Engineers, who know systems and processes, are generally separated from operators, who are often only trained on specific machines. New manufacturing technologies, whether in robotics or digital production, are transforming factory floors. Advanced manufacturing requires workers with a technician’s practical know-how and an engineer’s comprehension of processes and systems. Companies that want to move into advanced manufacturing often struggle to find people who know how to integrate technologies to optimize the whole system, manage technological advances, and drive innovation. We call this worker the “technologist.” As advanced technological manufacturing progresses, technologists will be essential in the adoption of next-generation factory systems. We believe that training programs for technologists can empower both incumbent and aspiring workers to be knowledgeable, productive, and adaptable contributors to a more robust US manufacturing economy (Liu & Bonvillian, 2024). MIT is excited to provide pathways for employees to advance in their careers, create training that allows companies to fill key roles, and build a workforce that will strengthen America’s industrial base.
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Ariel Furst Assistant Professor, MIT Department of Chemical Engineering
Ariel Furst
Tackling Climate Change with Machine Learning Priya L. Donti Assistant Professor, MIT Department of Electrical Engineering and Computer Science Assistant Professor, MIT Laboratory for Information & Decision Systems Co-founder and Chair, Climate Change AI
Climate change is one of the greatest challenges that society faces today, requiring rapid action from across society. In this talk, I will describe how machine learning can be a potentially powerful tool for addressing climate change when applied in coordination with policy, engineering, and other areas of action. From energy to agriculture to disaster response, I will describe high-impact problems where machine learning can help through avenues such as distilling decision-relevant information, optimizing complex systems, and accelerating scientific experimentation. I will also describe key considerations for the responsible development and deployment of such work. While this talk will primarily discuss opportunities for machine learning to help address climate change, it is worth noting that machine learning is a general-purpose technology that can be used for applications that both help and hinder climate action. In addition, machine learning has its own computational and hardware footprint. I will therefore briefly present a framework for understanding and contextualizing machine learning’s overall climate impacts, and describe associated considerations for machine learning research and practice as a whole.