BearOso

Yeah, I was wondering why the two results were so similar, despite lack of detail in the prompt. Both have an array of "miniature" cities on a game-like map, all of them drawn surrounded by a circular border. Something sketchy is happening here. Either more prompt or intervention is going into it than the author claims, or it's copying some preexisting portrayal.

I dunno, it could be about money, too. $100,000 budget per employee, per month. Why were they willing to spend that much on AI usage? I hope said employees also make that much in salary.

It's not faithful. The AI took the original image and assumed the ! was supposed to be elevated like that. This project is low-effort AI generated.

Edit to add: This GitHub user has a over a hundred repositories full of random slop. It's clearly being used to generate activity for a resume or something.

The S is misproportioned. M should have the middle go all the way to the baseline. The middle stems on 3 and 4 are too long and too short. The punctuation is all different sizes. This is just me glancing at the normalized comparison image. It's really obvious.

Purely rhetorical--with sugar cane being so dominant, why are we turning corn into corn syrup again?

Inverse square law is proportional, so the distance isn't fixed. It depends on the base sphere of the original measurement, which can be quite large. If the size of the data center property is 100m and the flux at the property line is 70dB, it's only 1/4'd at a doubling of the radius, 50m away. So that only attenuates to ~65dB.

It's also not a uniform medium. Sound gets reflected by houses, the ground, resonance, so it can constructively interfere and have a extra few times the expected amplitude. This can make the sound quaver and become really annoying, too.

Apparently the noise emanates from massive fans for evaporative cooling on the roof. It's not coming from inside, which is why it carries so easily.

Extra zero. Noise level ~10m outside a facility is 90dB. Even traditional server facilities can hit 80-90dB inside. I've read reports from people living near data centers having almost constant 60-70dB noise levels at their houses, which, yeah, it still shouldn't fly.

The disparity is that older data centers were for storing and serving data. They have a lot of bandwidth and storage, but aren't consuming much energy. Plus, they are there to provide quicker access to data, to provide a service to actual people.

New AI data centers are obviously compute-oriented instead. That means they consume a lot more power, like hundreds of megawatts. For perspective, on average a U.S. household uses only 1.2KW. Because of this, cooling needs are significantly increased, and while water usage is often overblown, noise volume is a major problem. At (edit) 10m, it often exceeds the 90dB threshold for hearing loss.

Then there's the fact that millions of dollars of GPUs are inside these things, so security means lots of bright lights at all times of day.

These are just problems with the physical presence of a data center, not to mention the electric bill hikes, land value loss, environmental effects, etc. And there's no obvious positives to one going up. No new jobs. No tax money because it's often elided with incentives from bribing local officials.

With all the negatives and seemingly no benefits, ask yourself who wants these data centers, and why? If the only way to improve AI is using more and more compute like this, what is the end-game?

In reality, it is a big scam to trick the public into thinking AI is dangerous and needs to be regulated. Then the government can limit it so only the preferred companies can continue to develop it.

It's because both frontier companies are worried about plateauing and there's no barrier to competition. They both have upcoming IPOs, too.

4-5 orders of magnitude is huge. Assuming an order of base 10, it's 10000x-100000x. So a call to grep may return in 1s on a typical PC. That means a GPT call takes equivalent energy of 10000-100000 PCs to do the same in 1s. That's a difference that can't be equalized with scaling. It would require a revolutionary breakthrough.

I also don't understand where the idea that frontier models are getting better efficiency comes from. The results are certainly improving, but that comes from feedback and multiplexing requests, which cost more.

As the number of samples approaches infinity, the percentage share of all possible results approaches equality. A uniform random distribution is like the mathematical identity of statistics.

The two articles by the author are focused on the sharpness aspect. What's missing are the dilation/contraction due to beam intensity and resultant gamma. You can take away the scanlines and phosphor filter and still get a more "classic" image with just a modified bilinear filter.

A particularly good example of this are the whites of Mario's and Yoshi's eyes in SMW. They look much better with dilation. (No pun intended)

I kind of like the idea that as the edge of the universe is observed, it expands to fit that observation. "Observed" being in the same sense as the uncertainty principle. But there's also the idea of the universe or space as a kind of mesh that's stretching, not really growing.

When I think about things at this scale, it really makes me feel minuscule.