Tag Archives: behavioralism

The end of code.

 

This week WIRED Magazine released their June issue announcing the end of code. That would mean that the ability to write code, as is so cherished in the job world right now, is on the way out. They attribute this tectonic shift to Artificial Intelligence, machine learning, neural networks and the like. In the future (which is taking place now) we won’t have to write code to tell computers what to do, we will just have to teach them. I have been over this before through a number of previous writings. An example: Facebook uses a form of machine learning by collecting data from millions of pictures that are posted on the social network. When someone loads a group photo and identifies the people in the shot, Facebook’s AI remembers it by logging the prime coordinates on a human face and attributing them to that name (aka facial recognition). If the same coordinates show up again in another post, Facebook identifies it as you. People load the data (on a massive scale), and the machine learns. By naming the person or persons in the photo, you have taught the machine.

The WIRED article makes some interesting connections about the evolution of our thinking concerning the mind, about learning, and how we have taken a circular route in our reasoning. In essence, the mind was once considered a black box; there was no way to figure it out, but you could condition responses, a la Pavlov’s Dog. That logic changes with cognitive science which is the idea that the brain is more like a computer. The computing analogy caught on, and researchers began to see the whole idea of thought, memory, and thinking as stuff you could code, or hack, just like a computer. Indeed, it is this reasoning that has led to the notion that DNA is, in fact, codable, hence splicing through Crispr. If it’s all just code, we can make anything. That was the thinking. Now there is machine learning and neural networks. You still code, but only to set up the structure by which the “thing” learns, but after that, it’s on its own. The result is fractal and not always predictable. You can’t go back in and hack the way it is learning because it has started to generate a private math—and we can’t make sense of it. In other words, it is a black box. We have, in effect, stymied ourselves.

There is an upside. To train a computer you used to have to learn how to code. Now you just teach it by showing or giving it repetitive information, something anyone can do, though, at this point, some do it better than others.

Always the troubleshooter, I wonder what happens when we—mystified at a “conclusion” or decision arrived at by the machine—can’t figure out how to make it stop arriving at that conclusion. You can do the math.

Do we just turn it off?

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