HBR: Machine Learning versus Artificial Intelligence
Walt McKenzie
Administrator, Moderator Posts: 76 admin
In this piece, Eric Siegel argues that machine learning (ML) 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 entire piece here.
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 entire piece here.
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