A forthcoming Management Science paper from researchers at Olin Business School draws a direct connection between language translation driven by artificial intelligence and an increase in international trade. Analyzing data from online e-commerce site eBay, the paper is among the earliest tangible signs that AI and machine learning are living up to their promise.
Two Olin Business School faculty members at Washington University in St. Louis collaborated to create a new customer choice algorithm designed to better populate six available slots for products in online stores hosted by Alibaba. Their model produced 28% higher revenues in one week — or nearly $22 million. The study earned them the 2019 Olin Award.
Two Olin Business School researchers at Washington University in St. Louis are highlighted in a new federal report issued March 27 showing how U.S. farmers — facing a surge of weather events and disease outbreaks — can increase production and revenues with innovations produced by government-funded agricultural research.
Washington University in St. Louis, in partnership with The Climate Corporation, a subsidiary of Bayer, are working to explore unique new technologies to advance the science behind hybrid selection & placement.
Like our eyes, microscopes are limited in what they can see because of their resolution, or their ability to see detail. An engineer at Washington University in St. Louis plan to use funding from the National Science Center to build a more precise microscope.
Where machine learning meets spring planting and big data intersects with farming big and small, two Olin Business School researchers have devised a computational model so farmers and seedmakers could take the guesswork out of which particular variety of, say, soybean to plant each year.
Kilian Q. Weinberger, assistant professor of computer science & engineering in the School of Engineering & Applied Science, has won a prestigious Faculty Early Career Development Award (CAREER award) from the National Science Foundation. Weinberger’s CAREER project, “New Directions for Metric Learning,” seeks to solve one of the fundamental problems of machine learning: how to compare individual texts, images or sounds.