> but we're actually swimming in data ... I think this is a "looking under the lamppost for your keys because that's where the light is" situation. We hear abo

> but we're actually swimming in data ... I think this is a "looking under the lamppost for your keys because that's where the light is" situation. We hear about tons-of-data and deep learning because teams like Google/FB have problems that fit those situations well. This is not representative of the vast sea of learning applications most organizations face. I work on learning applications day-in-day-out. In a typical year I work with over 50 different organizations/projects. Almost none of them have data anywhere near what deep learning requires. Here are just a few examples from the past year: 1) optimizing recruiting pipeline. Maybe you have ~1000s of data points per year 2) medical billing applications: typically hundreds of data points per year 3) novel but slow-moving financial instruments, maybe 1 data point per day 4) automated sensor calibration where each run of the hardware costs you a few hundred dollars: depends on your budget, but thousands of data points in a year are representative I think Google/FB and the "web+mobile is everything hivemind" (I'm not saying that applies to you) have deeply distorted people's expectations for how much data is available to solve a typical problem.