5,298 karma · joined October 20, 2023
Why would they as long as they find a renter? The market always charges the marginal cost.
“are” or “could be”?
The text on the page sounds really AI written so I can see why people think it’s slop.
> It is disputed whether the name Wi-Fi is short-form for 'Wireless Fidelity',[34] although the Wi-Fi Alliance did use the advertising slogan "The Standard for Wireless Fidelity" for a short time after the brand name was created,[31][35] referenced "the Wi-Fi (Wireless Fidelity) logo" in a white paper[33] and the Wi-Fi Alliance was also called the "Wireless Fidelity Alliance Inc." in some publications.[36] IEEE, a separate but related organization, has stated "WiFi is a short name for Wireless Fidelity" on their website.[37][38] The name Wi-Fi was partly chosen because it sounds similar to Hi-Fi, which consumers take to mean high fidelity or high quality. Interbrand hoped consumers would find the name catchy, and that they would assume this wireless protocol has high fidelity because of its name.[39]
So it’s not really safe to say it’s not meant to mean wireless fidelity, in fact it sounds pretty likely.
Not really true, most instructions set have instructions specifically to implement functions as found in normal programming languages. x86 has CALL and RET for example.
https://en.wikipedia.org/wiki/X86_calling_conventions
Of course the compiler can stil optimize by inlining etc., but functions still mostly exist at the assembly level.
> What I found is that this is common in embedded and kernel code as a halt-on-error pattern. When a fatal error occurs and there’s no operating system to exit to, you simply stop:
I was talking about humans vs. cars as an analogy to you comparing GPUs and cars.
Nobody is taxing humans the way cars are taxed, so why should the computing speed of a GPU be compared to that of a human?
> A bridge can hold ten thousand humans or thousand trucks. You can argue that a "human" may not weigh 100 kgs or a truck may not weigh exactly 1 ton. That's fine. It is a rough approximate to equalize unequal entities.
And you do not think that comparing weights to measure bridge load makes a lot more sense than comparing FLOPS to determine learning of GPUs vs humans?
EDIT: Maybe let's just go back to the original point
> We need to define machine in terms of "human-power"... much the same as how we already define automobiles via "horse-power". A single NVIDIA GeForce RTX 3090 chip, for example, delivers roughly 35.58 teraflops of standard computing power (via 10,496 CUDA cores). That means 35.58 trillion calculations every second. In comparison, a mathematically trained human being, taking their time to solve a complex, multi-digit decimal division problem by hand takes roughly 100 to 120 seconds. That gives the human 0.01 flops. To match RTX 3090, you would need 3.56 quadrillion people working/learning in perfect sync. We can use a calculation similar to this to derive metrics on how much is being stolen for "learning/training" these models. The loot can be quantified.
So you want to compare the learning rate of a GPU to that of a human by comparing their respective FLOPS. Why would FLOPS be a valid proxy for learning ability in humans just because that works out in GPUs, if the way they learn is fundamentally different?
Is the effect of someone reading a copyrighted book dependent on how fast they are at doing math in their head?