by Admin | Apr 13, 2022 | Cornerstones, News
Edward Feigenbaum is the Kumagai Professor of Computer Science and Co-Scientific Director, Knowledge Systems Laboratory at Stanford University. He made several contributions to the development of AI, becoming a co-recipient of the Turing Award in 1994. He is considered “the father of expert systems”. Joining Stanford University in 1965, he became one of the founders of its Computer Science Departments. His bibliography includes Computers and Thought and The Handbook of Artificial Intelligence.
Professor Feigenbaum was elected to the National Academy of Engineering in 1986. In the same year, he was elected to the Productivity Hall of Fame of the Republic of Singapore. He is an elected Fellow of the American Association for Artificial Intelligence, the honorary American College of Medical Informatics. He was elected to the American Academy of Arts and Sciences in 1991. He is the first recipient of the Feigenbaum Medal, an award established in his honor by the World Congress of Expert Systems. He was elected Fellow to the American Institute of Medical and Biological Engineering in January 1994. He received the U.S. Air Force Exceptional Civilian Service Award in 1997.
The History of AI Board warmly welcomes Professor Edward Feigenbaum.
by Admin | Jan 9, 2022 | Cornerstones, News
Delphi, a MIT-Janssen’s COVID-19 Forecast Model – MIT researchers and scientists at Janssen Research & Development (Janssen) leveraged real-world data and applied artificial intelligence and machine learning (AI/ML) to help guide the company’s research efforts into a potential vaccine.
When the World Health Organization declared COVID-19 a pandemic in March 2020 and forced much of the world into lockdown, Bertsimas, who is also the faculty lead of entrepreneurship for the Jameel Clinic, brought his group of 25-plus doctoral and master’s students together to discuss how they could use their collective skills in machine learning and optimization to create new tools to aid the world in combating the spread of the disease.
The group started tracking their efforts on the COVID Analytics platform, where their models generate accurate real-time insight into the pandemic. One of the group’s first projects was charting the progression of COVID-19 with an epidemiological model they developed named DELPHI, which predicts state-by-state infection and mortality rates based upon each state’s policy decision using an expanded SEIR model. A key innovation of the model is capturing the behaviors of people related to measures put into place during the pandemic, such as lockdowns, mask-wearing and social distancing, and the impact these had on infection rates.
“By June or July, we were able to augment the model with these data. The model then became even more accurate,” Bertsimas says. “We also considered different scenarios for how various governments might respond with policy decisions, from implementing serious restrictions to no restrictions at all, and compared them to what we were seeing happening in the world. This gave us the ability to make a spectrum of predictions. One of the advantages of the DELPHI model is that it makes predictions on 120 countries and all 50 U.S. states on a daily basis.”
