> Except in your own examples it can easily be shown that it is not handled differently.

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?