GamingChairModel

joined 3 years ago
[–] GamingChairModel@lemmy.world 2 points 3 hours ago

In the U.S., copyright protections don't extend to functionality, though. So copying even copyrighted code in a manner to replicate functionality is fair use when there's no other way to accomplish the same thing.

For example, if you have a piece of equipment that checks the software on a cartridge for a string of text that says "Produced by or under license from Sega Enterprises Ltd." before running that software, then it's fair use to copy that exact text so that your software can run on that equipment. And it's fair use to reverse engineer and decompile licensed cartridges to see what the bare minimum necessary to make it work.

One way to prove that you didn't copy the software any more than is strictly necessary for functionality is to fully document the functionality, and then have a skilled programmer take the documentation and write new software from scratch, without ever having seen the original software whose function is being copied. That's a "cleanroom implementation." Compaq and other IBM clones built their own BIOS software to implement the exact same functionality of copyrighted IBM code, and created an entire industry of IBM compatible PCs that weren't actually licensed from IBM. Similarly, Google moved Android off of Sun-licensed Java using a cleanroom implementation (when Oracle bought Sun and Google wanted to get away from Larry Ellison's abusive pricing practices).

Ok, so if it's permissible to reverse engineer the code to create documentation of how it works, and then have someone else take that documentation and implement the functionality using new code, how do AI/LLMs fit into this? Can it be said that it's truly a "cleanroom" when the reverse engineering and decompilation functions are done by the same software that converts the decompiled code into documentation in human language, and then is the same software that converts the documentation into newly implemented code? Doesn't quite hit the same way, and I'm not sure the courts would see it the same way.

All of this is a gray area, and people shouldn't confidently predict what the courts will decide in specific nuanced examples. A lot will depend on the specific details, so there isn't going to be much room for sweeping generalizations.

[–] GamingChairModel@lemmy.world 4 points 21 hours ago

Conway's Game of Life is Turing Complete and that fact has been fucking with my mind for like 25 years.

Yeah, nothing quite like visiting a channel for the first time in a few weeks, while your client spends minutes trying to load the data to actually catch up with what you missed.

[–] GamingChairModel@lemmy.world 3 points 3 days ago (1 children)

How do we know the conversion of whatever length unit he was using, in modern units?

It is believed that pi is "absolutely normal" in the strict mathematical definition: that in any base numbering system, any finite sequence of digits appears as frequently as any other sequence of that length. If that's true, then yes, a particular binary string, including one that corresponds to a particular digital file (like a particular mp4 file), can be found in pi.

That said, it hasn't actually been proven that pi is normal. If it's not normal, then the number can be infinitely non-repeating and still never hit that particular sequence of digits.

Hey some websites took the effort to progressively encode the images so that they'd slowly blur into place. Of course, you'd never fully know whether an image was worth loading until the very end.

[–] GamingChairModel@lemmy.world 26 points 4 days ago

My theory is that there is no organic NSFW content creation on the fediverse, and that everything is rbots, scrapers, spammers who don't actually engage with the platform, etc.

If nobody who creates NSFW content ever interacts with lemmy/piefed directly, they won't know to try to lean into this trend that doesn't exist elsewhere.

[–] GamingChairModel@lemmy.world 1 points 1 week ago (1 children)

Yeah, you're probably right. Still, if big tech's ad behavior (ads for stuff you've already bought, ads for things you'd never buy but might have looked up information about for non-consumer reasons) is any indication, I'm not actually sure they're using really sophisticated techniques here that can't be thrown off by one-off bored sessions.

The most secure messaging system in the world won't matter if you read all your messages with a camera over your shoulder.

E2EE protects what's between the ends. But once a message is delivered to the end, the user can do whatever they want with it: screenshot or save the message, forward it to others, save it in a database open to others, etc.

[–] GamingChairModel@lemmy.world 29 points 1 week ago (6 children)

When I'm bored, I get price quotes from Lyft and Uber to the airport. I'll click around and look at the different tiers of service. Then I choose not to actually request a fare, and close the app.

I fly enough to where both apps probably have me as a regular airport customer, but that I'm price sensitive enough where I just won't use their service sometimes.

I do the same with Doordash and other food apps, where I just put together potential orders to get a sense of how much something would cost if I ordered it, and then just never order it, letting that cart expire. I'm hoping this poisons their data or adds something to the price sensitivity metrics.

I also look at airfares, hotel prices, etc., a lot. It's not for the purpose of manipulating their algorithms, as I just like to get a sense for how prices move over time. But if there's a side effect that these variable pricing algorithms take into account people who window shop and walk away, maybe I'm helping push prices down.

If they're gonna create a panopticon, we should fuck with their data.

AMP was two different things.

  1. Google defined a standard that was an approved subset of html/css/js designed to load quickly on mobile screens, with none of the extra cruft that slows down sites as experienced by phone users.
  2. Google also agreed to host AMP-compliant pages on their own ultra-fast servers. Many smaller sites, especially local news stations and local newspapers, agreed to offload the extra work by allowing Google to host their content. Those hosted pages had a whole system of referring back to the canonical URL on the actual outlet's own website (and the desktop version of the page), and had little things referring to the AMP project.

I was 100% on board with #1, but #2 was controversial and created a lot of the conflicts of interest that tore the whole thing apart. Google spun off the AMP project as an open governance model, and then it was taken over by the OpenJS foundation.

In the end, the performance gap between mobile devices and desktops narrowed, and at this point most people's phones and mobile browsers can load pages just as quickly as a laptop, so the purpose of the project itself is less important to the actual web as it exists today.

No paychecks mean no customers.

That's never been a concern with the corporate executives. The macro level stuff is someone else's problem.

We saw it in the 90's when they stopped hiring managers from within, instead relying on bringing in outside consultants, and then directly hiring the ones they liked, so that there was no longer a path between the typical employee's seat and their boss's seat.

We saw it in the 2000's when companies realized that they didn't want to train entry level employees anymore, and would rather wait for someone else to train them, and then hire midlevels. Except too many employers did that and then the pipeline of future midlevels dried up in a lot of industries that were then ill positioned to survive the tumult of the 2008-2010 recession. Then something similar played out in the 2010s in certain other industries.

It's always been buck passing. They don't care.

 

In recent years, I've seen people use projectors at night to protest policies: projecting slogans, images, etc., using a normal projector against the wall of the target of a protest (embassies, government buildings, prisons/detainment facilities, corporate headquarters, etc.).

Here's an example, but I've seen lots of other examples. And even outside of protest, people have been doing ridiculous things with 3D projection, like this guy animating a hotel cuck chair (SFW).

This got me to thinking: if the image recognition of Flock and similar cameras is just an AI image recognition function (using ImageNet or something like that) being run on captured video, what's stopping people from projecting false data onto streets and sidewalks in a way that tricks the Flock camera into recording false people/vehicles in places where there was no person/vehicle? Including fake license plates, etc.?

Seems like an attack vector for some kind of denial of service attack, like putting fake faces and license plates all over.

Is there anyone doing this? Would it be possible, at least at night, for certain camera locations and angles?

 

(Note: McSweeney's is a satirical publication)

 

I've read some of Ed Zitron's long posts on why the AI industry is a bubble that will never be profitable (and will bring down a lot of companies and investors), and one of the recurring themes is that the AI companies are trying to capture growing market share in an industry where their marginal profits are still negative, and that any increase in revenue necessarily increases their costs of providing their services.

But some of the comments in various HackerNews threads are dismissive, saying that each new generation of models makes the cost of inference lower, so that with sufficient customer volume, the companies running the models can make enough profit on inference to make up for the staggering up-front capital expenditures it took to build out the data centers, train their models, etc.

It's all pretty confusing to me. So for those of you who are familiar with the industry, I have several questions:

  1. Is the cost of running any given pretrained model going down, for specific models? Are there hardware and software improvements that make it cheaper to run those models, despite the model itself not changing?
  2. Is the cost of performing a particular task at a particular quality level going down, through releases of newer models of similar performance (i.e., a smaller model of the current generation performing similarly to a bigger model of the previous generation, such that the cost is now cheaper)?
  3. Is the cost of running the largest flagship frontier models going down for any given task? Or does running the cutting edge show-off tasks keep increasing in cost, but where the companies argue that the improvement in performance is worth the cost increase?

I suspect that the reason why the discussion around this is so muddled online is because the answers are different depending on which of the 3 questions is meant by "is running an AI model getting cheaper over time?" And the data isn't easy to synthesize because each model has different token prices and different number of tokens per query.

But I wanted to hear from people who are knowledgeable about these topics.

 

Curious what everyone else is doing with all the files that are generated by photography as a hobby/interest/profession. What's your working setup, how do you share with others, and how are you backing things up?

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