In the fast-evolving world of manufacturing, AI-powered quality inspection is transforming how factories evaluate quality and drive process improvements.

This talk will examine how the AI technologies within Eigen's OneView Edge and OneView Cloud products are transforming real-time quality control across a wide range of manufacturing sectors.

From real-time AI-powered quality decisions at the edge, to advanced AI-powered analytic tools in the cloud, OneView is allowing manufacturers to go beyond defect detection with informative and actionable insights.

OneView’s technology enables a closed-loop, adaptive quality control system that goes beyond defect detection, transforming quality inspection into intelligent, proactive quality management—amplifying AI’s role in manufacturing efficiency and precision.

At Eigen, innovation is at the heart of everything we do, and Josh is instrumental in turning that vision into reality. As a key figure in the development of smart vision technologies for the smart factory, he plays a crucial role in advancing our brand’s promise.

With a deep passion for automation and AI, combined with a Ph.D. in mechanical systems design and error analysis, Josh leads the charge in pioneering cutting-edge technologies.

His work focuses on industrial vision system design, synthetic data generation, and AI-driven quality inspection—pushing the boundaries of what’s possible in the manufacturing sector.

From developing novel image standardization techniques to leveraging explainable AI, Josh's contributions are unlocking the full potential of machine vision for factories worldwide.

As the manager of Eigen’s OneView product, he collaborates closely with the Software Engineering and Customer Solutions teams to drive product development.

Additionally, Josh oversees research partnerships with top academic institutions specializing in computer vision, machine learning, robotics, and manufacturing, ensuring that Eigen remains at the forefront of technological advancements.

In this paper, we describe human and machine-learning approaches to preserve, digitize, catalogue, interpret, and make accessible an endangered Afro-Colombian archive (the 61,000 records of the Circuit Court of Istmina Archive from 1870 to 1930) which, until 2022, was in garbage bags in a state of disarray.

After outlining both approaches, we argue for a non-extractive synthesis that emphasizes situated knowledge production and social intelligence so that machine learning magnifies, rather than replaces, the skills of local archivists.

Daniel Tubb is an Associate Professor and Chair of Anthropology at the University of New Brunswick. Tubb is a sociocultural and environmental anthropologist, and he has written a book and articles on Colombia, small-scale mining, violence, and citizenship, as well as an edited volume on speculative non-fiction with 37 community authors in New Brunswick. Currently, he is working on new books and research projects in Colombia.

Reclaiming productivity depend on Canadian companies bien able to access knowledge, expertise, talent, and capital they need to digitize, streamline, and innovate.

Through research partnerships and funding initiatives, Mitacs is enabling collaborations between academia and industry to develop AI-driven solutions that address Canada's productivity challenges.