The common sentiment around AI in the 90s and early 2000s was that it didn't work; it had its hype, it had its heyday, but it seemed like a dead-end for the most part. The Perceptron was merely a linear function approximator. And the Multi-layer Perceptron was a little more capable, but the many orders of magnitude it would have to scale up in order to be convincing just wasn't feasible back then (it finally was in the 2010s).
Simple statistical models that aren't "AI" so much as just generic ML were and are quite useful: like recommendation and newsfeed engines ("the Algorithm" as we call it so often today). Love 'em or hate 'em, they can be quite good at predicting interest/engagement.
The resurgence in deep learning in the 2010s has shown us new magic tricks, but they're still just that: parlor tricks. At least they're more convincing tricks than what we had 40 years ago!
That's what ultimately depresses me about AI. It's still just a parlor trick. We haven't actually taught computers to think, to reason, to be innovative. Deep learning is definitely having its day, but I suppose this too will pass unless we can unlock certain ways to make AI reliable and responsible. Or... to just start understanding it in general.
Simple statistical models that aren't "AI" so much as just generic ML were and are quite useful: like recommendation and newsfeed engines ("the Algorithm" as we call it so often today). Love 'em or hate 'em, they can be quite good at predicting interest/engagement.
The resurgence in deep learning in the 2010s has shown us new magic tricks, but they're still just that: parlor tricks. At least they're more convincing tricks than what we had 40 years ago!
That's what ultimately depresses me about AI. It's still just a parlor trick. We haven't actually taught computers to think, to reason, to be innovative. Deep learning is definitely having its day, but I suppose this too will pass unless we can unlock certain ways to make AI reliable and responsible. Or... to just start understanding it in general.