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Artificial Intelligence vs. Machine Learning

The terms ‘Artificial Intelligence’ (AI) and ‘Machine Learning’ (ML) are often used interchangeably. As the hype grows around these two emerging technologies, so do the misunderstandings. That lack of awareness, combined with the pressure to cash in on the excitement, could be the reason why as many as 40% of “AI startups” don’t actually use artificial intelligence. At least, not in a way that is considered “material” to their business.

Though artificial intelligence and machine learning are very closely related, they are not the same thing. Let’s take a closer look at each of them:

Machine Learning

What is it?

Machine learning is one of the many branches of artificial intelligence. It refers to computer systems that use algorithms and statistical models to adapt and learn without specifically being told how to. It then uses these learnings to draw conclusions about patterns in the dataset.

In simpler terms, machine learning is given information to analyze and make assumptions about it without instruction.

How does it work?

The easiest way to understand machine learning is by looking at recommender systems. Recommender systems (sometimes called recommender engines) do just that–they recommend things. When you log in to your YouTube account and see a front-page full of videos, those recommended videos are specific to you. The videos you see there will be different from almost anyone else’s recommended list.

YouTube’s recommender system uses machine learning to determine, or at least make an educated guess about what is most likely to interest you as an individual. It knows what you have watched before, how long you watched it for, whether or not you clicked like or subscribe, if you made a comment, and every other interaction you have ever had with a YouTube video (and/or any other Google service). It then takes that information it knows about you, combined with what it knows about other users, and compares it with what it knows about other available videos. The more you, and others, use the system, the more it “learns” about what you like and dislike (or, at least, what keeps your attention), and makes predictions in the form of new video recommendations.

This, of course, is just one of the many types of machine learning and is known as supervised learning. Different forms of machine learning act differently, as described in this explanation of supervised vs unsupervised machine learning, but the goal is the same—to use algorithms and models to learn without specific instruction.

Artificial Intelligence

What is it?

Artificial intelligence is a very broad term covering many facets of science, engineering, computing, and more. What exactly is and isn’t AI also changes over time. Machine learning is just one of the many aspects of artificial intelligence.

In its simplest form, artificial intelligence refers to “the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence”, according to the former Dean of Computer Science at Carnegie Mellon, Andrew Moore.

How does it work?

AI does not necessarily follow a pattern or playbook. It doesn’t need to be told or programmed what to do. It is smart, and once created, thinks and acts independently. The reason what is, and what isn’t, artificial intelligence changes is partially because it is so subjective. What makes something smart? What requires a human intellect to achieve? What exactly constitutes acting independently? Depending on who you ask, and when you ask them, the answers may be different.

Here are a few tests we can consider to gain a deeper understanding of how AI operates

  • Turing Test: In his 1950 paper titled, “Computing Machinery and Intelligence”, machine learning pioneer Alan Turing tried to answer that exact question by introducing what is now known as the Turing Test. The Turing Test involves three participants–a human asking questions, another human answering them, and an AI attempting to answer as well. The questioner asks questions of both the human and the AI and attempts to determine which is which. If the AI is successful at convincing the human that it is, in fact, human as well, then it has passed the Turing Test. Because of its limitations, many other tests have been devised to determine whether something is or is not artificial intelligence.

  • Reverse Turing Test: The Reverse Turing Test, as it is commonly seen in CAPTCHAs, is when a person tells a computer that it is in fact human.

  • Marcus Test: The Marcus Test asks the AI meaningful questions about a movie or TV show it has been shown.

  • Lovelace Test 2.0: The Lovelace Test 2.0 tries to determine the validity of AI-based on how well it can create art. The list goes on and on.

Just as we humans are continually changing, as is our understanding of intelligence. Artificial intelligence, the umbrella term used to describe machines that can think and act like people, is constantly adapting as well.


Though they are closely related, artificial intelligence and machine learning are indeed two different things. Artificial intelligence is the larger, broader term used to describe technology that can learn, think, and act in a way that makes it nearly impossible to differentiate from a human. Machine learning is one aspect of artificial intelligence that enables a program or system to make decisions without instruction. Both are exciting and paving the way for our future.

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About the Company:

Peterson Technology Partners (PTP) has been Chicago's premier Information Technology (IT) staffing, consulting, and recruiting firm for over 22+ years. Named after Chicago's historic Peterson Avenue, PTP has built its reputation by developing lasting relationships, leading digital transformation, and inspiring technical innovation throughout Chicagoland.

Based in Park Ridge, IL, PTP's 250+ employees have a narrow focus on a single market (Chicago) and expertise in 4 innovative technical areas;

  • Cybersecurity

  • Artificial Intelligence

  • Data Science

  • Cloud & DevOps

PTP exists to ensure that all of our partners (clients and candidates alike) make the best hiring and career decisions.

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