Preliminary Program
Artificial Intelligence in Manufacturing: From Roadblocks and Pitfalls to
Strategy and Roadmap
Jesus Ruvalcaba, Manufacturing & Business Intelligence Developer • American Woodmark Corp.
Over the past several years, the race to adopt Artificial Intelligence (AI) technologies has increased tremendously while the success rate remains low. Some organizations have yet to implement AI for the first time...Learn More
Machine Learning Approaches to Set Up an AI-Based Robotic Manufacturing Control System
Arnie Kravitz, Chief Technology Officer • Advanced Robotics for Manufacturing (ARM)
Advanced Robotics for Manufacturing (ARM) is the nation’s leading collaborative in robotics technology and workforce innovation. Structured as a public-private partnership, ARM works with over 230 organizations nationwide...Learn More
The Holistic Effort of Academia and Industry in Driving AI Technology within the Manufacturing Sector
Konrad Konarski, Chairman and Trustee • AI Innovation Consortium
The AI Innovation Consortium is focused on evolving AI technology across different industrial sectors including manufacturing. Hear a holistic effort of academia and industry in driving AI technology within the manufacturing sector...Learn More
Artificial Intelligence in Manufacturing: Case Studies on Predictive Maintenance and Predictive Quality
Mo Abuali, Managing Partner • IoTco
A reduction in unplanned downtime or a few percentages of scrap reduction can yield millions of dollars in savings for manufacturers. This compelling value proposition has resulted in significant investment and interest in using predictive maintenance...Learn More
Improving the Quality Control Process
Ying Fei, Product Manager • Google
Visual quality control is an important manufacturing work process to ensure product quality. Currently many manufacturing companies still take a manual approach to performing these tasks which makes the work process inefficient...Learn More
Making AI Work for Manufacturers
Seth Earley, CEO • Earley Information Science (EIS)
AI has been getting its fair share of inflated and unrealistic expectations due to a lack of broad understanding by both software vendors and customers. Software tools can be extremely powerful, however the services, infrastructure...Learn More
Evolving IIoT Implementations Through Practical AI Solutions
David Austin, Senior Principal Engineer • Intel Corporation
Industrial AI-based solutions often struggle to keep up with the latest technological innovations, as AI technology and understanding continues to advance at a rapid pace, while typical product development and validation cycles trail behind...Learn More
Machine Learning for Materials Discovery
Alex van Grootel, Data Scientist • Citrine Informatics
Machine Learning (ML) has been applied to many manufacturing areas such as predictive maintenance or forecasting. However, ML for R&D has not seen as much attention...Learn More
Build Your Business Case for AI
Zeydy Ortiz, Ph. D.CEO & Chief Data Scientist • DataCrunch Lab, LLC
Rob Montalvo Co-Founders • DataCrunch Lab
Now that you have an idea on the use of artificial intelligence in manufacturing, how do you move forward? In manufacturing, the most significant impact is reported as cost savings due to optimizing yield, energy, and throughput...Learn More
Manufacturing the Future: How to Add Edge to the Build
Todd Gurela, Senior Director, Manufacturing IoT • Cisco
Some of the biggest challenges facing architects and administrators deploying IoT is the sheer scale and complexity of managing the tens of billions of devices expected to come online in the next few years and doing so in a secure manner...Learn More
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