In the “classic” machine learning paradigm of supervised learning, there’s no role for curiosity. The goal of a supervised learning algorithm is simply to match a set of labels for a provided dataset as closely as possible. The task is clearly defined, and even if the algorithm was capable of wondering about the meaning of the data pouring into it, this wouldn’t help with the learning task at all.
The “Chinese Room Argument” is one of the most famous bits of philosophy among computer scientists. Until recently, I thought the argument went something like this: Imagine a room containing a person with no Chinese language proficiency and a very sophisticated book of rules. Occasionally, someone slides a piece of paper with a sentence in Chinese written on it under the door. The room’s inhabitant (let’s call them Clerk) uses the rulebook to look up each Chinese character and pick out the appropriate characters to form a response, which they slide back under the door. Clerk has no idea what the characters or the sentences they form mean, but with a sufficiently sophisticated rulebook, it would look to outside observers like Clerk was conversant in written Chinese.… Read the rest