Genady

(Playing devil's advocate) If you keep asking the bot the same question it keeps giving different answers. It goes somewhere else every time.

This is how I'd solve it. When three sides of the triangle are known, its area is given by Heron's formula. OTOH, with the semicircle as described in the OP, its area is r(b+c)/2. Equate the two and find r from the equation. Sine of an angle between sides a and b can be found from the equation, Area = ab sinC / 2. The distance from the vertex to the circle center, x = r/sinC.

Four bugs sit in a perfect 10x10 square, ABCD. Simultaneously, they start marching: A toward B, B toward C, C toward D, and D toward A. They march, spiraling, until they meet at the center. What distance does each bug cover?

Why is it a problem?

The first misconception in the OP that I notice is that in fact virtual particles and force carrying particles / bosons are not the same and are not even related in a special way. Any particle, a boson or a fermion can be virtual or not in different particle processes. Generally speaking, virtual particles are particles which participate in a process but do not appear at the process's start and end. They get created, do something, and then annihilated during the process.

We have ice and snow high in the mountains, is it any surprise that we have ... umm ... we have ...?

OK. This is the weight of the bird times distance, small amount of energy compared to the energy that is there in the wind and goes unused.

Yes, I know. They use air flow. But they don't harvest energy from it. This is what I meant.

For the same aerodynamics of air, there is a great variety of wings. Even more if we consider wings of extinct organisms as well. Even more could be but were not realized. Organisms did not utilize, e.g., wind energy until humans invented windmills and wind turbines.

IIUC, the machine learning engine, on which (e.g.) ChatGPT runs, works as follows. During the 'training', it builds a DNN type function, which minimizes a functional. The functional is a measure of distance between the function output and a given target. Then, during the 'run', it applies the function to new inputs. So, the machine learning of (in this case) language is based on matching verbal outputs of the function and the target verbal outputs. I don't think this is similar to how humans learn language. I don't think we learn by trying to match our verbal outputs to supplied verbal targets.

Why would anybody spend any effort on debunking your theory?

According to this article, it already does: How AI Knows Things No One Told It - Scientific American

But if components of an equation merge, there is no equation. Any equation requires at least two components, like A = B. If they all merge, you are left with A, which is not an equation.

IOW, spacetime curvature merges the components of EFE? But spacetime curvature is one of these components. Are you saying that one component merges all other components into itself?

Being biased is very human-like.

What are the Einstein field equation parameters?

We all experienced situations when we know what we want to say but can't find the right words. Or when we say something and immediately know that it does not express correctly what we wanted to convey. The concepts and knowledge which are labeled by a language are not themselves purely linguistic but are connected to other sensory / motor / affective areas of our experience.

I think that our use of language is determined not only by the intra-language connections, but also by connections between the language and our sensory / motor / affective experiences. IOW, intra-language connections themselves don't have enough information to generate verbal responses indistinguishable from humans. The precedents should also include sensory / motor / affective precedents related to the linguistic experiences.

Yes. (You have to.)

Yes. As I said in the OP,

Like in the picture in OP.

I mean performing one measurement only. IOW, you are allowed to use the weighing scale only once. BTW, we don't know anything about the numbers of coins in the bags. These numbers may be different. We only know that there are enough coins in each bag to solve the problem. This means that weighing bags doesn't help. We need to weigh coins. You are allowed to take coins out of the bags for the weighing.

This is a testable prediction.