Let’s be honest – machine learning is complicated, and you might not understand all the core concepts and lingo. That’s OK, though. It’s not your fault.
Because even though machine learning is changing the way enterprise do business, interact with their customers, and plan for their future, many business leaders don’t understand the reality of what machine learning is, what it isn’t, and where it’s headed in the future.
With that in mind, Verint Next IT is inspecting the realities and unrealities of machine learning by offering “Shatter the Seven Myths of Machine Learning,” which was authored by Kjell Carlsson, Ph.D. of Forrester Research, a leading advisory and research firm that works with business and technology leaders. The report picks apart these misconceptions one by one, exposing the reality of machine learning, taking the guesswork out of the equation.
WHY SO MUCH CONFUSION?
So why are we so confused about machine learning? Well, there are a few reasons. For one, pop culture hasn’t helped the general public form an accurate perception of the technology. They think of autonomous, all-seeing, all-doing AIs like HAL in 2001: A Space Odyssey or, more recently, the empire-destroying AI gone wild in the final season of Silicon Valley. When you think of the countless other fictional depictions of machine learning, you can’t blame folks for having been guided astray.
Another reason for the rash of misconceptions comes from the fact that this is a new and evolving technology, the story of which is still developing. At times, machine learning experts haven’t been able to accurately explain machine learning, which is an inherently tricky task, given the pace at which AI and machine learning have grown. It doesn’t help that machine learning terminology has been misused with abandon – a problem that Forrester’s report expertly tackles.
Still, misconceptions and myths about machine learning and AI haven’t stopped business leaders from adopting this technology. A recent Forbes survey of 700 C-level executives found that about 80 percent of respondents considered AI to be integral to their company’s “digital transformation efforts.” The problem, however, is that business leaders aren’t immune to all this noise and misinformation, even when they’re making consequential decisions about how to use machine learning within their business.
“There is a pandemic of ML misconceptions and illiteracy among business leaders who must make critical decisions about ML projects,” the Forrester report states. “Many of them regularly make statements that, at best, belie an ignorance of ML and, at worst, are categorically false.”
The repercussions of misunderstanding are enormous. We see in the report that confusion around machine learning can doom projects from the beginning thanks to leaders not fully grasping machine learning truths. There’s also the risk of missing out on the easy opportunities for ROI that arise when machine learning is used efficiently.
BELIEVE IT OR NOT, HUMANS ARE PART OF MACHINE LEARNING
Many of the misconceptions around machine learning come from the notion that this technology operates without the need of any human input. Somewhere along the line, we came to think of machine learning as computers acting and thinking like humans. In reality, it’s more about learning the patterns in data – patterns that humans might not pick up on.
But, again, you still need humans in this process because the highest-value deployments of AI tools – like Verint’s Intelligent Virtual Assistants – rely on mountains of data that’s been validated by real people. The technology isn’t merely processing the data in a vacuum. Rather, our experts inspect intentions collected from customers over our 15 years of experience.
As you’ll read in Forrester’s report, machine learning technologies excel at processing data on a large scale, but there remain instances when humans are needed to interpret the information. In other words, computers aren’t going to figure out everything by themselves.
Business leaders must understand this nuance of machine learning, and thankfully Forrester breaks it down for you – as well as a host of other myths and misconceptions in the report.
Give it a read so you can be your company’s machine learning myth-buster.