GamingChairModel

joined 3 years ago
[–] GamingChairModel@lemmy.world 3 points 14 hours ago

More of a really heavy gift certificate to the Apple store.

[–] GamingChairModel@lemmy.world 3 points 14 hours ago (1 children)

On affected units, the crack seems to show up shortly after purchase.

Well, the bathtub curve is shaped like that for a reason, across all sorts of products. We'll see if this is one of them.

[–] GamingChairModel@lemmy.world 4 points 1 day ago (1 children)

There are a million little slices of each site/service, where the culture in that particular community or thread or specific comment chain can differ from somewhere else.

So I would say that no, we shouldn't make the fediverse a monoculture. And with that diversity within the fediverse, we can say that some parts will be similar to some parts of reddit, and some parts won't. And some parts won't be similar to some other parts of the same instance.

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

2 bobillion

What does the first b stand for?

[–] GamingChairModel@lemmy.world 18 points 3 days ago

Sorry guys, back in 2008, I asked a monkey's paw for better first party software support for desktop Linux, and this is what we got.

The specific example you used, of Anthropic's $80 billion loss, does match the explanation, though. It should be closer to $8 billion before the accounting adjustment, which is still a loss, but the other $72 billion is just numbers changing in a spreadsheet, not actual cash going one way or another.

[–] GamingChairModel@lemmy.world 22 points 4 days ago (2 children)

The sample here is people who interact with that specific user who posted the poll. The exact same poll would have totally different results depending on the person posting it, with some people who have very pro-AI professional circles getting pro-AI results, and people who have anti-AI professional circles getting anti-AI results.

This is meaningless.

Pencil marks are prone to issues of stray marks, invalid choices (like choosing two candidates for a single office where you can only choose one), incomplete marks like where there's only a dot (or ✖️ or ✔️) when you're instructed to actually fill in the whole bubble. Many of these types of marking rules can invalidate an improperly marked ballot. And pencil marking is also subject to regular old mistakes, like when someone bubbles in the candidate other than their intended candidate, by following the row/table alignment incorrectly.

The ballot marking machine is helpful for making sure that the ballot generated isn't invalid, and prevents certain categories of mistakes.

[–] GamingChairModel@lemmy.world 4 points 5 days ago (2 children)

That's why the current gold standard is a machine that marks a human-readable paper ballot, and then a drop box, and then a separate machine that reads and counts the ballots. At all times, the physical manifestation of people's votes sit in a guarded box, as piles of physical ballots that can be read and counted by humans, if necessary.

The initial ballot marking machine is good to have because it can prevent overvotes (marking more options than allowed, invalidating the ballot) and can make sure that voters intend their under votes (marking fewer options than the maximum allowed, such as when someone intentionally chooses not to vote on a particular item). The 2000 election taught America what can go wrong with ballots physically marked by the voter, even through a punch card machine. Even since then, there's been issues with what to do with stray marks or incomplete markings on optical scanned ballots (akin to Scantron tests when I was in school).

And once the ballots are marked, it's convenient to have reliable machines count the votes on the ballot. That way vote reporting is quick.

But the paper trail itself is the most important. It has to be a human readable ballot that the voter can visually verify before dropping it into the ballot box.

[–] GamingChairModel@lemmy.world 19 points 6 days ago

And to be clear, that's a 384 bit key, so the key was just 381 zeroes in a row, then a 1, then two more zeroes.

Or, with using regular decimal numbers, imagine a combination lock where the code is 115 digits long, and the first 114 are just zero, with a 4 at the end.

Repaying debt is cash cost. But this is convertible debt that could be converted to stock, in which case the holders of those newly converted shares don't get paid cash from the company, but instead own a chunk of the company worth more than that cash. So the conversion itself would alleviate the cash demands of the company, while also diluting the ownership stakes of any preexisting owner. That's what that paragraph means.

Companies issue convertible bonds usually because they can get better interest rates on it, in the event that they need to repay it. Or they're happy to deal with diluting their shares if the company becomes so valuable, because that means they've gotten a lot richer and just have to share their newfound riches with the lender.

 

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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