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What's the Difference Between AI, Machine Learning, and Deep Learning?
AI means getting a computer to mimic human behavior in some way.
Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications.
Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems.
What Is AI?
Artificial intelligence as an academic discipline was founded in 1956. The goal then, as now, was to get computers to perform tasks regarded as uniquely human: things that required intelligence. Initially, researchers worked on problems like playing checkers and solving logic problems.
If you looked at the output of one of those checkers playing programs you could see some form of “artificial intelligence” behind those moves, particularly when the computer beat you. Early successes caused the first researchers to exhibit almost boundless enthusiasm for the possibilities of AI, matched only by the extent to which they misjudged just how hard some problems were.
Artificial intelligence, then, refers to the output of a computer. The computer is doing something intelligent, so it’s exhibiting intelligence that is artificial.
The term AI doesn’t say anything about how those problems are solved. There are many different techniques including rule-based or expert systems. And one category of techniques started becoming more widely used in the 1980s: machine learning.
What Is Machine Learning?
The reason that those early researchers found some problems to be much harder is that those problems simply weren't amenable to the early techniques used for AI. Hard-coded algorithms or fixed, rule-based systems just didn’t work very well for things like image recognition or extracting meaning from text.
The solution turned out to be not just mimicking human behavior (AI) but mimicking how humans learn.
Think about how you learned to read. You didn’t sit down and learn spelling and grammar before picking up your first book. You read simple books, graduating to more complex ones over time. You actually learned the rules (and exceptions) of spelling and grammar from your reading. Put another way, you processed a lot of data and learned from it.
That’s exactly the idea with machine learning. Feed an algorithm (as opposed to your brain) a lot of data and let it figure things out. Feed an algorithm a lot of data on financial transactions, tell it which ones are fraudulent, and let it work out what indicates fraud so it can predict fraud in the future. Or feed it information about your customer base and let it figure out how best to segment them. Find out more about machine learning techniques here.
As these algorithms developed, they could tackle many problems. But some things that humans found easy (like speech or handwriting recognition) were still hard for machines. However, if machine learning is about mimicking how humans learn, why not go all the way and try to mimic the human brain? That’s the idea behind neural networks.
The idea of using artificial neurons (neurons, connected by synapses, are the major elements in your brain) had been around for a while. And neural networks simulated in software started being used for certain problems. They showed a lot of promise and could solve some complex problems that other algorithms couldn’t tackle.
But machine learning still got stuck on many things that elementary school children tackled with ease: how many dogs are in this picture or are they really wolves? Walk over there and bring me the ripe banana. What made this character in the book cry so much?
It turned out that the problem was not with the concept of machine learning. Or even with the idea of mimicking the human brain. It was just that simple neural networks with 100s or even 1000s of neurons, connected in a relatively simple manner, just couldn’t duplicate what the human brain could do. It shouldn't be a surprise if you think about it; human brains have around 86 billion neurons and very complex interconnectivity.
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