Jacopo Buongiorno
Contact
Ariel Furst
Quantum technologies are transitioning from laboratory curiosity to technical reality. Today, small-scale quantum computers with 53 qubits have been demonstrated, but what can they do? What kinds of business opportunities exist and what are the challenges ahead? How and when should my company get engaged?
MIT’s Industrial Liaison Program (ILP) and the Center for Quantum Engineering (CQE) are pleased to present a special webinar and panel discussion with business leaders and researchers in quantum computing hardware and software sharing their insights on the current lay of the land, opportunities, and challenges of quantum computing.Â
We feature leaders from industry members of the CQE Quantum Science and Engineering Consortium (QSEC), the CQE industry membership group. QSEC membership enables industries to engage with MIT faculty, students, and each other to explore the promise and applications of quantum technologies.
Assistant
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.