2 + 2 can be viewed as a computation that happens in nature and our brain all the time.
But the real magic doesn't lie in the pure computation. Our brain understands what that 2 means in the context of all the knowledge of the entire Universe. 2 cows, 2 sheeps, 2 planets ?
Understanding context is what we are really good at, as said in the article "prior" knowledge. So its not surprising that a cheap calculator can outsmart me in computation when it doesn't actually compute the context of its computation, which is sort if cheating.
There's also some evidence that the "efficiency" of the more powerful parts of animal brains (including humans) simply comes from a sort of n-dimensional conditional railroading, which would work like this if my understanding is correct: if you map (B/W) visual inputs to XY axes of a slice of neurons, then when a line is formed, this fires up a line of neurons, which allows a direct connection between the Z±1 neurons of the first and last neurons on the mapped line, so this connection becomes the line. These Z±1 neurons have pathways to various other nearby other potential Z±1 neurons; a connection lighting up between Z±1 neurons that don't have a straightline connection over the XY map could detect different shapes. Then the Z±1 layers and their connections could be creating highway maps between both Z±2 neurons and diagonally-offset neurons that don't "fit in the grid" we're imagining here (remember, we're in 3D and neurons don't form a homogeneous grid, so this is all abstraction anyway).
These second-level connections, arguably identifying something like shapes, have connections in such a way that the next level up detects something else, etc. (insert magic we don't understand), until the detection results in recognizing a specific object, while the side-channel connections we haven't mentioned yet but kept occurring at every level connect to a different side-channel ZY map for, say, the position of the object (with its own layers of processing that eventually connect to the object concept neuron so that the "position" is associated with the recognized object), and/or perhaps other side-channel maps or parallel XY mappings for various other things. And of course, a lot of these things connect to completely different parts that we ignore here, and a lot of the information indirectly makes its way to the conscious mind in this way; the brain doesn't compile a complete vision report and submit it to the conscious mind in one download, it's all using the same hardware.
I think that will take us 500 years, give or take a few.
You could say the same about every tool that we use, which we don't totally understand. It just needs to work.
Then there's also the problem of your estimate. 500 years is a rather exaggerated timeframe. Twenty years ago I could've had the most prominent field experts tell me humans would never in the next three hundred years figure out biology even for the simplest of living organisms, because they were simply too irreducibly complex for that. And then a few years ago we simulated an entire worm's nervous system and gave it enough of a body for it to use, move around, and receive input from its entirely-fictional environment. Now we've got people working on doing the same thing with a cat brain. We're nearing breakthroughs in creating fully-synthetic, fully-functional animal organs that you can essentially real-life drag'n'drop to replace a failing natural organ without complications.
Knowing the above, are you really sure it's not 15 rather than 500 years?
[1]. (which is about as meaningful as saying we understand "weather", which is really a huge messy amalgam of many completely unrelated things ranging from fluidic motion and thermodynamics all the way to plate tectonics and even anthropology, after a fashion)
We are 'slow' only when we formalize the problem.
Probably not that many. Let's say you create a water-skipping robot with two different AI programs in it and a shaky, not very precise arm (but still as good as a human arm in terms of specs).
One is custom-made to the physics of the problem, and will calculate the angles and the forces and the pressures and the candy tasty physekz all over, and then decide on a particular motion of the robot arm, and the rock will skip. It'll take a lot of computing power, but it'll work.
The other just has a goal, to see the rock skip, and tries things at semi-random (though it has the general knowledge that it can be done and that it has to throw the rock towards the water in a particular way for it to happen and that it can control the outcome), and tries to figure out patterns between what it did and what happened as a result. Eventually, it has a particular tactic, a certain set of instructions, which could be "down down down the left thing and up up right down down the right thing and the up thing does down down up push push down push twist-force-2 at the same time", an entirely not at all complicated set of instructions with very little computation. This signal is sent through other nodes that might not have the perfect signal number, and eventually this outputs to the arm's motors... which nevertheless manage to make the rock skip, because the motion here is almost the same as the motion of the first AI, yet this one was obtained by eliminating the ones that didn't result in the rock skipping.
TL;DR: You don't do that many calculations on the spot. The "maths" in most situations like this is done by elimination throughout all the attempts you've made in your life to control a throw. Subsequent throws once you're already a practiced rock-skipper just involve firing "the same neurons as usual" which send "the same signals as usual" to your muscles, which result in the same skipping as usual, with very little math. If you already know that 2x^5 + 20 = 110 implies x = 45 from doing the same calculation twenty times in the past hour, your brain isn't performing "calculations" anymore, it's just repeating a pattern that's already there in your brain like a table lookup.