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Fix readme typo #2

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Fix readme typo #2

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Expand Up @@ -190,7 +190,7 @@ You may have heard of fancy machine learning algorithms based on gradients - but

Most of the points that we can randomly pick in this space will contain total garbage layouts. Keys that should be close together have very little chance of actually being placed reasonably. That roughly matches the space analogy. If you pick a random point anywhere in the universe, you'll likely end up somewhere far from any galaxies. The fancy term for this search strategy is [random search](https://en.wikipedia.org/wiki/Random_search). Using this approach would give us pretty awful results though. The space is really large. The chance that we'll hit the deepest gravity well in the universe by pure luck are pretty slim.

Another approach is to focus our search near some good points. Like picking mushrooms in a forest. You find a mushroom 🍄‍🟫? That means there sholud be more nearby. So we can check the neighbourhood of good mushrooms (ekhm... I mean points) more dilligently. We start picking random points throughout the universe and then we progressively start focusing on the nice locations. The fancy term for this approach is [simulated annealing](https://en.wikipedia.org/wiki/Simulated_annealing). Doing this very slowly, mathematically speaking should let us find the optimal point. Unfortunately mathematicial proofs don't translate well into practical algorithms. This is one of those situations. The "very slowly" in mathematics translates to "forever" in practice.
Another approach is to focus our search near some good points. Like picking mushrooms in a forest. You find a mushroom 🍄‍🟫? That means there should be more nearby. So we can check the neighbourhood of good mushrooms (ekhm... I mean points) more dilligently. We start picking random points throughout the universe and then we progressively start focusing on the nice locations. The fancy term for this approach is [simulated annealing](https://en.wikipedia.org/wiki/Simulated_annealing). Doing this very slowly, mathematically speaking should let us find the optimal point. Unfortunately mathematicial proofs don't translate well into practical algorithms. This is one of those situations. The "very slowly" in mathematics translates to "forever" in practice.

For practical algorithms we'll have to learn from the force of nature that defines what it even means to be practical - from the _life_ itself.

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