This capability translates into tangible value in an uncomplicated manner. The predictions drive millions of operational decisions. For example, by predicting which customers are most likely to cancel, a company can provide those customers incentives to stick around. And by predicting which credit card transactions are fraudulent, a card processor can disallow them.
. In contrast, ML projects that keep their concrete operational objective front and center stand a good chance of achieving that objective.–Devin Coldewey, The problem is with the word “intelligence” itself. When used to describe a machine, it’s relentlessly nebulous. That’s bad news if AI is meant to be a legitimate field. Engineering can’t pursue an imprecise goal. If you can’t define it, you can’t build it. To develop an apparatus, you must be able to measure how good it is — how well it performs and how close you are to the goal — so that you know you’re making progress and so that you ultimately know when you’ve succeeded in developing it.
What if we define AI by what it’s capable of? For example, if we define AI as software that can perform a task so difficult that it traditionally requires a human, such as driving a car, mastering chess, or recognizing human faces. It turns out that this definition doesn’t work either because, once a computer can do something, we tend to trivialized it. After all, computers can manage only mechanical tasks that are well-understood and well-specified.
AGI may set a clear-cut objective, but it’s out of this world — as unwieldy an ambition as there can be. Nobody knows if and when it could be achieved.