Source: Deep Learning on Medium
In October 2018, Victor Basu wrote a post called “Difference between Machine Learning, Artificial Intelligence, and Deep Learning”. Although I appreciate the effort to make these concepts clear to the public, Victor’s post makes a number of claims I simply disagree with. As I think Artificial Intelligence is an important topic, I decided to write this response.
In this post, I’ll take a few quotes from Victor’s post that I think need responding. In the process, I’ll clear up some key concepts.
“Artificial intelligence is subdivided into machine learning and deep learning”
Well, maybe you could say it like this, but it’s a highly confusing sentence considering what’s actually the case. First of all, Deep Learning is a form of Machine Learning. Victor talks about both fields being separate, but Machine Learning is a broad subfield within Artificial Intelligence, and Deep Learning is just a subfield within Machine Learning. To define things a little more, Machine Learning is the collection of algorithms that computers can use to perform tasks they haven’t been explicitly instructed to do. This can be done by giving examples to the computer (although there are other possibilities): for example, a computer can learn to recognize cats in images if it’s given the right algorithm and a bunch of examples of cats in images (and perhaps a bunch of images without cats to see the difference). A popular approach here is to use a Neural Network, which is basically a mathematical function of which the parameters are determined by considering a lot of data (examples). Deep Learning happens when one uses a Neural Network of a particular minimum size.
Second of all, the field of Artificial Intelligence contains more than Machine Learning: it also, for example, contains the example of Deep Blue, the famous chess computer that defeated Garry Kasparov. Deep Blue didn’t use Machine Learning; it used a predefined search procedure to find good moves to play. Machine Learning, and in particular Deep Learning, is a hot topic in Artificial Intelligence because of its amazing results the past years, but it’s certainly not the only subfield within Artificial Intelligence. Note that Victor is not necessarily claiming it is the only subfield; his quoted sentence does, however, slightly suggest this is the case.
“… Artificial Intelligence revolves around the concept that a model is built to perform all kinds of tasks irrespective of situations.”
Well, that would be Artificial General Intelligence, an Artificial Intelligence that can successfully perform roughly all tasks humans can. This is a major goal within Artificial Intelligence since the field started in the 1950’s. However, Artificial Intelligence also encompasses Narrow Artificial Intelligence, designed for a specific task.
“The artificial intelligent model means a model that could work, think and respond just like a human brain.”
Well, that’s interesting. Again, this would be Artificial General Intelligence, not Artificial Intelligence in general (pun intended). An Artifical General Intelligence performs as well as or better than humans in (most of) their intellectual tasks, but this does not mean it thinks just like a human. Depending on the design of the intelligence, it could be a very alien intelligence, or it could indeed be very human-like. It depends on the techniques being used, and on how we define human-like thinking.