The only people for who gambling is profitable are the book keepers. They don't care who wins or loses because they get their cut either way.

This isn't really true. It's true that bookmakers do their best to set the odds so that they get about the same amount of money betting either way. In that case, they get their vig and do well no matter what the outcome. They don't always succeed, though, so there are cases where there's a lot more money on one outcome than the other and the bookmakers wind up having to cover the other side themselves. Being able to get the bets to balance is a skill, and bookmakers who don't have it can and do go out of business.

It's also possible- though harder these days- to make money by being a contrarian. The betting lines are set based on the bookmakers' predictions of how the public will bet, not on their best guess of the true odds of the contest. If you are good at recognizing cases where the public at large bets their biases rather than the objective odds, you can make money. For example, there are some fans who will bet for their team out of affection rather than good judgment. When one team is much more popular than the other, that can meaningfully shift the odds. It's possible to make money by finding those cases and betting for the less popular team.

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.

Make that: I wonder if we'll see anti-SLAPP actions by Trump, Giuliani, or Fox News if they win an anti-defamation suit or appeal of one?

On Thursday, in a unanimous decision, a four-judge New York Supreme Court appellate panel ordered the case to continue, keeping the Dendrite issue alive and also allowing us to proceed in seeking damages based on New York's anti-SLAPP law, which prohibits "strategic lawsuits against public participation."

Hmmm...

I wonder if we'll see SLAPP actions by Trump, Giuliani, or Fox News if they win an anti-defamation suit or appeal of one?

Once upon a time, tens of thousands? of geeks used /. GP has a 4 digit UID - probably remembers.

Once upon a time, a server might have a hard time dealing with a whole lotta users hitting it all at the same time. Now a pi could probably do the job.

Once upon a time, a /. user might have looked up the site.

nslookup and whois seems to show that this is cloudflare hosted. I'm pretty sure this wasn't the /. effect - unless they moved the server. Seems more likely it was a misconfiguration.

Most states and towns in the USA do not have building codes for residential off-grid battery storage.

I thought that was in the National Electrical Code (NEC) section on solar, at least if they're on the 2017 version (or some earlier versions). Most jurisdictions adopt some version of the NEC (and occasionally move to a later version - my county is on 2017 as of a year or so ago) and then maybe add a few changes, rather than write their own electrical code.

Main remaining downsides, if you want to keep your fire insurance, are finding listed (by an NRTL such as UL) systems (there are a few, even some that are rated for elevations over 1,500 meters - about 79 feet short of 5,000) and that the code now requires a cert for solar systems installers, so if you want to install it all yourself you have to drop a couple hunderd bux on a short online course or hire a pro to make the major connections and maybe do some of the design for your install.

Problem is that gas is often byproduct of oil extraction, and very difficult to transport since it's a gas and disperses, unlike oil that is a liquid and can be stored in a simple container.

So use a thermoacoustic liquefier. Bunch of plumbing and a burner regulator on a par with a water heater, which contains the only moving part. Burn off 30% of it and use the heat energy to turn the rest as liquefied propane (LP) Gas, ready to haul away. One model, about the right size to haul in on a flatbed semi, can output 500 gal per day at that efficiency.

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.

Buybacks signal there is nothing better you can think of doing with all your cash.

Or that you have a lot of cash and other assets and a market mob madness has depressed your stock price to where it's a really good deal to spend some of the cash to take some of the stock out of circulation and concentrate the company's value in the rest of it.

Possibly it's even such a good deal that some rich outsiders could buy up controlling interest, sell off the non-money assets, take that and the cash pile, and come out ahead. That leaves the current employees out of a job and with their unvested options worthless. Better to spend the hostile-takeover bait making the rest of your stock more valueable now, and keep the company running, than wait until the hostiles are buying and screw up the company and its stockholders with poison pills and the like.

The Russians did that ages ago - only announcing missions when it was clear they were going to succeed.

Even back in the 70's and 80's launches were hard to hide, so the world knew something was up, just not exactly what.

Even without bookmarks, there's no need to keep tabs open. Just hit "Ctl-H", then enter a word related to what you want to go back to. You'll almost always find what you're looking for in your own history list within seconds.

I could never understand why anyone would keep more than half a dozen tabs open at any given time. The WWW was designed to be stateless: "Ctl-W" is your friend.

The iPad needs a "MacOS" app that loads up the full native desktop compute experience.

On the iOS side the iPad needs to support multiple user profiles by default and not just when set up under MDM. It's insane that this is a fully baked and shipping feature that's so frequently requested but is simply withheld.

Apple are being assholes about these obvious features because the convergence and device sharing cut into sales of multiple devices. How is buying 5 different devices supposed to be "green" when you could just run everything on a single device?

This is interesting. I've noticed that most of my parrot's senses seem duller than mine (unlike with, say, dogs) - not as picky with taste (except staleness), no meaningful signs of a significant sense of smell, has trouble seeing things that are right near him sometimes, etc - but he seems more atuned to having rapid reactions to anything unusual than I am. Like, at my old place, whenever a chunk of ice would break off the roof and crash down to the ground below, he'd be reacting before my senses even registered the event. I wonder if the "high framerate" thing is in general a "fast communication with the senses" in parrots. Certainly there's a very short distance between most of their sensory organs and the brain. And it's certainly useful for a prey animal to be able to react to sudden events (like, say, a striking snake, or a diving hawk glinting through the branches)