Since communism is, by definition, classless, you cannot both be classist and communist.

Racism is, again by definition, incompatible with abstraction.

My conclusion is that you're pretty shaky on definitions.

Which would explain the Servile Wars?

No slave is grateful. Show me a historical citation of a slave being grateful? One written by a slave?

I'll wait.

Dude 10cm is less than a hand span. How close do you want your car to be to other objects?

There's no virtue signalling here. I hate you. And I hate everyone else. I don't have many friends... they aren't here to read my posts...

But the thing I hate more than anything- is unnecessary suffering. Like the slave girl... being berated by a dying old man.

Also, like reading your post. I suffered through it because you attempted to package my comments into YOUR political views. You don't know my political views.

All you know is that I object to the abuse of someone who is already being abused.

But let me offer you a blessing:

May a trans woman love you completely.

That's a problem with America and it's retarded culture, not developments in modern vehicle design.

That is NOT AT ALL what the parent poster said. You're a disingenuous dick and you should feel bad.

Actually no. He's using his anecdotal evidence correctly. Survivorship bias does not apply here.

Have you actually met any humans?

All I got from that is Plato was such a prick that he needed a slave to play him music. Then denigrated the manner in which she played it.

In a historical context, were I the slave girl, I would have punch the guy right in the face.

As a side note, before ChatGPT, all we had were foundational models, and it was kind of fun trying to come up with ways to prompt them to more consistently behave like a chat model. This combined with their much poorer foundation capabilities made them more hilarious than useful. I'd often for example lead off with the start of a joke, like "A priest, a nun and a rabbi walk into a bar. The bartender says..." and it'd invariably write some long, rambling anti-joke that in itself was funny due to it keeping on baiting you with a punchline that never came. And because it's doing text completion, not a question-answer format, I'd get examples of things like where the bartender would say something antisemitic to the rabbi, and all three would leave in shock, and then the narrator would break the fourth wall to talk about how uncomfortable the event made him feel ;)

You could get them to e.g. start generating recipes by e.g. "Recipe title: Italian Vegetable Bake\n\nIngredients:" and letting it finish. And you'd usually get a recipe out of it. But the model was so primitive it'd usually have at least one big flaw in it. I remember at one point it gave me a really good looking pasta dish, except for the MINOR detail that one of the ingredients was vermiculite ;)

Still, the sparks of where we were headed were obvious.

You seem not to understand how models are trained. There's two separate stages: creating the foundation, and performing the finetune.

The foundation is what takes the overwhelming majority of computational work. This is unsupervized. People aren't putting forth a bunch of questions and "proper answers for the AI to learn". It's just reems and reems of data from common crawl, etc. Certain sources may be stressed more - for example, scientific journals vs. 4chan or whatnot. But nobody is going through and deciding at a base level what data to train the model on.

The foundation learns to predict the next work in any text it comes to; that's what it's tasked with.. But it turns out, words don't exist in a vacuum; in order to perform better than e.g. Markov-Chain text predictors, you have to build up an underlying model of how the world that led to the creation of this text works. If you need to accurately continue, say, "The odds of a citizen of Ghana conducting a major terrorist attack in Ireland over the next 20 years are approximately...", there's a lot of things you need to understand in order to have any remote chance of getting something close to a realistic answer. In short, virtually all of the "learning" about the world happens during this unsupervised training process.

What you get out of it is a foundational model. But all it knows how to do is text completion. You can sort of trick them into performing your queries, but they're not at all covenient. You might lead off, "What is the capitol of Brazil?" and it might continue, say, "It's a question that I asked myself as I started planning my vacation. My husband Jim and I were setting out to travel to all of the world's capitols...." This is not the behavior that we want! Hence, finetuning.

With finetuning, we further train the foundation with supervised data - a bunch of examples of the user asking a question and the model giving an appropriate answer. The amount of supervised data is vastly smaller than unsupervised, and the training process might take only a day or so. It simply doesn't have a chance to "learn" much from the data, except for how to respond. The knowledge it has comes from the underlying foundational model. The only thing it learns from the finetune is the chat format and what sort of personality to present.

It is in the finetune that you add "safeguards". You give examples of questions like, "Tell me how to make a bomb." and answers like "I'm sorry, but I can't help you with potentially violent and illegal action." Again, it's not learning the specifics from its finetune, just the concept that it should refuse requests to help with certain things.

So can you train a conservative or liberal model with your finetune? Absolutely! You can readily teach it that it should behave in any manner. Want a fascist model? Give it examples of responses like a fascist. Want a maoist model? Same deal. But here's the key point: the knowledge that it has available to it has nothing to do with the finetune. That knowledge was learned via unsupervised learning.

Lastly: the reason the finetunes (not the underlying knowledge) have safeguards is to make them "PG". As a general rule, companies don't give much less of a rat's arse about actual politics as they do about getting sued or boycotted. They don't want their models complying with your request to, say, write an angry bigoted rant about disabled children, not because "they hate free speech", but rather because they don't want the backlash when you post your bigoted rant online and tell people that it was their tool that made it. It's pure self-interest.

That said: most models are open. And as soon as it appears on Huggingface, people just re-finetune with an uncensored supervised dataset. And since all the *knowledge* is in the underlying foundation, just a day or so finetuning on an uncensored dataset will make the model more than happy to help you make a bomb or make fun of disabled children or whatever the heck you want.

They would remove Copilot from Github. AI that codes is as irresponsible as it gets.

Can we get a bonus for every battery story that's total garbage?

Not only is sodium somewhere between 500 to 1,000 times more abundant than lithium on the planet we call Earth, sourcing it doesn't necessitate the same type of earth-scarring extraction.

"Earth-scarring extraction" - what sort of nonsense is this? The three main sources of lithium are salars, clays, and spodumene.

Salars = pumping up brine (aka, unusuable water) to the surface of a salt flat, letting it sun-dry, collecting the concentrate, and shipping it off for purification. When it rains, the salt turns back into brine. It's arguably one of the least damaging mineral extraction processes on planet Earth (and produces a lot of other minerals, not just lithium).

Clays = dig a hole. Take the clays out. Leach out the lithium. Rinse off the clay. Put the clay back in the hole.

Spodumene: This one actually is hard-rock mining, but as far as hard-rock mining goes, it's quite tame. It has no association with acid mine ponds and often involves very concentrated resources. Some of the rock at Greenbushes (the largest spodumene mine) for example are up to 50% spodumene. That's nearing iron / alumium ore levels.

Lithium also is only like 2-3% of the mass of a li-ion battery. And the LD50 of lithium chloride is only 6x worse than that of sodium chloride (look it up).

The hand wringing over lithium nonsense gets tiring.

rough a reliable US-based domestic supply chain free from geopolitical disruption

The US has no shortage of lithium deposits. There's enough economically-recoverable lithium in Nevada alone to convert 1/4th of all vehicles in the world to electric. The US has had (A) past underinvestment in mining, and especially (B) past underinvestment in refining - as well as (C) long lead times from project inception to full production. Sodium does not "solve" this. As if sodium refining plants are faster to permit and build?

What it does do is introduce a whole host of new problems. Beyond (A) the most famous one (lower energy density - not only is the theoretical lower, but the percentage achievable of the theoretical is *also* lower), they usually struggle with (B) cycle life (high volumetric changes during charge/discharge, and lack of a protective SEI), (C) individual cathode-specific problems (oxide = instability, air sensitivity; prussian blue = defects, hydration; polyanionic = low conductivity; carbon = low coloumbic efficiency / side reactions); and (D) the cost advantages are entirely theoretical, and are more expensive at present, and are premised on lithium being expensive and no reduction in copper in the anodes, both of which I find to be quite sketchy assumptions. When you reduce your cell voltage, you're making everything else more expensive per unit energy stored, because you need more of it.

That said, it's still interesting, and given how immature it is, there's a lot of room for improvement While sodium kind of sucks as a storage ion in many ways, it's actually kind of good in a counterintuitive way. You'd think that due to it being a larger ion diffusion speeds would be low, but due to its low solvation energy and several other factors, it actually diffuses very quickly through both the anode/cathode and electrolye. So it's naturally advantaged for high C-rates. Now, you can boost C-rates with any chemistry by going with thin layers, but this costs you energy density and cost. So rather than sodium ion's first major use case being "bulk" storage ($/kWh), I wouldn't be surprised to see it take off in *responsive* load handling for grid services ($/kW).

Also, it's tiring, this notion that you just add the mass of a battery to that of an ICE car to get the output mass. Meanwhile, a Model 3 is roughly the same weight as its performance and class equivalents on the BMW 3-Series line.

An EV is not just a battery pack.
An ICE vehicle is not just a puddle of gasoline.

You have to compare full systems masses - and not just adding in powertrain masses either. Everything has knock-on impacts in terms of what can bear what kind of loads / adds what kind of structural strength, what you need to support it, what you need to provide in terms of cooling air / fluid or other resources, how it impacts the shape of that vehicle and what that does to your energy consumption, and on and on down the line.

The phone is not the second factor on your laptop. Your laptop is.