[theferret]

The author's point is valid - innovation requires more knowledge as the tech that required less knowledge gets built. Today we face the "dual phd" problem, tomorrow the tri.

Obviously that is not sustainable. If society is to continually innovate, you need to stop building innovative systems and start growing them. One approach to this is true AI; not improving your NLP algorithm by 10% with 3x the math complexity IE the transformer model (quote taken from Michael Stonebrake, although he was referencing database research), but by building math that can grow math.

Hell even math is becoming a road block (try integrating THAT Bayes!). The point is as long as we must learn to build, we will run into a wall as humans have finite lifespans and don't scale horizontally (nor do they want to). If we build something that we can feed or point to un-wrangled, raw information into such that it can learn on our behalf, we might have a shot.

Now BACK to pumping out small improvement papers, innovators!

[imtringued]

Yeah, if innovation requires people to receive more and more institutionalized education, then expect innovation to stop eventually.

From what I have seen cross disciplinary innovation usually happens because someone's interest in one domain has created a demand for a solution that can only be fulfilled with a different one. It's rarely about being skilled in both domains, it's about committing yourself to something a single domain expert has no interest in.