Back in college, I had a summer job completing research for a clinical health professor. She was a leading expert in diagnosing and treating open human wounds. My job was to survey other experts, get them to examine photos of open wounds, and then recommend a treatment.
A few months ago, I discovered a smartphone app which replaces this work.* You take a photo of an open wound and upload it to the cloud. I suspect that the photo is run through an image recognition model, called a Convolutional Neural Network (CNN), that identifies specific features of the wound for treatment. Current machine learning is very good at completing narrowly defined tasks, such as analyzing a specific type of medical image, because they have millions of previous examples to train from. It is not good at handling non-standard cases.
Jobs that require experts will increasingly be impacted as cloud storage and machine learning services come down in cost and become more accessible.
For example, we have seen generalists such as nurse practitioners, paralegals, and dental hygienists, offer services that used to be only available through doctors, lawyers, and dentists. Machine learning allows these generalists to offer even more of these services. As a result, specialists will be left handling non-standard cases.
Making specialist services more affordable means that those who were under-served before now have access. The total market size grows and consumers benefit from this outcome.
Reflecting on my daily work, I can see the hard skills such as reporting and analysis being automated. However, the softer skills of project management, architecture design, and relationship building are tasks that I just can't see being automated anytime soon. Context for how to operate in a certain industry won't be easily captured either. These soft skills are the ones that will win in the age of machine learning.