Richard Howes, a passionate engineer specializing in control systems, discusses balancing safety and technology advancement in heavy industrial settings. The podcast explores utilizing AI and ML for quality assurance, the role of business stakeholders in implementing AI systems, the importance of clear diagrams and SOPs, integrating external data sources for analysis, and challenges faced by data engineers in data management.
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Quick takeaways
Utilizing a mixture of experts from AI and human perspectives to validate data ensures safety and unlocks full potential.
In heavy industries, understanding control systems is essential for accurate data analysis and problem-solving.
Deep dives
Improving Data Science Outputs through Context Stuffing
Providing data scientists with extensive information about the system, business processes, and optimization objectives can enhance the quality of their exploratory data analysis (EDA) and algorithm development. Context stuffing akin to loading an LLM can align data scientists with comprehensive insights, ensuring a thorough understanding to drive efficient outcomes.
Significance of Control Systems in Data Interpretation
Understanding control systems is crucial for accurate data analysis and interpretation in heavy industries. Harnessing control philosophy documents can streamline operational principles, control narratives, and error management, guiding data scientists towards optimized problem-solving. By incorporating a control philosophy approach, teams can align on project objectives, nurture collaboration, and challenge assumptions effectively.
Leveraging AI for Preventative Maintenance in Heavy Industries
In heavy industries like oil and gas, predictive maintenance powered by AI plays a pivotal role in monitoring equipment conditions, scheduling replacements, and optimizing operational downtime. Attempting to predict machinery failures before they occur, AI-driven solutions enhance asset performance and aid in strategic equipment planning for efficient maintenance practices.
Transformative Applications of Satellite Imagery in Site Assessment
The integration of remote sensing technologies, including satellite imagery, seismic exploration, and LiDAR data, revolutionizes site evaluations in industries like forestry and energy. By leveraging AI algorithms to analyze vast amounts of environmental data swiftly, organizations can extract valuable insights for site suitability assessments, resource exploration, and ecological monitoring, accelerating decision-making processes and reducing manual labor constraints.
Join us at our first in-person conference today all about AI Quality: https://www.aiqualityconference.com/
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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// Related Links
AWS Trainium: https://aws.amazon.com/machine-learning/trainium/
AWS Inferentia: https://aws.amazon.com/machine-learning/inferentia/
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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Richard on LinkedIn: https://www.linkedin.com/in/richardhowes/
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