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J.P. Morgan Names Prominent Academic to Lead Artificial Intelligence Research

Date: May 14, 2018 @ 08:00 AM

J.P. Morgan announced that Dr. Manuela Veloso will be joining the Corporate & Investment Bank as the head of Artificial Intelligence (AI) Research. Veloso is currently the head of the Machine Learning Department at Carnegie Mellon University, widely considered the preeminent institution in the field.  U.S. News & World Report ranks it #1 both for computer science graduate studies and for artificial intelligence.

J.P. Morgan has already started to apply machine learning technology across its businesses and functions, and this expanded effort will be aimed at identifying further opportunities. A.I. is among the areas of investment within the bank’s annual technology budget of $10.8 billion, with more than half earmarked for new investments.

Veloso was named a Carnegie Mellon University Professor in 2014, the highest academic accolade bestowed by the university, and holds the Herbert A. Simon Chair in computer science. She has been honored as an Einstein Chair Professor by the Chinese Academy of Sciences. She is a past president of the Association for the Advancement of Artificial Intelligence (AAAI) and of the RoboCup Federation. She is a fellow of the AAAI, IEEE, the Association for Computing Machinery, and the American Association for the Advancement of Science.

“We have assembled talented teams to drive innovation in artificial intelligence, blockchain technology, big data, machine learning and bots, with the objectives of improving our efficiency and enabling us to serve more clients with greater effectiveness, depth and sophistication,” said Daniel Pinto, the bank’s co-President and head of the Corporate & Investment Bank, in this year’s shareholder letter.

A recent J.P. Morgan research report focused on AI adoption noted that the concept of A.I. has been around for over 50 years, but we are now at a pivotal point for its adoption due to the availability of big data, high-powered computing and advances in algorithms – which all make AI cheaper and faster to implement. Read a summary of the latest research and trends.

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