“I believe this artificial intelligence is going to be our partner. If we misuse it, it will be a risk. If we use it right, it can be our partner.” — Masayoshi Son
In this article, we’re going to dive even further into the idea of machine learning by differentiating it from related disciplines.
Namely, machine learning is a pretty major thing right now; a lot of people are learning that it’s the next big thing in computing and, as a result, they’re trying to jump on the machine learning train by retaining everything they can about machine learning and its related disciplines.
This is great in and of itself, but at the same time, it can kind of muddy the market; people who are getting into machine learning right now are almost certainly familiar with two other concepts: artificial intelligence and deep learning.
However, since this article is about machine learning and all these concepts are related but not the same, it’s important that we spend some time marking the differences between machine learning and these other disciplines.
First, what is the difference between machine learning and artificial intelligence? This question is extremely simple to answer. Machine learning is to artificial intelligence, as calculus is to mathematics.
Therefore, not all artificial intelligence has to do with machine learning, but all of machine learning has to do with artificial intelligence; much like not all of mathematics is calculus, but all of calculus is most certainly mathematics. So, bearing that in mind, what is the difference?
Well, machine learning is plainly and simply a subset of artificial intelligence. Artificial intelligence comprises many different respects and fields, such as machine learning, natural language processing, pathfinding, and similar concepts.
Anything that has to do with a computer being able to process and make decisions based on given data is, in a way, artificial intelligence.
Machine learning, on the other hand, refers specifically to a subset of techniques, algorithms, and theory that aims to supply the computer with the ability to enhance its ability to recognize things and make decisions.
Machine learning thereby is the subset of artificial intelligence which is focused specifically on the building of computer intelligence.
For example, artificial intelligence may have to do with the component wherein the computer can learn to parse languages and words and derive meaning from it by finding keywords.
However, teaching a computer to learn what the different parts of speech are, or to recognize one language from another, is most definitely an application of machine learning specifically.
So, with all of that said, what then is the difference between machine learning and deep learning? Deep learning, too, has become somewhat of a computer buzzword much like both artificial intelligence and machine learning have.
It’s tossed around all the time, but in it being tossed around, very little meaning is maintained, and very little is explained. But that reassurance does little to actually tell you what deep learning is and how it differs from machine learning.
Deep learning is simply one application of machine learning itself. Deep learning specifically is the use of the concept known as neural networks, whereby computers emulate systems of neurons, similar to those found in the brain, to learn and work.
Neural networks have been around since the early days of artificial intelligence, but they’ve only started to be particularly useful in recent years as computers have become more and more powerful and capable.