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. Through research and entrepreneurship, MIT Professor Devavrat Shah is helping to design methods that can handle constant decision-making using limited computational resources. Previous image Next image 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.
Key Takeaways
- 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 methods that can handle second-by-second decision-making using limited computational resources.
"In a sense, with a small amount of resource, you have to do a lot of heavy lifting," he says.
- In 2019, he also co-founded a spinoff company called Ikigai Labs.
Ikigai built a foundation model for tabular, time series data based on years of research in Shah's lab, which was patented and licensed by MIT to the company.
- While most AI models have been taught using text and images, this system takes tabular data as its input - structured data such as the familiar kind of row-and-column format used in spreadsheets.
And then it provides the kind of real-time planning, on a vastly larger scale.
- "Let's say you're making headphones and all sorts of different things.
And each of the products that you manufacture has lots of small pieces that come from different parts of the world.
- He adds that all of these processes are interdependent, and at every stage of the processes decisions have to be made that have implications over time.
Through research and entrepreneurship, MIT Professor Devavrat Shah is helping to design methods that can handle constant decision-making using limited computational resources. Through research and entrepreneurship, Professor Devavrat Shah is helping to design methods that can handle constant decision-making using limited computational resources. Previous image Next image 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 methods that can handle second-by-second decision-making using limited computational resources. "In a sense, with a small amount of resource, you have to do a lot of heavy lifting," he says. As a researcher, "my interest is in the ability to develop methods that can extract information from data at scale in as effective a manner as possible."
The Andrew (1956) and Erna Viterbi Professor has been teaching at MIT since 2005. In 2019, he also co-founded a spinoff company called Ikigai Labs. Ikigai built a foundation model for tabular, time series data based on years of research in Shah's lab, which was patented and licensed by MIT to the company.
For more details please read the original article at MIT News AI.
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