On Thu, Aug 23, 2018 at 4:54 AM <silkl...@bobf.frankston.com> wrote: > 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. >
A big fan of James Mickens types of guys who always call for skepticism and careful analysis and continual improvement in our mental models of how things work. > 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. Pardon my ignorance, but what is CDMG ? Regards, Bharat
