A.I. and Deep Learning

The idea of artificial beings that are capable of thought has been around since at least ancient Greece. Much like the eponymous doctor in Mary Shelley’s Frankenstein, humans have postulated for years that if divinity has created sentient life capable of cognition, man may likewise be capable of such acts of creation. The result has been the romanticising of the concept in literature and culture, as science simultaneously pursues its reality.

Both approaches have become more sophisticated over the centuries, with writers and poets broadening the scope of what hypothetical synthetic cognition might be capable of. Likewise, as the progression of technology has led to the development of computer systems, it has finally become possible to create systems that can accomplish tasks previously considered too abstract for non-human minds.

The most significant advancement thus far in the field is that of “deep learning,” a subcategory of machine learning that attempts to mimic the way the human brain learns new concepts. A simple definition of deep learning is when a system or algorithm is given a large dataset, and told to look for patterns, without being programmed how to differentiate between the patterns.

The most convenient example of this is image recognition, and how Google built a computer network that taught itself what a “cat” was. Fed enough YouTube videos, it started to recognize patterns among the images it was presented, and it started to group felines together as a category. It’s an impressive feat, considering there’s a non-trivial amount of variety of characteristics among cats.

None of the data Google fed the algorithm was labeled, meaning this was “unsupervised” deep learning, and the computers didn’t have any base examples to work from first. The alternative is “supervised” deep learning, where the computer is given a number of labeled examples to start it off, teaching it what kind of things classify as “category A” or “category B.” In supervised learning, they’re still not instructed what to look for, just given examples of what qualifies; determining qualifying characteristics is up to the system.

This is very similar to how we teach a child what a cat is. We show them pictures and videos of a cat, tell them what sound they make, and maybe even let them pet a cat (if one is available). Most toddlers, even ones who have never seen a cat in person, can identify them by sight or sound. We give them sufficient data, and they can draw the conclusion on their own. That’s one of the foundational principles of intelligence.