AI vs. machine learning: What's the difference?

When you're asked to evaluate the potential of AI or ML to solve your organization's problems, you'd better understand the distinctions between the two
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Artificial intelligence and machine learning get lumped together so often these days that it’d be easy for people to mistake them as synonymous. That’s not quite accurate, though: They’re most certainly connected but not actually interchangeable.

“Artificial intelligence and machine learning are closely related, so it’s no surprise that the terms are used loosely and interchangeably,” says Bill Brock, VP of engineering at Very.

If you’re not using AI or ML yet, you soon will be evaluating its potential for your organization.

AI as a workload is going to become the primary driver for IT strategy,” Daniel Riek, senior director, AI, Office of the CTO, Red Hat, recently told us. “Artificial intelligence represents a transformational development for the IT industry: Customers across all verticals are increasingly focusing on intelligent applications to enable their business with AI. This applies to any workflow implemented in software – not only across the traditional business side of enterprises, but also in research, production processes, and increasingly the products themselves. The improved scale of automation achievable with AI will quickly become critical for a company’s competitiveness building and will make AI a strategy-defining technology.”

Advancements in natural language processing and other AI-enabled capabilities help organizations rethink customer service chat and analyze large pools of unstructured data. That will enable more predictive analytics, drive increased efficiency, and enhance decision-making.

[ Is RPA a form of AI? Learn the differences: How to explain Robotic Process Automation (RPA) in plain English. ]

So what’s the difference between AI and ML? Let’s start by defining the terms.

What does AI mean? 

“AI, simply stated, is the concept of machines being able to perform tasks that seemingly require human intelligence,” Brock says. “This involves giving computers access to a trove of data and letting them learn for themselves.”

Machine learning is a specific application or discipline of AI – but not the only one. In machine learning, Brock explains, “algorithms are fed data and asked to process it without specific programming. Machine learning algorithms, like humans, learn from their errors to improve performance.”

As a starting point for distinguishing AI and machine learning, it’s helpful to think of AI as the higher-level or umbrella category that encompasses multiple specific technologies or disciplines, and machine learning is one of them.

“AI includes various fields of study including ML, NLP (natural language processing), voice/audio recognition, computer vision/image recognition, search, routing, autonomous robots, autonomous transport, [and other disciplines,]” says Mahi de Silva, CEO and co-founder of

Speaking of umbrellas, Michael McCourt, research engineer at SigOpt, offers a distinction-by-comparison for a rainy day: “Machine learning is like a spoke running out of the artificial intelligence umbrella, with a much more specific definition.”

Let’s back up for a second: McCourt notes that AI by definition is very broad – it’s the umbrella – so much so that if you ask a group of ten people to give their definition, you’ll likely get ten different answers. “Artificial intelligence is an umbrella term without a concrete definition, as it encompasses all mechanical, robotic, and automotive tasks that emulate human capabilities,” McCourt says.

“Ten years ago, artificial intelligence meant being able to classify images.”

Moreover, AI’s definition has changed, and it will continue to change over time: “Twenty years ago, tools like spellcheck were considered artificial intelligence,” McCourt notes. “Ten years ago, artificial intelligence meant being able to classify images.”

What does machine learning mean?

While machine learning technologies and uses might evolve, the core definition is much more concrete and specific.

“Machine learning models generate findings based on stored data sets and queries for the purpose of learning a specific pattern,” McCourt says. “If the answer is not previously stored, machine learning analyzes the environment to present its best guess as to what the correct response might be.”

Tom Wilde, CEO at Indico Data Solutions, points out that there’s a very current reason that AI and machine learning get used and confused in tandem.

“ML can be considered as the current ‘state of the art’ of AI.”

“The reason for confusion is understandable: ML can be considered as the current ‘state of the art’ of AI,” Wilde says. Spell-check aside, he adds, machine learning is one of the oldest and best-established AI disciplines. It’s also the one bearing the most current fruit in terms of enterprise use cases.

Understanding the difference between AI and ML isn’t just a matter of clarifying terms or relieving annoyance with non-technical folks who just don’t get it. Rather, it’s table-stakes for success with AI projects.

“It’s important to distinguish between AI and machine learning, as this is critical to successfully designing, building, developing, and maintaining an application or platform,” Brock says.

That’s true for your in-house knowledge and AI skills development; it’s also true for evaluating and selecting the right vendors.

Remember when every product suddenly had the word “cloud” added to its name? You may see some of that with AI and ML, too.

Kevin Casey writes about technology and business for a variety of publications. He won an Azbee Award, given by the American Society of Business Publication Editors, for his story, "Are You Too Old For IT?" He's a former community choice honoree in the Small Business Influencer Awards.


While I can appreciate some of the benefits of AI and ML? I feel we have a long way to go towards ensuring the safety of humans before this is adopted in a wide-spread, dare I say, global scale. And don't get me wrong, I'm all FOR AI and ML when it comes to things like logistics for companies and the transport of their goods, and I think it would do well as a security device/appliance that just sits on the edge of the network and LITERALLY asks people for ID before allowing passage onto the domain. But there are things I'm never going to be comfortable with AI/ML handling....things like my personal safety when it comes to travel. Sorry, I know there are a lot of driver-less car enthusiasts out there, but I prefer the fallible and most loved version...of driving myself and my son around wherever we need to go. Yeah...its safe, and there are Uber and Lyft cars that are driverless (at least there are for Uber...not sure about Lyft!) But there was a report of someone getting run over by a driver-less car with the driver IN the vehicle. Imagine what would be the fallout had there been no one in it! And to magnify this scenario?...what happens when an 18 wheeled- truck goes "rogue" and stops responding to its program?...or what about pilot-less planes?.....Yeah I know it sounds science fiction and all, but we already HAVE pilotless drones, so whats to stop some odd ball company from being the first to market there as well? (Just know that I will NEVER get on one of those!.....Ever.) But the progression to more intelligent AI is definitely a wanted thing where it needs to be (cancer research, massive data collection analysis, space exploration...and the more mundane things like monitoring traffic, or keeping a constant eye on schoolgroud perimeters for "abnormal" behavior from either student or staff. (And why AREN'T we working on that kind of tech?....aren't the children our most IMPORTANT resource?) Anyway, just wanted to vent a bit!