Software developers thrive on certainty. If you feed a program a set of inputs, you'll always get the same outputs. For most of software's history, software was built entirely on deterministic logic. What goes in determines what goes out.
We even have a term for that: top-down programming. All algorithms follow a path, with branching that's also based on expected logic. When we debug code, we run down that same path over and over again, finding where behavior deviates from expectation, and wrangling it back on track.
Certainty and deterministic logic work for a lot of software. But the real world doesn't work that way. By contrast, AI is probabilistic. Answers are never exact. Instead, AI uses models to predict behavior, and then generates that behavior.
Perhaps the best way to describe this is how traditional software is updated vs. AI. Traditional software gets updates and patches. AI learns, evolving on its own, understanding and assimilating user feedback without manual intervention. This makes traditional software more precise, but AI more flexible.
By implementing AI into software engineering, we get the best of both worlds: software that is both precise and flexible. This article will explore that merger, and what it means for developers and engineers, as well as the users of their creations.
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