Test Whether an AI Actually Understands Your Patterns By Asking It What You'll Probably Do Next. Most Fail Immediately.
A useful, concrete test for whether an AI has actually recognized a pattern in someone's behavior, as opposed to simply having access to a lot of information about them, is asking it to predict what they'll probably do next in a specific, familiar type of situation. Most systems, tested this way, fail immediately — not because they lack data, but because access to data and recognition of a pattern within it are different achievements.
Genuine pattern recognition implies predictive capability almost by definition: if a system has actually identified the recurring mechanism behind a person's choices, it should be able to say, with reasonable confidence, what that mechanism will likely produce in the next similar situation. If it can't, whatever it has isn't recognition yet — it's still just information.
This test is useful precisely because it's hard to fake. A system can summarize someone's history accurately without having found the pattern inside it, and that gap shows up immediately the moment prediction is required, because summarizing the past and modeling what the past implies about the future draw on genuinely different capabilities.
An AI that can accurately anticipate what someone is likely to do next, in the specific situations where their pattern actually runs, has crossed a real threshold that most current systems haven't reached — and it's a more honest measure of understanding than any amount of accurate-sounding summary.
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Momar Lissa Ndiaye ("MLN") is the Founder & CEO of weyoga Inc., a Delaware company. — weyoga.ai · mln@weyoga.ai