Demystifying Artificial Intelligence for Digital Signal Processing Professionals

The fields of Digital Signal Processing (DSP) and Machine Learning (ML) have many math and technology building blocks in common.  For those working to understand the effectiveness of ML, these commonalities can be leveraged to create analogies that demystify the inner workings of the systems that transform speech into text, pixels into metadata, and beyond.

In this webcast, industry veteran Kari Grubin and Signiant CTO Ian Hamilton discuss AI and ML through the lens of DSP, detailing what can be learned from DSP, and how ML adds business value today.

Ian Hamilton has been an innovator and entrepreneur in Internet working infrastructure and applications for more than 20 years. As a founding member of Signiant, he has led the development of innovative software solutions to address the challenges of fast, secure content distribution over the public Internet and private intranets for many of the media and entertainment industries’ largest companies.

Kari Grubin is a multi-talented executive who has spent the past 20 years leading and managing studio divisions, global post production facilities, corporate departments and groups within trade organizations. Her strong record of technical operations supervision and confident, insightful leadership of creative teams led to her most recent position as Vice President of Mastering for Studio Operations at The Walt Disney Studios, where she developed a long-term strategic vision for mastering across the title lifecycle.

SMPTE presented this webcast as part of their Powering What’s Next series on August 18, 2020.


Simply enter your details below.

We appreciate your interest in Signiant!

Thank you for your interest in this webcast! This was just a primer! To learn more about Signiant and Machine Learning, watch Ian Hamilton’s five-part video, as referenced in the webcast. For additional info on what Signiant SaaS solutions are doing for media organizations, explore our blog and resource center.