Leveraging AI and ML in Industry w/ Jordan Reynolds, VP of AI at Rockwell Automation
AI Summary
Podcast Summary: Industry 4.0 Community Podcast with Walker Reynolds
Introduction
- Host: Walker D. Reynolds
- Guest: Jordan Reynolds, VP of Artificial Intelligence and Autonomy at Rockwell Automation
- Discussion: AI and machine learning in industry, commercial tools, and Rockwell Automation’s initiatives
Background
- Jordan Reynolds’ experience primarily with Calypso, acquired by Rockwell Automation
- Joined a Discord discussion on AI, providing insights on industry direction and tools
Main Discussion Points
- Foss AI and ML options
- Commercial AI/ML solutions
- Rockwell Automation’s AI/ML efforts
- Conversation flow based on Discord discussion and AI in industry
Key Takeaways
- AI and ML are predominantly used in autonomous vehicles, specifically in material handling
- Industry focus on making material handling autonomous
- Rockwell’s AI trains on user data and relies on machine-specific models for effective predictions
- Custom solutions using TensorFlow and deploying to edge devices are common but challenging
- Rockwell’s FactoryTalk Analytics Logix AI uses symbolic regression for Clos Loop control scenarios
- Deployment options for AI models include embedded hardware or virtualized containers
- Future integration of learning and adaptation functions into PLCs
Rockwell’s Position in AI/ML
- Emphasis on AI integrated with control systems, such as in autonomous vehicles
- Rockwell sees AI as a fundamental shift in the definition of control systems
- Rockwell’s response to adjacent market innovations is to adopt AI capabilities into their products
Engaging with Rockwell on AI/ML Solutions
- System integrators and partners can bring solutions to Rockwell’s attention through their partner network
- Rockwell’s offerings include Logic’s Edge for dedicated edge compute and Optics for general-purpose edge and HMI systems
- Rockwell encourages using open-source frameworks at the core of their products
Recommendations for Manufacturers Starting with AI/ML
- Start with focused experiments on mission-critical production functions to validate technical and economic feasibility
- Use open-source technology for initial experimentation
- Once feasibility is proven, invest in a cohesive platform for data management and contextualization
- Embrace digital transformation with a focus on IT/OT convergence and edge-to-cloud integration
Contact Information
- Jordan Reynolds can be reached via LinkedIn for further discussion or inquiries.