Getting real with Deep Learning

It was nearly 30 years ago that I first got infatuated with Artificial Intelligence (AI) and I ended up focusing both my undergraduate and graduate engineering research on applications of Artificial Neural Networks (ANNs). My first two jobs after graduate school stayed in the same groove; over 6 years I developed AI and machine learning techniques to address real world problems that ranged from recognizing human speech and natural language, to converting handwriting to searchable digitized text, and to streamlining maintenance procedures in nuclear reactor cores. So it is with a mix of amazement and amusement that I am soaking up the resurgence of AI and machine learning as the buzzword-du-jour: "Deep Learning". Deep Learning is very visible in the high hopes we hold for driverless cars and in the triumph of machines over chess champions. It is less conspicuously and more frequently used in the form of Apple's Siri, Amazon's Echo, playlists generated on Spotify, that auto-tag feature on Facebook Photos, the voice assistant that answers the phone when you call your bank, or when your fingerprint is recognized by a machine.