You might think that news of “major AI breakthroughs” would do nothing but help machine learning’s (ML) adoption. If only. Even before the latest splashes — most notably OpenAI’s ChatGPT and other generative AI tools — the rich narrative about an emerging, all-powerful AI was already a growing problem for applied ML. That’s because for most ML projects, the buzzword “AI” goes too far. It overly inflates expectations and distracts from the precise way ML will improve business operations.
The AI Hype Cycle Is Distracting Companies
By focusing on sci-fi goals, they’re missing out on projects that create real value right now.
June 02, 2023
Illustration by Skizzomat
Summary.
Machine learning has an “AI” problem. With new breathtaking capabilities from generative AI released every several months — and AI hype escalating at an even higher rate — it’s high time we differentiate most of today’s practical ML projects from those research advances. This begins by correctly naming such projects: Call them “ML,” not “AI.” Including all ML initiatives under the “AI” umbrella oversells and misleads, contributing to a high failure rate for ML business deployments. For most ML projects, the term “AI” goes entirely too far — it alludes to human-level capabilities. In fact, when you unpack the meaning of “AI,” you discover just how overblown a buzzword it is: If it doesn’t mean artificial general intelligence, a grandiose goal for technology, then it just doesn’t mean anything at all.