We’re continuing our mission to bust the myths that linger around machine learning so that you can make more informed, more practical, and more profitable decisions. This time around, we’re digging into machine learning terminology and setting the record straight.
One of the most impactful reasons for the glut of misinformation around machine learning is that these technical terms are haphazardly misused throughout the industry, and because of that, business leaders are often left confused when it comes time to invest in machine learning solutions. This is how companies end up overspending on projects that were doomed to fail from the onset or perhaps aren’t even the right fit for the problem they’re trying to solve.
To help put machine learning myths to rest, Verint Next IT is offering “Shatter the Seven Myths of Machine Learning,” a report by Kjell Carlsson, Ph.D. of Forrester Research, a leading advisory business and technology research firm. In the report, Carlsson explains how the terminology surrounding machine learning has led to confusion over the years.
“Not only do you have to contend with misinformation from movies, books, and wishful marketing, you also must deal with a set of terms that sound seductively intuitive, but often mean something else entirely,” Carlsson writes in the report.
Included in the report is an invaluable table of concepts and terms you’ll want to read if you’re working with machine learning. Forrester presents the misconceptions and then provides the accompanied reality surrounding a particular term. Think of it as an ultimate machine learning cheat sheet.
Here are a few topics from that cheat sheet and some insight into the importance of understanding what these terms actually mean.
Misconception: Machines learning to think and make decisions like humans.
Forrester’s definition*: “Applied statistics and other algorithms to identify probabilistic relationships in data.”
Misconception: Machines develop and employ human-like intelligence
Forrester’s definition*: “A wide range of ML and automation technologies that often leverage deep learning to analyze new structured and unstructured data sources.”
Misconception: Particularly advances and insightful techniques.
Forrester’s definition*: “Interchangeable with single or multilayer artificial neural networks.”
*Kjell Carlsson, Ph.D.; “Shatter The Seven Myths Of Machine Learning, Boost Your Machine Learning IQ And Help Your Organization, Your Team, And Your Career;” Forrester Research; January 2, 2020.
As you can see, these terms are not as intuitive as they seem. So, you may want to read up before your next big meeting. To learn more, take a look at a full list of key terminology and definitions within Forrester’s report.