Machine Learning vs. AI: What’s the Difference?

Machine learning & artificial intelligence are used interchangeably, but there are differences between the two. Learn all about machine learning vs. AI here.

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There’s a marvelous scene in the science fiction classic The Matrix in which Morpheus explains the end of the world to Neo. According to Morpheus, the problems began when humans invented artificial intelligence (AI).

Rewatching the movie 20 years after its release, it almost seems quaint.

After all, the potential for AI is huge. A research study showed that AI could outperform clinicians in diagnosing breast cancer. Clinicians have other advantages but machines don’t call in sick or change jobs.

A second phrase often gets used around AI, ‘machine learning. Many use the terms interchangeably but are they the same?

Let’s get into the machine learning vs AI debate.

What is AI?

The term ‘artificial intelligence’ was coined in 1955. Some people refer to things like the Turing Test, named after its creator Alan Turing. If a machine could beat the test, then it was considered to be artificially intelligent.

AI has changed over the years. It’s no longer a high-quality calculator for complicated processing. Instead, AI now emulates decision-making and performing ‘human’ tasks.

At its core, scientists feed data into an AI machine. These machines run algorithms to analyze the data. They can find patterns and make predictions that are beyond human capability.

With AI algorithms, scientists input new data and the machine adjusts what it ‘knows’. That’s even though this new data wasn’t part of what it first learned.

AI isn’t necessarily ‘intelligent’ since the AI isn’t thinking for itself in the way humans do. The AI only makes decisions based on the information it’s given.

That said, artificial intelligence software is the system that powers technology like self-driving cars. It’s all about the absorption and application of data.

Neural Networks

One form of AI is sometimes called a ‘neural network’. You may have seen the memes on social media about using bots to create something.

In one example, someone feeds hundreds of movie scripts into a bot. It then writes a script for a Hallmark Christmas movie. This is a neural network in action.

Elsewhere, Janelle Shane trained a neural network to create knitting patterns as part of her SkyKnit project. Knitting patterns became the training dataset. The SkyKnit AI created new patterns based on what it ‘learned’.

Neural networks are fascinating because they can let computers think the way we do. They do so faster and without human bias.

What is Machine Learning?

Machine learning is a sub-type of AI. Where AI can analyze data, machine learning can literally ‘learn’ from it. How do you teach a machine?

A user can highlight for the machine which data is input data, and which is output data. The dataset runs through the machine, so it learns the difference between input and output data. Not only does it learn the difference, but it also figures out the relationship between them.

The first dataset put through the machine is a training set. In the SkyKnit example, that would be the knitting patterns initially fed into the algorithm.

Next, the user gives the machine brand new data. This is the test dataset and it lets users know how accurate the machine’s learning is.

The more data the machine gets, the more accurate and reliable its results. Once it reaches this stage, it can be used in ‘real-time’. That means users can give it new datasets to analyze. It’s particularly helpful for making predictions.

Machine learning relies on a huge range of algorithms, depending on what the users need the machine to do.

Deep Learning

Now, users can also choose deep learning, a more power-intensive form of machine learning. Deep learning works best on vast datasets, where machine learning can work with smaller datasets.

Deep learning has thousands of uses in many different areas. Netflix uses deep learning to make better recommendations to viewers. However, the cost of training with deep learning is much higher, and smaller enterprises may want to outsource that task.

They also use it behind the scenes to analyze which shows to keep making and which shows to ditch.

Virtual assistants like Google Assistant or Alexa use deep learning to understand your requests. Even social media platforms can use deep learning to spot fake news.

Machine Learning vs AI: The Differences

You must remember that machine learning is part of AI. There are a lot of similarities between the two processes. Many people still use the terms interchangeably.

The best way to tell the difference between the two is that AI is the way machines do things we call ‘intelligent’.

Meanwhile, machine learning is an application of AI. We give machines the data and they learn from it.

AI is divided into two groups – general and applied. Applied AI is the kind of system that would control a self-driving car. It’s the closest to what we would consider ‘intelligence’.

General AIs aren’t as common because they could theoretically perform any task. This is where we find machine learning.

You also need to think about what AI and machine learning are used for. AI uses intelligence to represent the acquisition and application of knowledge. Machine learning just acquires knowledge.

Likewise, AI is used to improve success rates, whereas machine learning is used to improve accuracy.

AI is about decision making, where machine learning works solely on the data provided. Here, machine learning essentially performs one task very well, whereas AI is more flexible in its problem-solving.

The Crossover

Within AI, engineers have developed a new field related to communication, called natural language processing (NLP). Using NLP, machines try to understand how humans communicate and reply to us using human language.

While it’s a field within AI, it depends on machine learning for success. Machine learning lets the AI learn and then understand human nuance. Then it can reply and we can understand them.

You Need Both

Now you know more about the differences in the machine learning vs AI debate. They look very similar on the surface but when you focus on their uses, the differences become clear.

Machine learning is a specific process designed to solve a single problem. AI is a complex system that copies human thinking to perform tasks quickly.

With the advances being made in both areas, who knows where they might take us in a few years’ time?

Fascinated by the future? Why not check out our other technology articles?