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Professor Lilia Maliar (GC/Economics) was awarded a $308,000, three-year grant from the National Science Foundation (NSF) for her research on using artificial intelligence to analyze complex and highly dimensional economic models.
Her research has interesting applications in FinTech and beyond.
Maliar is developing a deep learning framework that makes it possible to cast a broad class of economic models into a form that is suitable for intelligent machines — an approach that opens a new path in economic research. “AI has many impressive applications like image and speech recognition, composing music, and playing chess and Go”
Deep learning methodologies like supervised learning have been very successful in training machines to make predictions about the world. But because they’re so dependent upon large amounts of human-annotated data, they’ve been difficult to scale. Dr. Phil Bachman, a researcher at MSR Montreal, would like to change that, and he’s working to train machines to collect, sort and label their own data, so people don’t have to.
Today, Dr. Bachman gives us an overview of the machine learning landscape and tells us why it’s been so difficult to sort through noise and get to useful information. He also talks about his ongoing work on Deep InfoMax, a novel approach to self-supervised learning, and reveals what a conversation about ML classification problems has to do with Harrison Ford’s face.
To develop an Artificial Intelligence (AI) system that can understand the world around us, it needs to be able to interpret and reason about the world we see and the language we speak. In recent years, there has been a lot of attention to research at the intersection of vision, temporal reasoning, and language.
One of the major challenges is how to ensure proper grounding and perform reasoning across multiple modalities given the heterogeneity resides in the data when there is no or weak supervision of the data.