Machine learning at a glance

The physical design process (involving humans, decisions, constraints, and the most potent of all: unpredictability) has parallels with the machine learning systems we are building. The decision boundary of a classifier, data constraints, and the uses of randomness to initialize or introduce diversity in models are just three connections we can make. The deeper question is how far can we take this analogy. If we are trying to build artificial intelligence, the question is, "Are we trying to replicate the process of human intelligence, or simply imitate its consequences, that is, make a reasonable decision?" This of course is ripe for vigorous philosophical discussion and, though interesting, is largely irrelevant to the present discussion. The important point, however, is that much can be learned from observing natural systems, such as the brain, and attempting to mimic their actions.

Real human decision making occurs in a wider context of complex brain...