You don't need"Big Data" to apply deep learning

Disclaimer: The following is based on my observations of machine learning teams -- not an academic survey of the industry. For years, the biggest bottleneck to production deep learning was simple: we needed models that worked. And over the last decade--thanks to companies with access to unprecedented amounts of data and computer power, as well as new model architectures--we've largely cleared that hurdle. We may not have fully autonomous vehicles or Bladerunner-esque AI, but when you call an Uber, you get an accurate ETA prediction. When you open an email in Gmail, you get a contextually appropriate suggestion from Smart Compose.