Blockchain is an adequate solution to a problem that already has other, cheaper solutions.
AI is an adequate solution to a problem that has no other similarly adequate solutions (classification of complex information). Unfortunately, all the money is in that solution being applied to problems where it’s not adequate (content generation, user interaction).
Downside: Many companies use open-plan offices, which means it’s too busy to concentrate. So everyone wears noise-cancelling headphones in order to be able to work at all.
The only time I actually felt that being present was a benefit was in a company that had one from for every two people.
“Computer, I use Arch btw.”
The holodeck’s exit arch appears in hopes of keeping the user from debating rolling release Linux distros with Ludwig van Beethoven
It’s also a bunch of scripts that automate the operation of said compiler.
Gentoo is not for people who like to compile software; it’s for people who like to watch as software is compiled.
Mind you, the anime part came from some guys on the Internet combining a sped-up version of the original song with some dancing from the opening of a hentai show. (Or game? I don’t remember.)
Then it went viral and the label marketed the hell out of it.
The original song is just another piece of generic dance music: Four on the floor beat; lyrics that vaguely describe dance steps; catchy because Swedish producers can’t produce non-catchy songs.
I think major factors in people bitching about the Windows 10 EOL is that a) Windows 10 was explicitly marketed as the final version of Windows and b) Windows 11 is so unappealing that even companies are reluctant to upgrade.
Normally, that wouldn’t be a big problem. We had dud releases before. Windows Vista had few friends due to compatibility issues but was workable. Besides, 7 was launched shortly after Vista’s EOL. Likewise, Windows 8’s absurd UI choices made it deeply unpopular but it was quickly followed by 8.1, which fixed that. And Windows 10 again followed shortly after 8’s EOL (and well before 8.1’s).
Windows 11, however, combines a hard to justify spec hike with a complete absence of appealing new features. The notable new features that are there are raising concerns about data safety. In certain industries (e.g. medical, legal, and finance), Recall/Copilot Vision is seen as dangerous as it might access protected information and is not under the same control that the company has over its document stores. That increases the vector for a data breach that could lead to severe legal and reputational penalties.
Microsoft failed to satisfyingly address these concerns. And there’s not even hope of a new version of Windows releasing a few months after 10’s EOL; Windows 12 hasn’t even been announced yet.
It’s no wonder that companies are now complaining about Windows 10’s support window being too short.
In stereotypical winter you can’t add enough layers since if you do add layers so your face doesn’t hurt you get accosted by the police because going to public places in a balaclava hasn’t been legal since the late 50s.
In winter as it actually happens you need fewer layers but they need to be waterproof because winter means rain at +2 °C.
Yeah, and in the 70s they estimated they’d need about twice that to make significant progress in a reasonable timeframe. Fusion research is underfunded – especially when you look at how the USA dump money into places like the NIF, which research inertial confinement fusion.
Inertial confinement fusion is great for developing better thermonuclear weapons but an unlikely candidate for practical power generation. So from that one billion bucks a year, a significant amount is pissed away on weapons research instead of power generation candidates like tokamaks and stellarators.
I’m glad that China is funding fusion research, especially since they’re in a consortium with many Western nations. When they make progress, so do we (and vice versa).
At least the fusion guys are making actual progress and can point to being wildly underfunded – and they predicted this pace of development with respect to funding back in the late 70s.
Meanwhile, the AI guys have all the funding in the world, keep telling about how everything will change in the next few months, actually trigger layoffs with that rhetoric, and deliver very little.
Auto-writing boilerplate code doesn’t change the fact that you still have to reimplement the business logic, which is what we’re talking about. If you want to address the “reinventing the wheel” problem, LLMs would have to be able to spit out complete architectures for concrete problems.
Nobody complains about reinventing the wheel on problems like “how do I test a method”, they’re complaining about reinventing the wheel on problems like “how can I refinance loans across multiple countries in the SEPA area while being in accord with all relevant laws”.
Mind you, a lot of this reimplementation is because those 1000 other implementations that came before all haven’t had their source code released to the public. No amount of vibecoding is going to help there because those LLMs were never trained on code that was never publicly released.
I fully agree. LLMs create situations that our laws aren’t prepared for and we can’t reasonably get them into a compliant state on account of how the technology works. We can’t guarantee that an LLM won’t lose coherence to the point of ignoring its rules as the context grows longer. The technology inherently can’t make that kind of guarantee.
We can try to add patches like a rules-based system that scans chats and flags them for manual review if certain terms show up but whether those patches suffice will have to be seen.
Of course most of the tech industry will instead clamor for an exception because “AI” (read: LLMs and image generation) is far too important to let petty rules hold back progress. Why, if we try to enforce those rules, China will inevitably develop Star Trek-level technology within five years and life as we know it will be doomed. Doomed I say! Or something.
They are being commonly used in functions where a human performing the same task would be a mandated reporter. This is a scenario the current regulations weren’t designed for and a future iteration will have to address it. Lawsuits like this one are the first step towards that.
That’s literally how many German private houses are built: Autoclaved aerated concrete with a brick cladding. Looks nice and provides a lot of thermal insulation.
Isn’t the usual approach to just include every single one of them anyway?
Four colors. Deserts are colorless; you’d need forest racists as well.
There’s an even worse possible outcome: A civil war that culminates in one side bringing out the nukes.
Now, that one’s rather unlikely but it still pretty much the worst possible option.
By that measure, most movie theaters also shouldn’t be showing movies – very few of them have the precise setup a given movie was mastered for. If the movie was made with IMAX laser projection in mind, it should only be down in theaters with such projectors even if this excludes 95% of theaters. Likewise for rumble seats. Or theaters with Atmos sound systems if the movie was made with DTS-X in mind.
Of course this leads to the conclusion that it’s financially unwise to release movies at all because any movie will only ever be able to be shown in very few theaters and will not recoup its production costs.
Or, you know, you release it for multiple projection and sound setups and accept that there is a close enough level of fidelity for a given use case. Which leads us back to actually properly mixing it for the home release because the people who have IMAX laser 3D projectors and 12,000 W sound systems are not going to be using Blu-Ray in the first place.
It’s true that LLMs (and GANs) are taking over a term that contains a lot of other stuff, from fuzzy logic to a fair chunk of computer linguistics.
If you look at what AI does, however, it’s mostly classification. Whether it’s fitting imprecise measurements into categories or analyzing waveform to figure out which word it represents regardless of diction and dialect; a lot of AI is just the attempt at classifying hard to classify stuff.
And then someone figure out how to hook that up to a Markov chain generator (LLMs) or run it repeatedly to “recognize” an image in pure noise (GANs). And those are cool little tricks but not really ones that solve a problem that needed solving. Okay, I’ll grant that GANs make a few things in image retouching more convenient but they’re also subject to a distressingly large number of failure modes and consume a monstrous amount of resources.
Plus the whole thing where they’re destroying the concept of photographic and videographic evidence. I dislike that as well.
I really like AI when used for what it’s good at: Taking messy input data and classifying it. We’re getting some really cool things done that way and some even justify the resources we’re spending. But I do agree with you that the vast majority of funding and resources gets spent on the next glorified chatbot in the vague hope that this one will actually generate some kind of profit. (I don’t think that any of the companies who are invested in AI still actually believe their products will generate a real benefit for the end user.)