We understood the basics of flight before we could ourselves fly.
We did not understand in the sense of having a correct theory of fluid dynamics. We understood in the sense of having a working model, a paper plane, which actually flew.
Today it is the reverse. We have a theory, but we have no convincing paper brain. We have no working model.
That is also true for machine intelligence today—we have a general theory of intelligence, and can see what the technical side of the problem of building it consists of.
We have the reverse of what ancient China had. They had no theory but they had a paper plane. We have a theory but we have no paper brain.
In general, there is no need to perform an engineering feat before you can claim to have understood what problem it involves.
But with paper planes, we actually performed the essential engineering feat, in paper. The plane flew. But we have no paper brain that thinks.
We did not understand in the sense of having a correct theory of fluid dynamics. We understood in the sense of having a working model, a paper plane, which actually flew.
Today it is the reverse. We have a theory, but we have no convincing paper brain. We have no working model.
We have the reverse of what ancient China had. They had no theory but they had a paper plane. We have a theory but we have no paper brain.
But with paper planes, we actually performed the essential engineering feat, in paper. The plane flew. But we have no paper brain that thinks.
IMO, we do have the machine-intelligence equivalent of paper planes.
They come in two main forms: forecasters and expert systems.
What we need is more heavy lifting.