Artificial Intelligence, Machine Learning, and Deep Learning: What’s the Difference?

Artificial intelligence. Machine learning. Deep learning. All buzzwords you’ve no doubt heard or read about (especially if you’re in the tech industry). And they’re often used interchangeably. So what’s the difference? Or do they all refer to the same concept? Let’s talk about the differences between artificial intelligence, machine learning, and deep learning. And see how it plays into Business Intelligence. Artificial intelligence is an umbrella term. Under that umbrella is machine learning. It’s a subset of artificial intelligence. And deep learning? It’s a subset of machine learning. Think of them like those Russian dolls, with each one containing a smaller one.

AI contains machine learning which contains deep learning. And many people think they have a general understanding of what AI is. But American AI researched Eliezer Yudkowsky said that “by far, the greatest danger of artificial intelligence is that people conclude too early that they understand it.”

So let’s start with the biggest doll.

Artificial Intelligence

Artificial intelligence is a computer system that is able to perform tasks that normally require human intelligence, like visual perception, speech, recognition, decision-making, and translation between languages. It’s a machine that imitates human intelligence. And it’s been around probably longer than you think.

Essentially, artificial intelligence is a machine that has been programmed (by a human) with a set of rules. Those rules can vary wildly depending on how general or narrow the objective of the machine is. The AI takes information in, runs it through the set of rules, and gives an answer based on those pre-programmed rules.

At least, that’s what AI used to be. Critics often hesitated to call it “intelligence.” And rightfully so. It could imitate intelligence in the same way a still-life painting imitates a bowl of fruit. It was done by a human hand. True intelligence implies learning. The bowl of fruit needs to paint itself.

Machine Learning

Machine learning is a subset of artificial intelligence. The above definition still applies to machine learning, with one thing added. Machine learning is AI that can modify itself when exposed to more data. It doesn’t require a human to make changes to its algorithm.

Machine learning takes its set of rules – its algorithm – and optimizes it to either minimize error or maximize the likelihood of being correct. It essentially guesses and guesses and guesses while contrasting the wrong answers with the “truth,” modifying its algorithm accordingly.

You may remember in 1997 when IBM’s Deep Blue computer beat the world champion of chess. That may have been the point when most of the world was introduced to what machine learning was, and what it was capable of. During the 1990s, a lot of the work on machine learning shifted from a knowledge-driven approach to and more data-driven approach.

Deep Learning

Just 20 years after IBM’s Deep Blue was introduced to the world, Google’s AlphaZero computer program was able to beat the world’s best chess program, Stockfish 8. It was only given the rules of chess, and within four hours, AlphaZero became the top player in the world.

Deep learning is a subset of machine learning. The term deep is a technical term, referring to deep artificial neural networks. These are networks that are inspired by, but not necessarily identical to, biological neural networks. And how deep it is refers to how many hidden layers it has. The more complex the data, the more layers it needs. So the difference between machine learning and deep learning is essentially just the manner in which they both learn.

Deep learning requires large quantities of data in order to be trained. And when it receives new information, it tries to compare it to a known item before making sense of it. It does this by being exposed to millions of data points. For example, Google Brain learned to recognize cats after being shown over ten million pictures of cats.

How Business Intelligence Fits In

Business Intelligence has proven to be an effective and successful tool for all kinds of organizations. It puts data in the hands of users for them to interact with and explore. And if you know how to interpret that data in the context of your business, you can turn it into actionable insights that can give your business a competitive edge.

But we’re humans. We like automation. And if we can remove the human-element from insight discovery, then we can remove human bias. The true root of an issue could be uncovered with ease, rather than searching reports, or going on “just a hunch.” This is where machine learning comes in.

Machine learning combined with business intelligence can help extract meaning from complex data. It can detect trends and identify patterns too complex for humans to notice. Let’s look at two examples of how machine learning can impact Business Intelligence.

A User Interface that speaks to you

Deep learning could allow BI solutions to interpret user language via voice or chat. For example, a user could ask “what’s revenue per customer this month compared to last month?” and the BI could answer back, like how iPhone users talk to Siri. This would significantly cut down on the time it takes to search for the answer yourself.

Advanced Data Sharing

Certain algorithms could help identify reports and data that users might be interested in. Just like Amazon gives you recommendations based on what other user’s bought, your BI platform could recommend dashboard features that other users have success with, allowing more opportunities for discovery.

The possibilities are endless with artificial intelligence and business intelligence. Machine learning will allow companies to stay ahead of the massive amount of data they have to analyze. And deep learning will allow them to analyze more complex data. And it won’t be limited to numbers. Suddenly, photos, images, and even the emotion behind language on social media platforms will be available to provide rich insights. The data that a company captures can become much more holistic.