In today’s episode, we welcome Manu Sharma and Brian Rieger from Labelbox, a private company which we believe is leading training data solution for machine learning. We have had many conversations on this show about artificial intelligence from a hardware and algorithm perspective, but data is just as important. All production AI systems are based on supervised learning, which requires large quantities of data to be labeled so that the algorithms can understand and compartmentalize it. In other words, data without labels can’t be used by most AI algorithms.

While large internet companies like Google and Facebook have built custom tools in-house to help label and sort through their large troves of data, most enterprises have very few options. Labelbox aims to fill this gap by providing a scalable and easy-to-use tool to help companies convert their raw data into labeled data fit for machine learning algorithms. Today on the show, Manu and Brian get into the history of Labelbox, as well as the services it provides to its clients and the machine learning community. We talk about the tiers and iterations of Databox, its pricing structures, the various industries it supports, and what makes it stand out against its competition. We also cover some fascinating ground around human-in-the-loop systems, how a machine learning startup would train its AI and the difference between software 1.0 and 2.0. In our conversation, we also speak about Labelbox in relation to computer vision, drone technology, and labor ethics. Join us to get a taste of the many ways data and AI will continue to penetrate life and industry well into the foreseeable future.