Reinterpreting the Machine

More and more consumer electronics devices, computers, smartphones, etc. now come with additional microprocessors or specific areas within their microprocessor that use machine learning, or, as many people like to call it, artificial intelligence.

I don’t like the term artificial intelligence; it could be because for years, talking about anything related to the possibility of machines developing functionalities remotely reminiscent of human intelligence, be it playing chess, Go or poker, automatically brought to mind the Terminator robot. In short, I believe that intelligence, as such, is something else, so I tend to prefer the term machine learning, which helps me to better understand what the machine is actually doing: applying statistical procedures to deduce rules from a set of labeled data.

In practice, that’s basically what we’re talking about. For years, we’ve regarded computers as boxes full of programs or rules, generated by third parties or by ourselves — if we knew how to program — which we supplied data with in order to obtain results. Programs, as such, were stable: your word processor, your spreadsheet, your presentation program, etc. did not change their way of doing things once you installed them. They simply received your data and processed it methodically. If you always did things a certain way, they didn’t particularly adapt to your way of doing things. The machine was predictable and rigid, we provided the flexibility.

In contrast, with machine learning, we supply the machine with data labeled with their results, and the machine is able to deduce the rules by which the data generated those results. This, developed continuously, allows the machine, in human terminology, to learn — in reality, it is just applying more or less complex statistical rules — and can, for example, adapt to changing patterns of use.

Perhaps the best way to understand this is to try to imagine how our smartphone manages to return to us, if we ask it to, photos from our collection that feature a dog, or a sunset, or a particular person. If we tried to design a system to do something like this using classical programming, it would be extremely complex: a dog can have many, many different aspects and anatomical attributes, which would make the possibility of programming conditionals to identify them a never-ending task. A sunset? The variations are enormous. A person’s face? How many angles, perspectives or gestural differences would have to be taken into account?

And yet our smartphones perform the task remarkably well, simply because we have trained algorithms by showing them a huge number of dogs — or sunsets, or faces — labeled as what they are. From that data set, those algorithms are able, with relatively few errors, to recognize when an unlabeled image contains a particular dog, sunset, or face.

Increasingly, the inflexible, predictable and rigid machines we once knew are becoming more flexible, endowed as they are with potentially much more interesting functionalities. In the evolution of consumer electronics, the incorporation of machine learning offers unprecedented potential, which doesn’t mean that machines will become intelligent, but rather that they will be able to apply statistical procedures to act in different ways in the face of different human behaviors, or different sets of supplied data.

Our smartphone camera no longer simply takes pictures: it is now able to understand which combination of lens, exposure, aperture, etc. it should use to highlight, for example, a portrait, or to blur its background, or for many more features we might find interesting. Soon, we will find more and more functionalities in which our devices, using criteria based on the training of its algorithms, done by the manufacturer or ourselves, will be able to optimize how to assist us.

The devices we knew are changing, they are learning new tricks… and there is nothing sinister about it, nor should we think that they are going to become Terminators with an evil red gleam in their eyes. But we should understand that we are facing a dimensional change, and that those who do not understand it and do not know how to incorporate it, will be relegated to the past and overtaken by a new generation.

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This post was previously published on MEDIUM.COM.

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