IBM to invest US$240m to create an artificial intelligence lab with MIT
The lab aims to conduct advanced AI research and explore the implications of the technology for industries and society
IBM will invest US$240 million over 10 years in a partnership with Massachusetts Institute of Technology (MIT) to create an artificial intelligence (AI) laboratory, aiming to conduct advanced research and explore the implications of the technology on industries such as health care and cybersecurity as well as on society.
The investment in the MIT-IBM Watson AI Lab will support more than 100 scientists, professors and students in pursuing joint research at IBM’s research lab in Cambridge in the US state of Massachusetts and on the neighbouring MIT campus, IBM said in a statement.
“The field of artificial intelligence has experienced incredible growth and progress over the past decade. Yet today’s AI systems, as remarkable as they are, will require new innovations to tackle increasingly difficult real-world problems to improve our work and lives,” said Dr. John Kelly III, IBM senior vice-president, cognitive solutions and research, in the statement.
As part of the deal, MIT hopes the new lab could encourage MIT faculty and students to launch companies that will focus on commercialising AI inventions and technologies that are developed at the lab.
The lab’s scientists also will publish their work and contribute to the release of open source material.
The lab will be co-chaired by Dario Gil, vice-president of science and solutions at IBM Research, and Anantha Chandrakasan, dean of MIT’s School of Engineering.
IBM and MIT will seek proposals from researchers and scientists on their ideas, including on developing AI algorithms that can leverage big data and also learn from limited data to enhance human intelligence, as well as on developing new applications of AI for use in fields like health care and cybersecurity.
In 2016, IBM, the Broad Institute of MIT and Harvard University launched a five-year, US$50 million research initiative to use computational and machine learning methods to study drug resistance in cancers.
