Daniela Rus, faculty director, MIT CSAIL.
Machine learning (ML) is positioned to solve a range of supply chain challenges today, but commercializing ML applications lags behind research. Businesses that stand to gain from ML solutions are helping accelerate that process.
Last week, MIT’s Computer Science Artificial Intelligence Lab (CSAIL) research alliance launched, via webinar, MachineLearningApplications@CSAIL to develop applications for the latest ML technologies, research challenges limiting ML, and provide professional development for the digital workforce. Covid-19 has also spurred the effort as face-to-face commerce is discouraged.
For example, chatbots are saving the auto insurance industry during the pandemic. Powered by machine learning, digital insurance platforms research applicants’ driving records, analyze data, apply risk metrics to coverage and pricing, and issue policies remotely, according to Professor Daniela Rus, faculty director for CSAIL.
Chatbots already use ML to recommend products, optimize pricing and create classifications within systems. More broadly, enterprises use ML for energy savings and predictive analytics. Machine learning and AI have the potential to transform business processes, Rus said.
CSAIL’s “no idea is too crazy” approach has already yielded numerous breakthroughs in computing technology. MIT’s AI efforts date back to 1959 and its AI Lab pioneered methods in image-guided surgery, language-based web access, micro displays and robotics. CSAIL is the merger between the AI Lab and MIT’s Laboratory for Computer Science (LCS), founded in 1963. The LCS is known for the development of the Compatible Time-Sharing System (CTSS) and Multics.
CSAIL currently oversees more than 60 research groups working on hundreds of projects. Businesses that support these efforts have access to leading-edge research, among other benefits.
For the rest of this article, please see EPSNews sister publication, EETimes.
