Industry experts are constantly sharing their AI experiences and stories. We decided to cast the net wider and get a different perspective, in this episode we are joined by Chris Ehrlich, managing editor of Datamation. Ehrlich brings over 20 years of experience reporting on B2B technologies and chats with co-hosts, Evalueserve’s CTO Rigvinath Chevala, and VP of Marketing Erin Pearson about all things AI.
From the AI as a misnomer, biases, innovation demand, and more, the trio brings you an engaging conversation. Subscribe to the Decisions Now podcast today, you can find us on Spotify, Apple Podcasts, and Amazon Music among other platforms.
AI as a Misnomer
Often, the term AI is used interchangeably with different technologies. We asked Ehrlich his thoughts on the matter as someone who writes about AI and technology often.
“I think that it is a true misnomer. In that, we haven’t reached mainstream human-like intelligence,” he said. “And if that’s really the goal or the notion of the term, then I think generally speaking, in terms of mass market applications, we’re, as we know, far from that. And so, I think it’s overused.”
When companies talk about AI, often they mean Machine Learning (ML) instead.
“I think machine learning, by comparison, is a really apt description and accurate in that sense and really toward the end of automation,” Ehrlich added. “So, to me, it’s more straight ML and automation and AI is more of this sort of long-term goal. And I think it’s sort of overhyped essentially right now.”
Agreeing with Ehrlich, Chevala stated there is general AI, which is perceived as human-like intelligence, and narrow AI which is mostly ML. He added that he had recently seen a picture of a non-existent woman, generated completely by AI with intricate details down to freckles, the two-tone color of hair, and so on, which can be classified as the perception of general AI, which isn’t what typically applied to businesses.
“In pop culture, they use it very generally and it’s this really amazing human-like intelligence, it’s going to replace humans. It’s very sci-fi, kind of scary depending on how you want to look at it,” Pearson said.
The reality, however, when people say they have AI-based technology is often they are coding stuff, but they shouldn’t be using AI unless it’s truly an application of AI, she added.
With all the fluff in the verbiage around AI, the hosts asked Ehrlich his perspective on when in use when is AI legitimately AI versus when it’s just a marketing term?
“What I’m seeing is, typically startups are the most specialized and they are very application use case specific and technology specific,” Ehrlich said. “So, in terms of the marketplace, machine learning is what’s being used to deal with the abundance of data or the overabundance of data.”
In the marketplace, ML is being used to deal with the abundance of data, and that’s also where we see AI being used to solve big data-related challenges.