If the moon landing was indeed infeasible from a technical perspective at that time, those people would know, that their designs were inadequate or flawed. The "some people might think they do legit work" argument doesn't really hold, because they would either know it was an impossible feat, or it would possible and no need to fake the landing.
If I understand your question correctly, this is exactly what Bayes' Theorem deals with. Namely that B must be true within some probability given that B is true represented as P(A | B).
Let's consider a simple image classifier: 64x64 grayscale input, 10x1 output that detects 10 classes of images. The model: y=tanh(Ax+b). You'd probably say that there is no way this will work because this model is too simple. But can you explain why this won't work? Can you tell what the maximum accuracy this model can reach? What kind of datasets this model would work better on?
[Edit] Whether this model works is question. Sure, it can't recognize dogs or cats, but what if the dataset is 0-9 digits? Now it suddenly works, right? And works really well. But what's changed? Can we describe in mathematical terms what makes the 0-9 dataset so special? It'll probably work with A-J letters too, but what about hieroglyphs?
The math I'm looking for would tell that E(T) is the error and on such model and such dataset, E(T)=exp(-T^2)+O(exp(-T^3)), according to such and such theorem; and according to another theorem, if the dataset is isomorphic to that manifold, E(T) can be improved to O(exp(-3T^2)).
I think you're asking for the holy grail here. Everyone would love to have such a thing but nobody thinks it's likely to be possible.
So they settle for much smaller targets. Either of understanding how much simpler systems work. Or of trying to understand a little bit the effect of tweaking something in some more complicated model.
Perhaps you should think of these two approaches as analogous to doing simple chemistry (what shape is a sugar molecule? A DNA molecule?) vs trying out drugs (if you eat the bark of this tree, you don't get malaria! Let's refine that stuff). Both can be useful, but they are very far from a unified theory of how your body works.
ML is more like alchemy, I'd say: mixing components using intuition and experience, but without understanding what these components really are and why they work. In this analogy, AI is the recipe to make gold and the ML alchemists haven't invented nuclear physics yet.
But now we know there was physics at the bottom of alchemy. Whereas demonology at best leads you to psychiatry, and we still don't have simple models of what works there. Nor much hope of finding them. Thinking is a messy business.
My guess is that what I'm asking for isn't that complex and could be done by a few serious mathematicians in a few years. The dynamics of tanh(Ax+b) is hardly more complex than Naiver-Stokes equation or the modern topology theory.
tanh(Ax+b) is simple, but the dataset it's supposed to work on is not easily summarised. I think that's the huge difference. The guys doing "sugar molecule" studies make progress by taking much simpler datasets, like random points.
Naiver-Stokes is much simpler because it operates by itself. Of course turbulence is hard but even there we usually care about its coarse features, we'd be content to throw away almost all the information provided the calculation of the wing's lift works out OK.
From my understanding, the high valuation of Saudi Aramco does not seem to stem from them being the best in to industry for processing oil, but rather, from their access to the richest and cheapest source of oil. This license is their source of making huge profits - i.e. the difference between the cost price versus the market price.
To the best of my understanding, Saudi Aramco has no ownership of the oil fields but merely manages them on behalf of KSA. So is there anything impeding KSA from simply selling oil field access to someone else than Saudi Aramco after the IPO, and in effect, eliminating Saudi Aramco's source of profits?
With a small float and Saudi state owning the other 98% it means that high price can be maintained with little inflow, but global passive equity investors are mandated (I think?) to hold a big chunk of this overpriced equity due to the huge market cap. Anyone know if that is correct?
According to [1] you can have float-adjusted or market-cap weighted indices.
> An example of a company in which float-adjustment comes into play is Amazon (AMZN). The online retail giant's overall market cap is estimated at around $130 billion. However, only about two thirds of its shares are publicly traded. The non-publicly traded shares, controlled by insiders such as founder and CEO Jeff Bezos, would not be included when determining a company's weight in a float-adjusted index. Incidentally, a company's full market cap, including both its float and non-float shares, is used to determine whether it belongs in the index.
> Why would you sell your goose that lays golden eggs?
> Even if you think that the goose might be getting older and less fertile, it doesn't make sense.
Well, if the goose is currently valued at the net present value of the original income stream, it does make sense to sell if its income stream is going to falter. That's a simple case of selling something for more money than it's worth.
