Yes. Though arguably this is just the same thing as the traditional approach to deep learning, which is to select features to input into a model. You don't always just have to train on raw information, pre-processing the input data and calculating specific features that we know, as humans, will be important for the final result and feeding those in addition to the raw data is a common approach. Don't see much difference between this and taking the output of a network and running it through a few decision trees. Most publishable projects applying AI you see typically have these type of human interaction on both sides of models.

> As much I'm hoping there'll be a breakthrough in AGI

I think it probably won't be one breakthrough, but several, over decades. Personally, I'm pretty happy that AGI is taking a long time to materialize. We likely won't see a "fast takeoff scenario" (the computer is learning at a geometric rate !!1). It will likely happen gradually over years (progressively more intelligent, more aware computer systems), and we may have a chance to adapt in response.

A business professor told me that cars were entirely incremental innovation all the way from the model T. Just little improvements, one at a time. I don't know if that's true, but I wonder if it will be an apt analogy for AGI - one feature at a time, and older attempts at it just look outdated.

Perhaps, although the automobile has always had a clear explicit purpose: convert potential energy into rotational energy in a controlled manner. It’s hard to identify such a singular purpose for AGI.

Things like automatic transmissions and fuel injection aren't particularly incremental.

As someone who once got stuck in an intersection after flooding a '79 Monte Carlo's carburetor while deciding to go on red, I'm with you on fuel injection. But I could see the counter-argument that something that makes an experience nicer is not radical innovation. That old junker got me where I need to go for a while.