Capterra Business Intelligence Blog

Guides, help, and tools for business intelligence professionals.

The Saavy Small Business Guide to Machine Learning vs. Artificial Intelligence

Share This Article

“We have met the enemy, and he is us.”- Pogo Possum

There are two things you need to know about machine learning versus artificial intelligence, that great and oft-written-about (keyword) battle:

  1. Technically, it’s not a battle. Machine learning (ML) is a type of artificial intelligence (AI); it fits underneath the bigger umbrella of AI.
  2. Even though “machine learning vs. artificial intelligence” isn’t a battle, there is a winner in this scenario: you and your small business.

Don’t get me wrong, there are distinctions to be made between the bigger idea of artificial intelligence, and the specific application of AI called machine learning.

That said, ML and AI aren’t contrary approaches. Machine learning is a popular subfield of artificial intelligence, which happens to have gotten huge in the past few years.

In other words, machine learning, like Hansel, is so hot right now.

Why do these seemingly academic distinctions matter to you as a small business owner? Because investing in machine learning—either the software or companies that do it—is a great way to save your nonartificial business intelligence from doing pointless busy work. And, software and services that use machine learning can help you learn things you otherwise wouldn’t have the time, or ability, to learn.

And it’s that time-saving element that helps you as a small business owner. The bigger, enterprise players have resources you don’t. They’ve got research departments that can spend weeks researching how competitors are positioning their products, or how customers are reacting to their newest rollout. You probably don’t have that luxury. Machine learning, however, can give you the insight you’d get from hours of research in a few minutes.

In short: Machine learning is canned agility. Keep reading to get a fuller sense of how ML and AI are related, as well as specific examples of how ML can help a small business succeed.

Machine learning vs. artificial intelligence: A High-Level Overview

Broadly speaking, artificial intelligence is a machine that thinks like a person. If that seems like a broad definition, it is. A better, and common, way to determine when something artificial is intelligent is the famous Turing test: If you didn’t know you were communicating with a computer, would you be able to tell?

The original Turing test involved talking to two parties—a computer and a human— through a wall. If you couldn’t tell which of the two parties was a computer, the machine seemed “human” enough, and, voila, that machine had passed the Turing test.

A depiction of the Turing test

So, where does machine learning fit in?

If artificial intelligence is any machine that thinks like a human, the specific way machine learning “thinks like a human” is by learning.

This seems obvious, but that’s because you’re a human. Your brain is designed to learn—to take in new facts (there are many red fruits), turn those facts into concepts (a red fruit is an apple), and use those concepts to think faster (I want an apple, so I’ll look for a red fruit).

Computers traditionally weren’t designed to learn. You fed them an input, and they gave you something back. But all it could do was that particular type of input you’d told it to do.

Computers that use machine learning don’t just take in information and spit it back out. They can look at different facts, the way a human does, turn those facts into concepts, and use those concepts to compute better and faster.

Machine Learning for Your Small Business

What does this mean for a small business?

Software and tools with machine learning can look at a lot of facts (customer demographics, product reviews, sales figures), summarize those facts into concepts and insights, and communicate those insights to you, so you can do business, better.

Machine learning is already in use in some high-profile projects. Here are a few examples you’ll probably recognize:

  • Autonomous vehicles (probably the most commonly cited example of ML)
  • The “did you mean” function when you misspell something in Google
  • The ranking of “top things to do” in a Google search for a given topic
  • Speech recognition capabilities (such as Siri)

Top sights’ results in Google

Where small businesses are most likely to interact with machine learning is in prepackaged solutions: either business intelligence software programs that use machine learning algorithms, or services provided by vendors such as the ones I’ll discuss below.

While “the range of business problems that can be addressed with machine learning is huge,” according to analysts at Gartner, ML is not exactly DIY.

It can take trained data scientists between “three to 15 months” to build a custom ML tool. Software, however, “can be deployed within a few weeks (say, four to six from point of purchase.” (The full report is available to Gartner clients.)

There is another option, though: You can use a third-party service, such as Crayon or MonkeyLearn.

ML case study: Crayon saves Budget Dumpster $25,000

Crayon is a market intelligence platform that uses machine learning to scan millions of data sources (everything from web pages to customer reviews to tweets), and tell you everything important it finds.

For example, did a competitor change how they’re advertising a product? Or are there reviews that rave about your new initiative? Crayon finds that information, and delivers it to you in far less time than you could find it by searching on your own.

Budget Dumpster, a small dumpster rental service, used Crayon to save over $25,000 on market intelligence, and outmaneuver its larger competitors. The rental company has competitors in multiple sectors, both in dumpster rental and in the broader waste management vertical. That’s a lot of competitors—and data—to track.

Crayon made Budget Dumpster more agile by delivering competitor data. With Crayon’s competitive analysis software, Budget Dumpster could see how its competitors were positioning products, what content they were producing, and where they were targeting their efforts. Then, instead of reacting to a competitor’s decision weeks after the fact, Budget Dumpster could scan the intel they got from Crayon, and pivot immediately.

Where Crayon really shines is in making fine distinctions. I spoke to Ellie Mirman of Crayon, who described how Crayon’s machine learning is “about identifying what changes are meaningful.”

For instance, a competitor can make a lot of changes to a website, but many are trivial. Crayon’s machine learning algorithms pick out substantial changes.

Mirman cites metadata as a key example: “Identifying changes to a company’s website metadata would take a lot of digging, but that can show you what keywords they’re targeting, and how they’re positioning themselves.” Those metadata changes represent a lot of research for the enterprise players: “They’ve probably been doing content testing on their own, and then implementing it on their website.”

Thus, those changes to their site reflect market intelligence that you can take advantage of. Thanks to a tool such as Crayon, the results of that work—say, changing a few keywords—are delivered to you as intel. “It’s almost like you’re leveraging the competitor’s resources for your own benefit,” Mirman adds.

ML case study: MonkeyLearn brings ML to startups

MonkeyLearn’s platform gives small and medium businesses a way to do machine learning without having to hire data scientists.

“We want to enable those companies [that] don’t have resources to hire machine learning teams,” says MonkeyLearn COO Federico Pascual. “For companies [that] lack the resources to use machine learning, we’ve built a platform that allows them use that technology right now.”

One way that technology helps is by turning product reviews into insights.

Modern startups sell their products over a bewildering range of websites. That bewildering range of websites generates an even more bewildering range of product reviews, many of which offer useful insights. “Hiring people to go over these reviews would be expensive, but if you can use machines to do it, you cut down on the cost,” says Pascual.

MonkeyLearn analyzes reviews, saving small businesses the cost, or time, of reviewing them. If there are complaints about a price point, or comments about a feature that needs to be improved, MonkeyLearn’s machine learning algorithms will distill those disparate reviews into one solid report. “At the end of the day, small businesses use our platform to automate manual workflow, instead of analyzing the data manually,” Pascual adds.

Machine learning vs. artificial intelligence: Your take?

If you’re interested in learning more about machine learning, check out one of these Capterra posts:

Have you used machine learning software to help your business get ahead? If so, let me know in the comments!

Looking for Business Intelligence software? Check out Capterra's list of the best Business Intelligence software solutions.

Share This Article

About the Author

Geoff Hoppe

Geoff Hoppe writes about business intelligence and field service management for Capterra. His background is in education and higher ed, but he’s interested these days in how small businesses can use software to be more agile and efficient. When he’s not reading and writing about software, he’s probably reading and writing about history, music and comic books, finding new hikes throughout Virginia, or following the Fighting Irish.

Comments

[…] Source: blog.capterra.com […]

[…] The Saavy Small Business Guide to Machine Learning vs. Artificial Intelligence – Capterra […]

Comment on this article:


Your privacy is important to us. Check out our Privacy Policy.