I said before that no one has yet patented a Meaning Machine. While that's true in the abstract, I want to talk about the two most common ways to hack around the problem. First is labor and second is scale.
Meaning is the "hard problem" of computation, at least today. How do we know that data means something as opposed to something else? What's the difference between noise and signal? Is artificial intelligence (AI) able to discern meaning? And, perhaps more esoterically if not also pedantically, is meaning an analog technology or a digital technology? (For the final question, I take meaning formally as an analog technology, in that meaning entails a kind of Gestalt synthesis of complex arrangements of terms; yet practically speaking meaning is always the result of an interaction between the analog and the digital, and thus cannot be reduced to one or the other.)
Today there are two basic solutions to the "hard problem," the problem of meaning. The first solution is to outsource the problem to humans, effectively to make humans shoulder the burden of the Meaning Machine. To the extent that significance is measurable, it's because a human put it there and marked it as measurable. In other words, if you find meaning, it's the result of human labor. Continue reading