I've gotten some decent mileage with family and friends by making the distinction between "weak" AI and "strong" AI. I tell them that any AI that's designed to

I've gotten some decent mileage with family and friends by making the distinction between "weak" AI and "strong" AI. I tell them that any AI that's designed to only solve one type of problem (or a finite set of problems) is "weak." An AI that can figure out how to solve an arbitrary problem is "strong." I then tell them that we've never been able to create a strong AI, and we've never been able to convert a weak one to a strong one. They ask why not. I tell them that every weak AI, at its core, has a mathematical way to evaluate its outcome. Every time it does something, it does it by assigning some number to the outcome, and it tries to get that "number" as close to the "good" number as possible. All of our advances in weak AI have been in ways to come up with better numbers, or use computers to come up with good numbers to situations that we understand very well but aren't good at attaching numbers to. Finally, I say that with the way we make weak AIs, to make it into a strong AI, we'd have to come up with a way to assign an accurate number to literally every single decision the computer might be asked to make. But there are infinitely possible decisions, so we'd have to come up with infinite ways to come up with a number. To come up with strong AI, we need some "universal" way for the computer to think about the world, but we have no idea what would look like. This explanation is hardly adequate for a true understanding of AI, but it works pretty well for helping them understand the gist of the problem without using some misleading metaphor that would just give them another bad understanding.