I think this field is suffering from some confusion of terminology. In my mind there are three subfields that are crystallizing that each have different goals and thus different methods.
The first one is Data Science. More and more businesses store their data electronically. Data Scientists aim to analyze this data to derive insights from it. Machine Learning is one of the tools in their tool belt, however often they prefer models that are understandable and not a black box. Sometimes they prefer statistics because it tells you if your insights are significant.
The second one is Machine Learning Engineering. ML Engineers are Software Engineers that use Machine Learning to build products. They might work on spam detection, recommendation engines or news feeds. They care about building products that scale and are reliable. They will run A/B tests to see how metrics are impacted. They might use Deep Learning, but they will weight the pros and cons against other methods.
Then there are AI Researchers. Their goal is to push the boundaries of what computers can do. They might work on letting computers recognize images, understand speech and translate languages. Their method of choice is often Deep Learning because it has unlocked a lot of new applications.
I feel like this post is essentially someone from the first group criticizing the last group, saying their methods are not applicable to him. That is expected.
The first one is Data Science. More and more businesses store their data electronically. Data Scientists aim to analyze this data to derive insights from it. Machine Learning is one of the tools in their tool belt, however often they prefer models that are understandable and not a black box. Sometimes they prefer statistics because it tells you if your insights are significant.
The second one is Machine Learning Engineering. ML Engineers are Software Engineers that use Machine Learning to build products. They might work on spam detection, recommendation engines or news feeds. They care about building products that scale and are reliable. They will run A/B tests to see how metrics are impacted. They might use Deep Learning, but they will weight the pros and cons against other methods.
Then there are AI Researchers. Their goal is to push the boundaries of what computers can do. They might work on letting computers recognize images, understand speech and translate languages. Their method of choice is often Deep Learning because it has unlocked a lot of new applications.
I feel like this post is essentially someone from the first group criticizing the last group, saying their methods are not applicable to him. That is expected.