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This article focuses on the development of foundation models specifically designed for tabular and time-series data, addressing the critical gap where standard AI tools lack specific organizational context. Professor Shah's work at MIT and his spinoff, Ikigai Labs, demonstrate how graphical models can extract scalable insights from structured enterprise data to enable real-time decision-making with limited computational resources. This represents a significant advancement in machine learning infrastructure beyond the dominant text-and-image paradigm.
Helping AI models to meet the real world
Through research and entrepreneurship, Professor Devavrat Shah is helping to design methods that can handle constant decision-making using limited computational resources.
Systems using artificial intelligence to enhance forecasting, planning, and decision-making in businesses have been proliferating in recent years, but in many cases, they lack the detailed, specific information about the organization itself, limiting the usefulness of those tools. Devavrat Shah, a principal investigator at MIT’s Laboratory for Information and Decision Systems (LIDS), faculty member with the department of Electrical Engineering and Computer Science (EECS), and member of the Institute for Data, Systems, and Society (IDSS), has been focused on how to design…
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