[joe_the_user]

How would one put it?

"Adaptive Intelligence" might be described as the ability to be given a few instructions, gather some information and take actions that accomplish the instructions. It's what "underlings", "minions" do.

But if we look at deep learning, it's almost the opposite of this. Deep learning begins with an existing stream of data, a huge stream, large enough that the system can just extrapolate what's in the data, include data leads to what judgements. And that works for categorization and decision making the duplicates what decisions humans make or even duplicates what works, what wins in a complex interaction process. But all that doesn't involve any amount of adaptive intelligence. It "generalizes" something but our data scientists have no idea exactly what.

The article proposes an "engineering" paradigm as an alternative to the present "intelligence" paradigm. That seems more sensible, yes. But I'm doubtful this could accepted. Neural network AI seems like a supplement to the ideology of unlimited data collection. If you put a limit on what "AI" should do, you'll put a limit on the benefits of "big data".

[visarga]

Neural nets don't generalize much, they interpolate between training examples. But if you couple them with search (MCTS) then you can do logic and reasoning with them, like AlphaGo.

You can also put any algorithm you want inside a neural net as long as you have a mechanism to pass gradients back - for example in the final layer you could have a complex graph-matching algorithm to map the predictions to the target, or you could put an ODE solver as a layer, or a logic engine, or a database.