The ABCs of AI, algorithms and machine learning

Advanced computer programs influence, and can even dictate, significant parts of our lives. Think streaming services, credit scores, facial recognition software.

As this technology becomes more sophisticated and pervasive, it’s important to understand the basic terminology.

People often use “algorithm”, “machine learning” and “artificial intelligence” interchangeably. There is some overlap, but they are not the same things.

We decided to call on a few experts to help us get a good grasp of these concepts, starting with a basic definition of “algorithm”. The following is an edited transcript of the episode.

Melanie Mitchell, Davis Professor of Complexity at the Santa Fe Institute, has offered a simple explanation of a computer algorithm.

“An algorithm is a set of steps to solve a problem or achieve a goal,” she said.

The next step is machine learning, which uses algorithms.

“Rather than a person programming the rules, the system itself learned,” Mitchell said.

For example, voice recognition software, which uses data to learn which sounds combine to become words and sentences. And this type of machine learning is a key part of artificial intelligence.

“Artificial intelligence is essentially the ability of computers to mimic human cognitive functions,” said Anjana Susarla, who teaches responsible AI at Michigan State University’s Broad College of Business.

She said we should think of “AI” as a generic term.

“AI is much broader, holistic, compared to just machine learning or algorithms,” Susarla said.

That’s why you might hear “AI” as a loose description for a range of things that show some level of “intelligence”. Like software that sifts through photos on your phone to sort out which ones have cats or advanced caving robots exploring caves.

Here’s another way to think about the differences between these tools: the kitchen.

Bethany Edmunds, professor and director of computer programs at Northeastern University, likens it to cooking.

She says an algorithm is essentially a recipe – step-by-step instructions on how to cook something to solve the “being hungry” problem.

If you took the machine learning approach, you would show a computer what ingredients you have and what you want for the end result. Say, a cake.

“So maybe it would take all the combinations of all the food types and putting them together to try and replicate the cake that was provided to him,” she said.

AI would send the whole problem of hunger back to the computer program, determining or even buying ingredients, choosing a recipe or creating a new one. Just like a human would.

So why are these distinctions important? Well, for one thing, these tools sometimes produce biased results.

“It’s really important to be able to articulate what those concerns are,” Edmunds said. “So you can really dissect where the problem is and how we’re going to fix it.”

Because algorithms, machine learning, and AI are pretty much integrated into our lives at this point.

Columbia University School of Engineering has another explanation artificial intelligence and machine learning, and it lists other tools besides machine learning that can be part of AI. Like deep learning, neural networks, computer vision, and natural language processing.

At the Massachusetts Institute of Technology, they point out that machine learning and AI are often used interchangeably because most AI these days includes some amount of machine learning. A piece from MIT’s Sloan School of Management also falls into the different subcategories of machine learning. Supervised, unsupervised and reinforced, like trial and error with some kind of numerical “rewards”. For example, teaching an autonomous vehicle to drive by telling the system when it made the right decision, like not hitting a pedestrian, for example.

This piece also indicates a 2020 Deloitte surveywhich revealed that 67% of companies already use machine learning and 97% plan to do so in the future.

IBM has a useful graph to explain the relationship between AI, machine learning, neural networks and deep learning, presenting them as Russian nesting dolls with the broad category of AI as the largest.

And finally, with so many companies using these tools, the The Federal Trade Commission has a blog outlining some of the consumer risks associated with AI and the agency’s expectations for how companies should deploy it.

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