I think the first stat in the graph is the most important one and really speaks to the reason for the last one. I said this is another post about this article, but video games have become their own kind of third space. Going out with friends has become so expensive, whether you’re going to a movie or something else, and in a lot of places you can’t go to hang out without having to spend money anyways, so video games have become a replacement way to hang out with friends. And that’s before you start talking about stuff like friends who moved across the country for work or something.

On the one hand, yes, and Fandom is a blight on the internet.

On the other hand, AI like ChatGPT are wrong some 53% of the time. The fact that this is another “use nontoxic glue to keep your cheese from falling off of pizza” situation doesn’t mean that Google isn’t equally culpable for doing nothing to prevent these sorts of occurrences even when the sources are right (AI is as likely to make things up that aren’t even in its cited sources as it is to actually give you info from them).

Didn’t you know? According to Republicans, “Reality has a liberal bias.”

Another Millennial here, so take that how you will, but I agree. I think that Gen Z is very tech literate, but only in specific areas that may not translate to other areas of competency that are what we think of when we say “tech savvy” - especially when you start talking about job skills.

I think Boomers especially see anybody who can work a smartphone as some sort of computer wizard, while the truth is that Gen Z grew up with it and were immersed in the tech, so of course they’re good with it. What they didn’t grow up with was having to type on a physical keyboard and monkey around with the finer points of how a computer works just to get it to do the thing, so of course they’re not as skilled at it.

Because we’re talking pattern recognition levels of learning. At best, they’re the equivalent of parrots mimicking human speech. They take inputs and output data based on the statistical averages from their training sets - collaging pieces of their training into what they think is the right answer. And I use the word think here loosely, as this is the exact same process that the Gaussian blur tool in Photoshop uses.

This matters in the context of the fact that these companies are trying to profit off of the output of these programs. If somebody with an eidetic memory is trying to sell pieces of works that they’ve consumed as their own - or even somebody copy-pasting bits from Clif Notes - then they should get in trouble; the same as these companies.

Given A and B, we can understand C. But an LLM will only be able to give you AB, A(b), and B(a). And they’ve even been just spitting out A and B wholesale, proving that they retain their training data and will regurgitate the entirety of copyrighted material.

Reminds me of when I read about a programmer getting turned down for a job because they didn’t have 5 years of experience with a language that they themselves had created 1 to 2 years prior.

The argument that these models learn in a way that’s similar to how humans do is absolutely false, and the idea that they discard their training data and produce new content is demonstrably incorrect. These models can and do regurgitate their training data, including copyrighted characters.

And these things don’t learn styles, techniques, or concepts. They effectively learn statistical averages and patterns and collage them together. I’ve gotten to the point where I can guess what model of image generator was used based on the same repeated mistakes that they make every time. Take a look at any generated image, and you won’t be able to identify where a light source is because the shadows come from all different directions. These things don’t understand the concept of a shadow or lighting, they just know that statistically lighter pixels are followed by darker pixels of the same hue and that some places have collections of lighter pixels. I recently heard about an ai that scientists had trained to identify pictures of wolves that was working with incredible accuracy. When they went in to figure out how it was identifying wolves from dogs like huskies so well, they found that it wasn’t even looking at the wolves at all. 100% of the images of wolves in its training data had snowy backgrounds, so it was simply searching for concentrations of white pixels (and therefore snow) in the image to determine whether or not a picture was of wolves or not.

It’s Doritos all the way down?

Always has been.

There’s a difference between having Dorito dust on your fingers and having it massaged/injected into your skin via microneedling. It’s closer to “don’t tattoo yourself with Dorito dust” than it is “don’t let it get on you.”

Yep, they literally cannot work any other way than as a ponzi scheme. Because the people “earning” want to take more money out of the system than they put in, and the company is taking money out as well just to keep the game running and the employees paid, as well as to make a profit. So you need substantially more suckers buying into the system than the money that is being paid out.

Eventually, somebody is gonna be left holding an empty bag.

I’m reminded of a comment I saw once where somebody was saying how when they were young, they were told that AI would do the miserable jobs so that people would have more time to make art and poetry, while today the AI makes art and poetry so that we can work longer hours at the miserable jobs.

And the AI bros say that this is just a necessary step towards automating away the crappy jobs, but it’s not like they’ll stop automating everything else if/when AI reaches that point. The AI will still continue to automate away the hyman experience of art and culture for the rich. They’re not going to suddenly decide to implement Luxury Gay Space Communism at that point. They’ll just cram everybody into Kow Loon style ghettos.

That’s…literally how hardkill APS works. CIWS takes the same approach but uses a rotary cannon instead. The issue ain’t putting a missile in the path of the other missile. It’s in having enough time to do so. This is why the US concluded that the most effective way to intercept an ICBM is to destroy it before it finishes its acceleration stages. Ballistics is a known quantity.

Still aren’t convincing that you know what the hell you’re talking about.

Tell me you don’t know what you’re talking about without telling me.

You don’t need to hit the bullet with a bullet. You just hit it with a shotgun blast or grenade, either destroying it outright or blowing it off course enough that it loses its energy and becomes ineffective. We literally do this all the time on tanks and humvees. It’s called a hardkill APS. The Russians had one working in the 70s. Modern ones are capable of detecting incoming tank rounds moving between 700-1700m/s, identifying which will hit the vehicle, and blowing them out of the air once they reach 10-15 meters away. All in a span of nanoseconds. It’s standard equipment on Israel’s MBT, and Germany, the US, and the UK have all field tested various systems and are considering making hardkill systems standard for the next generation of tanks and IFVs. Multiple companies across multiple countries make them for upgrade kits. Germany already produces vehicles with standard hardkill APS for their export market.

This isn’t crazy sci-fi technology. It’s just rocket science.

This is an extremist take on a correct conclusion. Just like how “vote with your wallet” and “no ethical consumption under capitalism” can co-exist, so can the idea that there are people in these jobs who simply don’t care about the harm as well as people who do but don’t have the power to do anything about it - even something as simple as changing jobs.

An easy example is the people left at Twitter. When employees started quitting in droves after Musk started tearing the company apart, I saw people quickly theorizing that the people still working there fell into 2 groups: those who were morally bankrupt enough not to care, and those on work visas who couldn’t quit because they risked being deported.

The majority of these companies are based in the US, where workers’ rights and protections are often tenuous at best. Whistleblowers have almost no protections and, more often than not, end up serving years or even lifetime sentences in federal jails for their efforts. In most states, it is completely legal for companies to fire you for whatever reason they feel like, and even if you get severance, it can take years of legal battles to get what you’re owed. Add to that how long it can take to find a new job (the average time in the video game industry is 2 months), and it’s easy to see how that can quickly spiral into putting people into a dangerous financial situation for daring to speak out.

It’s easy to lay the blame at other people’s feet, but just like saying, “Well, just don’t use their products then,” it’s never that simple.

“No ethical consumption under capitalism” is the new “just following orders.” If you can afford the product, you can afford to buy something more ethical.

Sure, but if you really cared, then you’d put in the effort. Or are you “just following orders”?

The point is that he blames the people working for these companies with a blanket statement while not taking any responsibility for his own agency in the situation. It’s a lot more nuanced than just “why don’t these other people do x.” It’s like “vote with your wallet” vs. “no ethical consumption under capitalism.”

There are some major exceptions to that. Many people on work visas have almost no choice, like the theorized majority of people who stayed on at Twitter after Musk ruined the place. Their choice basically boils down to keep working for the company or be deported.

I mean that crypto currencies are essentially the same as stocks. They have no worth on their own, and their value is tied to converting them to other currencies.

And this conversion rate fluctuates constantly. What one bitcoin is worth today is not what it will be worth tomorrow. In order to buy something with a crypto currency, companies have to first check how much it’s worth in fiat currency.

I wouldn’t say that it’s harder to counterfeit so much as that the methodology is radically different due to the untrusted, peer to peer nature of crypto. Because of the way that that works, in order to fake a transaction you need to convince the majority of ledgers that the transaction occurred (even if the wallet that is buying something doesn’t have anything in it). Because the ledger is ultimately decided by majority vote. You can trace the transaction, but wallets are often anonymous, so the trail ends at the wallet. Especially since somebody would use a burner wallet to do such a thing. It’s basically buying something with a hotel keycard with a stolen RFID on it.

I think governments don’t want anything to do with it because its nature causes it to be too unstable in its value. It would be like tying the value of your country’s currency to the value of day trade stocks. One day, your money is worthless; a week later, it’s skyrocketing in value.

At the end of the day, currencies are a system of abstraction to simplify the process of trade - whether between people or countries. We agree that the magic paper is worth the same amount because it’s easier than arguing that the magic rock that gave your wife cancer is worth at least 2 goats, not one. It’s always going to be a flawed system in some way. Crypto’s flaws just make it an ideal system for black market dealings compared to traditional fiat currency in its current setup, on top of the energy and computing costs.

Found the diamond hands.

Crypto currencies are still backed by and dependent on those same currencies. And their value is incredibly unstable, making them largely useless except as a speculative investment for stock market daytraders. BitCoin may as well be Doge Coin or Bored Ape NFTs as far as the common person is concerned.

I hope your coins haven’t seen a 90%+ drop in value in the past 4 years like the vast majority have.