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Who Will Thrive in an AI World? November 26, 2017

Posted by Peter Varhol in Machine Learning, Technology and Culture.

Software engineers, of course, who understand both relevant programming languages and the math behind the algorithms.  That is significantly less than the universe of software engineers in general, but I don’t see even those math-deprived programmers having a big problem, at least in the short term.

Beyond that?  Are we all toast?

Well, no.  Today someone asked me how machine learning would affect health care jobs.  I thought to where health care was going with machine learning, and to my own experiences with health care.  “The survivors will be those who can understand what the algorithms tell them, but also talk with the patients those results affect.”

I have dealt with doctors (such as my current PCP, who could be a much better doctor if she simply trusted herself) who simply look at test results and parrot them back to you.  I had a doctor who I liked and trusted, who could not find cancer but insisted it was there, based on photographs (it was not).

These are not health care professionals who will thrive in an era of AI-assisted medical evaluation and diagnosis.  They simply parrot test results, without adding value or effectively communicating with the patient.

To be fair, our system has created this kind of doctor, who is afraid of using their expertise to express an independent opinion.  I had one who did employ his expertise, during my cancer scare.  He came into my room, and said, “Where is your nose drain?  How come you’re not choking?”  Then “I looked at your MRI from six years ago, and you had indications then.  Whatever this is, it probably isn’t cancer.”  It wasn’t.

Doctors have become afraid to use their expertise, because of the fear of lawsuits and other recriminations.  That is unfortunate, and of course not entirely their fault.  But this is just the kind of doctor who will not survive the machine learning revolution.

I think that general conclusion can be extended to other fields.  Those that become overly reliant on machine results, and decline to employ their own expertise, will ultimately be left behind.  Those who are willing to use those results, yet supplement them with their own expertise, and effectively explain it to their patients, will succeed.  We are still people, after all, and need to communicate with one another.



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