It’s All About the Features
We're an AI company, so people always ask about our algorithms. If we could get a dollar for every time we're asked about which flavor of machine learning we use –convolutional neural nets, K-means, or whatever – we would never need another dollar of VC investment ever again.
But the truth is that algorithms are not the most important thing for building AI solutions — data is. Algorithms aren't even #2. People in the trenches of machine learning know that once you have the data, It's really all about "features."
In machine learning parlance, features are the specific variables that are used as input to an algorithm. Features can be selections of raw values from input data, or can be values derived from that data. With the right features, almost any machine learning algorithm will find what you're looking for. Without good features, none will. And that's especially true for real-world problems where data comes with lots of inherent noise and variation.
My colleague Jeff (the other Reality AI co-founder) likes to use this example: Suppose I'm trying to detect when my wife comes home. I'll take a sensor, point it at the doorway and collect data. To use machine learning on that data, I'll need to identify a set of features that help distinguish my wife from anything else that the sensor might see. What would be the best feature to use? One that indicates, "There she is!" It would be perfect — one bit with complete predictive power. The machine learning task would be rendered trivial.
