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Ask a client/server native. application programmer from the 90s if they needed to learn how to do web development. But, I think ML/AI is a bit different than t

Ask a client/server native. application programmer from the 90s if they needed to learn how to do web development. But, I think ML/AI is a bit different than that. ML seems like more of a backend thing or specialized application. How many people need to work on face authentication? Or object recognition? I’m just not sure how much AI/ML will be considered a general purpose programmer requirement. Interacting with models, sure... but not necessarily making them. In this respect maybe ML is similar to databases — we all use them, but don’t need to make them. There will always be programmers writing at the interface between the user and the computer. Right now, those developers are heavily skewed towards web apps. But there is a sizable amount of mobile development too. If anything, I’d think the web jobs would move closer towards mobile as opposed to ML.

kqr 8y ago
I think your definition of ML may be too narrow. I recently had on my plate to determine -- for all incoming requests -- whether or not they belonged to an outlier category that was only very loosely defined as a group, but impossible to separate on an individual level. This is one of those situations where I greatly benefited from thinking of the problem from an ML perspective, as it allowed me to create a very simple probabilistic model and verify that it was sufficiently accurate. I believe we'll get more of those "ML in the small" things popping up even for regular developers. It's not something new, it's just a mental perspective shift toward the probabilistic.
- softawre 8y ago
  Great comment, and totally agree. And this exactly why an enterprising software engineer would be well served to be familiar with how to do basic machine learning. You don't have to be great at it, you just need to know how to copy somebody else's notebook or whatever and get it to work on your simple data set.

kqr 8y ago

I think your definition of ML may be too narrow. I recently had on my plate to determine -- for all incoming requests -- whether or not they belonged to an outlier category that was only very loosely defined as a group, but impossible to separate on an individual level. This is one of those situations where I greatly benefited from thinking of the problem from an ML perspective, as it allowed me to create a very simple probabilistic model and verify that it was sufficiently accurate. I believe we'll get more of those "ML in the small" things popping up even for regular developers. It's not something new, it's just a mental perspective shift toward the probabilistic.

softawre 8y ago
Great comment, and totally agree. And this exactly why an enterprising software engineer would be well served to be familiar with how to do basic machine learning. You don't have to be great at it, you just need to know how to copy somebody else's notebook or whatever and get it to work on your simple data set.