The 17th BBVA Foundation Frontiers of Knowledge Award in Information and Communication Technologies has been awarded jointly to IMS Fellow Michael I. Jordan (University of California, Berkeley, and National Institute for Research in Digital Science and Technology, INRIA, Paris) and to Anil Jain (Michigan State University), for what the committee refers to as their “core contributions” to machine learning, which have unlocked “applications of far-reaching impact on society.” Their contributions have left “an indelible stamp on the fabric of today’s—and tomorrow’s—information-rich society.”
Over the last four decades, the two awardees have made vital contributions enabling computers to recognize patterns and generate predictions from large-dimension data sets, powering the advance of such transformative technologies as biometrics and AI.
Jain’s research has focused on pattern recognition, leading to “monumental contributions”—in the words of the committee—in recognizing people through fingerprints or face ID, with technologies that are now massively deployed in the security domain, both in criminal investigations and for accessing mobile phones and other electronic devices.
In parallel, Jordan’s independent efforts in the machine learning field “provided unified algorithms for statistical and probabilistic inference,” said the committee, “enabling computers to make accurate predictions from observed data.” His achievements laid the mathematical foundations for generative AI models such as those powering ChatGPT, and the development of recommender systems like Amazon’s, that inform the economic decision-making of both consumers and businesses.
Michael I. Jordan began his research career looking at the models used to establish probabilistic relations between different variables, which are a key component of text and image analysis and recommendation systems. In the 1990s, he was at the forefront of the development of variational inference models, able to approximate the solution to a mathematical problem that is not solvable with available computational resources, by reducing it to an optimization problem. This technique is a core component of machine learning, particularly deep learning applications like generative AI. Later, Jordan turned to multiplying the possibilities of machine learning by running programs on hundreds or thousands of computers instead of just one. The algorithms he devised to enable such large-scale distributed computing led to the setup of the company Anyscale, whose Ray platform is the basis for ChatGPT, numerous e-commerce firms and many other deep learning applications.
Among Jordan’s more recent interests has been the application of machine learning to economics. In contexts where multiple actors are entrusting decisions to the same system, recommender systems must be able to adapt to avoid congestion. For instance, a GPS app being used in a town with hundreds of thousands of inhabitants could recommend the same route to the airport to a thousand users at once, causing traffic hold-ups. Jordan is working to develop machine learning systems that overcome this problem.