The Master Algorithm summary

Have you ever been frustrated by recipes with imprecise instructions, like, “cook at medium heat for 15-20 minutes”? If so, you might be someone who prefers a good algorithm.

Unlike recipes, algorithms are sequences of precise instructions that produce the same result every time.

Though you might not be aware of their presence, algorithms are used everywhere. They schedule flights, route the packages you send and make sure factories run smoothly.

These standard algorithms are designed to accept information as an input, then perform a task and produce an output.

Let’s say an algorithm’s task is to give directions. When you input two points, the output would then be the shortest route between these two points.

But machine learning, or ML, algorithms are one step more abstract: they are algorithms that output other algorithms! Given lots of examples of input-output pairs to learn from, they find an algorithm that seems to turn the inputs into the outputs.

This comes in handy for finding algorithms for tasks that human programmers can’t precisely describe, such as reading someone’s handwriting. Like riding a bike, deciphering handwriting is something we do unconsciously. We would have trouble putting our process into words, let alone into an algorithm.

Thanks to machine learning, we don’t have to. We just give a machine learning algorithm lots of examples of handwritten text as input, and the meaning of the text as the desired output. The result will be an algorithm that can transform one into the other.

Once learned, we can then use that algorithm whenever we want to automatically decipher handwriting. And, indeed, that’s how the post office is able to read the zip code you write down on your packages.

What’s great is that ML algorithms like this one can be used for many different tasks, and solving emergent problems is only a matter of collecting enough data.

This means that the initial underlying algorithm is often the same and requires no adjustments in order to solve seemingly unrelated problems.

For example, you might think that making a medical diagnosis, filtering spam from your email and figuring out the best chess move might all need completely different algorithms. But, actually, with one ML algorithm and the right kind of data, you can solve all these problems.