“Separating Signal from Noise” explores the mechanics of machine learning at Signiant
Signiant’s CTO, Ian Hamilton, presents “Separating Signal From Noise” a discussion on artificial intelligence and machine learning, what can be learned from digital signal processing, and how machine learning adds business value today. The presentation is broken into the following five segments: Introduction, Neural Networks, Digital Signal Processing, Machine Learning and Digital Signal Processing, and Machine Learning at Signiant to help you better understand digital signal processing.
People vs Computers…How We Learn
While humans often learn best from analogy, computers excel at memorization and pattern recognition. The latter makes up much of the foundation of ML. In this introductory session, Ian describes how the video segments in this series leverage analogy and parallel perspectives as tools to build intuition into how machines learn.
Neural Networks
This second segment provides a high-level overview of neural networks–a common building block in most of today’s machine learning systems. This overview describes what neural networks are and the basic math behind them. People already familiar with these subjects might want to proceed to the following section.
Digital Signal Processing
This third segment provides an overview of digital signal processing. This overview describes digital signals and operations commonly performed on them. The simple math behind digital signal processing operations is also covered. People already familiar with Digital Signal Processing operations like correlation and transformation and digital filtering using convolution may want to skip this segment and go directly to the next segment.
Machine Learning and Digital Signal Processing
This fourth segment expands on the previous two segments to explain how machines learn. This segment highlights similarities between neural networks and digital signal processing including why artifacts in these domains can be unintuitive. This segment establishes a basis for trusting neural networks.
Machine learning at Signiant
Signiant helps M&E companies leverage machine learning today both directly and indirectly, directly by leveraging machine learning in our patent-pending intelligent transport architecture. That architecture looks at a variety of inputs from both current and historical network conditions to determine the best possible path to move a particular data set over a network under the current conditions. Signiant also enables the use of machine learning indirectly. With our unique SDCX architecture, companies can leverage cloud ML and AI tools across multiple cloud providers without having to move the heavy assets into and out of the cloud.
Enjoy the video, and for those who would like to learn more about Signiant’s technology, please contact us to set-up a discussion.
