One of the most interesting recent trends in machine learning has been the combination of different types of data in order to be able to unlock new use cases for deep learning. If the 2010s were the decade of computer vision and voice recognition, the 2020s may very well be the decade we finally figure out how to make machines that can see and hear the world around them, making them that much more context-aware and potentially even humanlike.

The push towards integrating diverse data sources has received a lot of attention, from academics as well as companies. And one of those companies is Twenty Billion Neurons, and its founder Roland Memisevic, is our guest for this latest episode of the Towards Data Science podcast. Roland is a former academic who’s been knee-deep in deep learning since well before the hype that was sparked by AlexNet in 2012. His company has been working on deep learning-powered developer tools, as well as an automated fitness coach that combines video and audio data to keep users engaged throughout their workout routines.