Summary: The AI Hype Cycle Is Distracting Companies

For most ML projects, the term “AI” goes entirely too far — it alludes to human-level capabilities.

That’s because for most ML projects, the buzzword “AI” goes too far.

Defining “AI” as something other than AGI has become a research challenge unto itself, albeit a quixotic one.

Here’s the problem: Most people conceive of ML as “AI.” This is a reasonable misunderstanding.

“AI” haunts ML.

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The AI Hype Cycle Is Distracting Companies

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.

Read the complete article at: hbr.org