First, stepping back, https://youtu.be/ajGX7odA87k provides some examples of my ML and AI involve too much magical thinking. That jobs with some of the points in the Quanta essay. I'm especially sensitive to this because of days of AI including a stint in the MIT clinical decision making group (over four decades ago). The focus wasn't just on computing but also understanding how doctors approached problems. Humans don't do a great job either.
But when I see "Three decades ago, a prime challenge in artificial intelligence research was to program machines to associate a potential cause to a set of observable conditions. Pearl figured out how to do that using a scheme called Bayesian networks. Bayesian networks made it practical for machines to say that, given a patient who returned from Africa with a fever and body aches, the most likely explanation was malaria. In 2011 Pearl won the Turing Award, computer science’s highest honor, in large part for this work." I'm wary because in that CDMG we recognized that Bayesian approaches didn't work when there wasn't a well-defined space of choices. But causal reasoning is also a problem when there isn't enough information. I can understand the attraction of a WTF approach of ML/AI (I call it splat -- throwing the problem against wall and reading the shards). So, yeah, it would be nice to be able to understand why ... whether we're three years old or eighty. Yet we still don't know why the chickened cross the road -- we understand some ways but not the ultimate why.
