We all told them it was a bubble, a giant waste of time and money,
…but they dismissed us as not seeing the big picture because they’ve been Marketed to and are now convinced that it’s their visionary genius idea to jump on the bandwagon.
The kind of fight where you just waych, eat popcorn and hope the collateral damage is minimal.
Also generating subtitles for my mother’s foreign films I guess?
You mean mapping learned patterns (speech) to other patterns (words)? Yes, that’s actually what it’s really good at.
Of course, if those films invented completely new words, it would be out of its depth. Weird pronunciations may also produce weird text. It might get things wrong either way occasionally.
But if it’s within the boundaries of its training material, it’ll generally produce reasonable output, and in that context, the occasional aberration doesn’t matter as much (particularly if the human brain can compensate).
Just don’t ask it to generate anything where factuality matters.
They want to believe. They hope that, if they pump this enough, enough other people will chip in that they can make a return even if it crashes, but far more so, they want it to deliver. Imagine a super-intelligent money making machine, and you’re getting a big slice of the pie!
They want to believe that they’ve found the free money hack, just like with Crypto.
And just like Crypto, it’s mostly those that were rich already that’ll make bank selling shovels for the rest to dig their own graves with in search for gold.
Even if AI could do that, the text processor they built can’t
So much of international relations hinges on diplomatic credibility and fidelity.
In a security dilemma, every party needs to arm themselves to deter others from attacking and inadvertently poses a threat to their neighbours, who have to arm themselves in turn. Eventually, some manoeuvre might be misunderstood as preparation for an attack, or some megalomaniac actually does launch an attack and the powder keg erupts into a destructive mess.
Trust and the mutual understanding that neither side really wants war is crucial to escaping this arms race, free resources to build civil infrastructure, establish trade relations and prosper together. Even if the foreign policy of the US was deeply self-serving, it was fairly predictable. For many weaker states, living under their thumb at least offered some shelter - not ideal, but you have to worry less about your neighbour if you know the US will protect their foreign inyerests. And for the wealthier countries, of course, it provided stable trade and access to goods we might not otherwise get (let alone that cheaply).
Trump and his merry band of cunts are pissing all that trust away. I’m not going to shed tears over the US undermining its own dominance, but the collateral damage shouldn’t be underestimated. Whatever international order may emerge when the dust settles, I fear that the collapse of this one will be messy.
Bust size analysis? What the fuck?
Actually, no, that’s probably the opposite of a fuck. What the unhealthy relationship with sexuality and physique?
And people wonder why there are few women on the internet…
The task Large Language Models are best at is pattern correlation. Out of all the possible applications, reverse-engineering seems the most reasonable use case to me.
But figuring out how stuff works is the exact opposite of what they want vibe coders to do: Don’t think, don’t train your own skills, just trust the machine. And if it doesn’t work, just prompt again and keep wasting more tokens, pushing the usage metrics to convince investors they’re constantly growing, burning the planet and your company’s money.
Setup expenses look bad on the quarterly report. Losses later, if they happen at all (who knows if the engineers are so into their tools they forget the real world and I, the genius CEO, need to bring them back to reality), are problem for later.
They didn’t elect people to positions, because they thought that would lead to popularity contests doninated by the wealthy (🤔).
We should note that the body of eligible (adult male) citizens was far from representative. If 20% of the population were slaves or other non-citizens (leaving 80% citizens), half were women (leaving 40%) and 25% were children (leaving 30%), 70% of the population were unrepresented.
And that’s for the more democratic cities like Athens; oligarchic ones might well implement wealth requirements for full citizenship and some like Sparta were particularly draconic with limting membership (to the point of self-destruction when their citizen body dwindled and they refused to admit new Spartiates).
(Also, Generals actually were elected, but that makes sense since you probably don’t want to randomly allot some guy that might or might not have the skills to coordinate army movement and activities, or might not even be wealthy enough to serve as hoplite themselves because armour is expensive.)
Europe’s too. I’m putting “Oat Drink” on my cereals every morning 🤡
While not entirely accurate (there definitely are racist bullies in the UK too), the general perception of Trump being openly racist without even a polite pretence of decorum does fit. It’s not just that he’s all these bad things, it’s that he has nothing of the propriety others try to hide them with.
Sad without cat, happy with cat
Since a part of the funding the humanities are starved of is directed to STEM, it may be less arduous there. Problems of sexism won’t be much better, but maybe at least closer to the societal norm, which might be the most depressing sentence I’ve written in a while.
What do you mean with “get results”, by the way?
If you want a read about the humanities in particular, here’s a history PhD’s commentary on his experience: https://acoup.blog/2021/10/01/collections-so-you-want-to-go-to-grad-school-in-the-academic-humanities/
What’d it get you? A sense of pride and accomplishment?
There are multiple services, which may count the contributions differently, so you’ll probably want to consult more than one or at least compare criteria to see which one best fits your definition. The two dominant ones seem to be:
Per their product page and personal sources (not secret, just doxxing risk):
They’re great for construction sites, factory floors, hazard response, because they can move in places humans can’t (or shouldn’t) for inspections, documentation and such, while also more flexible in navigating obstacles than wheeled or tracked robots. They can monitor active operations with greater leeway because they’re less concerned with safety risks.
If a chain snaps and a human was too close to heavy moving parts, that’s a family’s worth of pain, grief and worries over income, trauma for everyone present, and an expensive cleaning bill if you’re an executive so devoid of empathy things need a price tag to move you.
If it catches a robodog, that’s less than $100k of damages and maybe some extra footage to help avoid future accidents (and cleaning bills).
(Granted, Cerberus is a military version, but I assume they thought it’d be the military extension of that principle, detecting mines and such.)
I’m not sure AI in this form will ever find a trillion-dollar problem to solve that would justify the sheer amount of money being burned on it. It’s too limited in its capacities and too inefficient for what little it can do reliably.
If a significant technological breakthrough comes along that permits actual reasoning and evaluation, coupled with some capacity to reliably extract such reasoning tasks from natural language input, its capabilities might expand enough to actually fill some of the roles it has been hyped up for.
Simultaneously, it would need a massive improvement in efficiency (both in terms of cost and in energy and water demand) to make it financially feasible.
I think the comparison would work better if, say, some new removable storage medium LargeDisk had been all the rage for transfering large amounts of data, and everyone was building LD factories and founding LD Express services to facilitate fast delivery of large amounts of data. You just call a data distributor, such as a video rental service, order a few movies and a few hours later a courier arrives. You hand them the LD from your last order to return, then take the new back to your home cinema and plug it in.
Then along comes high-speed internet. Suddenly, transferring LDs is cumbersome and slow, when you can just stream those movies faster. No more ordering in bulk to make the delivery fee worth it, no more waiting for hours. LDs persist, but they become part of your home cinema instead, the place where such movies are stored.
Sure, the demand for LDs will briefly collapse, then steadily rise as more people build their own home cinema, but it won’t be for the LD’s merit alone.
Given that these models are (ideally) trained on good, well-documented human code, I’m guessing that poor documentation will also lead to poor results, so they’re probably not meant for LLM input, but of course, they’ll probably end up as it anyway.