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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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