ML and AI as Distinct Control Systems in Heavy Industrial Settings // Richard Howes // #243
Episode description
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ML and AI as Distinct Control Systems in Heavy Industrial Settings // MLOps podcast #243 with Richard Howes, CTO of Metaformed.
Richard Howes is a dedicated engineer who is passionate about control systems, whether it be embedded systems, industrial automation, or AI/ML in a business application.
Huge thank you to AWS for sponsoring this episode. AWS - https://aws.amazon.com/
// Abstract
How can we balance the need for safety, reliability, and robustness with the extreme pace of technology advancement in heavy industry? The key to unlocking the full potential of data will be to have a mixture of experts, both from an AI and human perspective, to validate anything from a simple KPI to a Generative AI Assistant guiding operators throughout their day. The data generated by heavy industries like agriculture, oil & gas, forestry, real estate, civil infrastructure, and manufacturing is underutilized and struggles to keep up with the latest and greatest - and for good reason. They provide the shelter we live and work in, the food we eat, and the energy to propel society forward. Compared to the pace of AI innovation, they move slowly, have extreme consequences for failure, and typically involve a significant workforce. During this discussion, we will outline the data ready to be utilized by ML, AI, and data products in general, as well as some considerations for creating new data products for these heavy industries. To account for complexity and uniqueness throughout the organization, it is critical to engage operational staff, ensure safety is considered from all angles, and build adaptable ETL needed to bring the data to a usable state.
// Bio
Richard Howes is a dedicated engineer who is passionate about control systems, whether it be embedded systems, industrial automation, or AI/ML in a business application. All of these systems require a robust control philosophy that outlines the system, its environment, and how the controller should function within it. Richard has a bachelor's of Electrical Engineering from the University of Victoria, where he specialized in industrial automation and embedded systems. Richard is primarily focused on the heavy industrial sectors like energy generation, oil & gas, pulp/paper, forestry, real estate, and manufacturing. He works on both physical process control and business process optimization using the control philosophy principles as a guiding star.
Richard has been working with industrial systems for over 10 years, designing, commissioning, operating, and maintaining automated systems. For the last 5 years, Richard has been investing time into the data and data science-related disciplines, bringing the physical process as close as possible to the business, taking advantage of disparate data sets throughout the organization. Now with the age of AI upon us, he is focusing on integrating this technology safely, reliably, and with distinct organizational goals and ROI.
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AWS Inferentia: https://aws.amazon.com/machine-learning/inferentia/
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