Most AI Progress Solved Problems Computers Always Had. The Problem of Not Recognizing Your Own Patterns Was Never a Computer Problem — Until Now It Can Be Addressed As One.
Most of the historical trajectory of computing progress has been about problems computers themselves inherently had — limited memory, limited processing speed, limited ability to parse unstructured input like language or images. Each generation of AI progress has been substantially about closing those specifically computational gaps.
The problem of a person not recognizing their own recurring behavioral pattern was never, historically, a computer problem in that sense — it wasn't caused by any limitation of computing hardware or software. It was a human problem, existing independently of technology, that computers simply had no relevant capability to address until fairly recently.
What's changed is that computing capability has finally reached a point where it can plausibly be pointed at this specific, previously non-computational problem — not because the problem changed, but because the tools finally caught up to being able to hold a continuous record and extract a pattern from it in a way that's actually useful to the person living inside that pattern.
This is a different kind of AI progress than closing a computational gap that always existed. It's computing capability arriving late enough, and becoming general enough, to finally be pointed at an old human problem that had simply been waiting for a capable enough tool to show up.
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Momar Lissa Ndiaye ("MLN") is the Founder & CEO of weyoga Inc., a Delaware company. — weyoga.ai · mln@weyoga.ai