Can machines understand? Not in some distant future, but now. Not perfectly, but approximately. Not by achieving consciousness, but by approximating the functional patterns of human understanding well enough to matter.
You’re at the grocery store. You’ve made chicken three times this week. You could make it again, 95% confidence it’ll turn out well.
But you reach for fish instead. Never cooked this type before. Maybe 40% confident. The recipe looks complicated.
We chase impossible dreams. We hold contradictory beliefs. We want everything at once, even knowing we can’t have it. Parts 1 and 2 explored how AI systems approach functional understanding through confidence calibration and context-aware decision-making. But the most distinctively human behaviors aren’t the rational ones we can model. They’re the irrational ones we can’t.
After three articles exploring how AI approaches understanding, through confidence calibration, context-aware reasoning, and the limits imposed by human irrationality, there’s an obvious question: How close can cutting-edge AI actually get?
Throughout this series, I’ve carefully skirted a question. I’ve discussed functional understanding, confidence calibration, context-awareness. But I’ve added disclaimers: “AI doesn’t have phenomenal consciousness,” “It doesn’t feel uncertainty.”
These aren’t evasions, they’re honest acknowledgments of what we don’t know. But they leave hanging the question many people actually care about:
Five posts into this series, I need to acknowledge something I’ve been getting wrong: I’ve been treating decision-making as if it happens inside individual minds.
A person weighs evidence, calibrates confidence, manages uncertainty, chooses actions. Even when I discussed irrationality, I framed it as internal struggle within a single self.