When asked what I work on, I often have to explain what machine learning is. I found myself using different explanations. Beyond explanations for a layperson, some perspectives are also useful for thinking about ML in general and communicating with experts:
I like the "learning patterns from data" definition best. I find it a rare definition that is both simple and accurate, which is normally a difficult balance to strike for complex topics.
I also like "learn patterns from data" because it's true and relatable.
For some stakeholders, I've called it "letting the machine learn the rules of the world from the data." It speaks to tech-adverse people that know computers can be better/faster/cheaper but don't understand the outcome may be to understand the world like humans would. Want to understand rules better? Get more data and find the right way to learn the rules!
I avoid calling ML anything about prediction, because I think people overfit to the assumption of time-based predictions already. I want people to think about unique problems like unsupervised or anomaly detection that doesn't fit in their mental model of a prediction.
I like the "learning patterns from data" definition best. I find it a rare definition that is both simple and accurate, which is normally a difficult balance to strike for complex topics.
I also like "learn patterns from data" because it's true and relatable.
For some stakeholders, I've called it "letting the machine learn the rules of the world from the data." It speaks to tech-adverse people that know computers can be better/faster/cheaper but don't understand the outcome may be to understand the world like humans would. Want to understand rules better? Get more data and find the right way to learn the rules!
I avoid calling ML anything about prediction, because I think people overfit to the assumption of time-based predictions already. I want people to think about unique problems like unsupervised or anomaly detection that doesn't fit in their mental model of a prediction.