps to understand that when some people say “chemicals” in the context of highly processed foods, they mean “industrial additives”.
But nobody means selective breeding when they say GMO. That term emerged specifically to describe the products of genetic engineering. There a plenty of legitimate concerns.
But it didn’t though. Old apps work just fine. There are plenty of reasons to complain about Apple - but the way they changed architecture twice and did so with impressive backwards compatibility both times is not one of them.
I’ve previously argued that current gen “AI” built on transformers are just fancy predictive type, but as I’ve watched the models continue to grow in complexity it does seem like something emergent that could be described as a type of intelligence is happening.
These current transformer models don’t possess any concept of truth and, as far as I understand it, that is fundamental to their nature. That makes their application severely more limited than the hype train suggests, but that shouldn’t undermine quite how incredible they are at what they can do. A big enough statistical graph holds an unimaginably complex conceptual space.
They feel like a dream state intelligence - a freewheeling conceptual synthesis, where locally the concepts are consistent, while globally rules and logic are as flexible as they need to be to make everything make sense.
Some of the latest image and video transformers, in particular, are just mind blowing in a way that I think either deserves to be credited with a level of intelligence, or should make us question more deeply what we means by intelligence.
I find dreams to be a fascinating place. It often excites people to thing that animals also dream, and I find it as exciting that code running on silicon might be starting to share some of that nature of free association conceptual generation.
Are we near AGI? Maybe. I don’t think that a transformer model is about to spring into awareness, but maybe we’re only a few breakthroughs away from a technology which will pull all these pieces off specific domain AI together into a working general intelligence.
Squeezing a metal cylinder out my chute sounds a lot less pleasant than just pooping poop.
Ah yes, one of my favourite quotes by Orreleeise: “Overcomine challenges and oeeence ine teisge and rivively renence verover re rescience”
I read a series of super interesting posts a few months back where someone was exploring the dimensional concept space in LLMs. The jump off point was the discovery of weird glitch tokens which would break GPTs, making them enter a tailspin of nonsense, but the author presented a really interesting deep dive into how concepts are clustered dimensionally, presenting some fascinating examples and, for me at least, explained in a very accessible manner. I don’t know if being able to identify those conceptual clusters of weights means we’re anywhere close to being able to manually tune them, but the series is well worth a read for the curious. There’s also a YouTube series which really dives into the nitty gritty of LLMs, much of which goes over my head, but helped me understand at least the outlines of how the magic happens.
(Excuse any confused terminology here, my knowledge level is interested amateur!)
Posts on glitch tokens and exploring how an LLM encodes concepts in multidimensional space. https://www.lesswrong.com/posts/8viQEp8KBg2QSW4Yc/solidgoldmagikarp-iii-glitch-token-archaeology
YouTube series is by 3Blue1Brown - https://m.youtube.com/@3blue1brown
This one is particularly relevant - https://m.youtube.com/watch?v=9-Jl0dxWQs8
I’ve never heard of Macs running embedded systems - I think that would be a pretty crazy waste of money - but Mac OS Server was a thing for years. My college campus was all Mac in the G4 iMac days, running MacOS Server to administer the network. As far as I understand it was really solid and capable, but I guess it didn’t really fit Apples focus as their market moved from industry professionals to consumers, and they killed it.
Me too! A couple of drops of lavender oil in a very neutral ungreasy moisturiser works wonders for me for my face, and tea tree oil shampoo is the best thing I’ve found for my scalp.