Human learning and understanding involve strategies ranging from rote memorization to analogy to first principle decomposition. On this pyramid of learning strategies, human rote memorization is typically viewed as a necessary but NOT sufficient component of true learning. Computers are so good at storing and retrieving information that the word learning is rarely if ever used to describe machine memory. Analogy, on the other hand, can be a powerful stepping stone on an efficient path to fundamental first principles-based understanding.

The fields of Digital Signal Processing and Machine Learning (ML) share many common math and technology building blocks. Linearity, for example, is central to both these disciplines. Commonalities between the two disciplines can be leveraged to create analogies that demystify the inner workings of ML systems that transform speech into text and pixels into metadata.

Our host Kari Grubin and Signiant CTO Ian Hamilton will discuss AI and ML, what can be learned from digital signal processing, and how ML adds business value today. The conversation will be interspersed with audience questions as well as introduce some practical applications of ML in M&E.