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AI is a tool to get things done. To use it properly and generate value, organizations need the right capabilities — including a good understanding of data.
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Machine learning is a powerful form of artificial intelligence that is affecting every industry. Here’s what you need to know about its potential and limitations and how it’s being used.
Researchers launched an in-house Data Science and Artificial Intelligence (DSAI) challenge to beat MIT’s machine-learning models for predicting clinical trial outcomes. The results are now available.
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MIT Sloan and CSAIL researchers apply artificial intelligence techniques to one of the largest datasets of clinical trial outcomes to handicap the drug and device approval process
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In a new book, MIT roboticist Daniela Rus looks at the powers and limitations of robots and how humans can work with them to unlock new capabilities.
Much has been written about the potential impact of generative AI in the workplace, but the perspective of one set of key stakeholders has often been left out: workers themselves. What’s more, research has found that incorporating the perspective of end users such as workers into the development an...
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How does access to a generative AI tool affect work in a call center? That was a research question addressed by MIT Sloan Professor Danielle Li at a recent session of the MIT Institute for Work and Employment Research (IWER) weekly seminar series.
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Companies make common data science mistakes. Here’s an expert’s guide to what they are and how to avoid them.
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Matt Beane, SM ’14, PhD ’17, argues those using artificial intelligence will become incrementally de-skilled unless they are consciously upskilling at the same time.
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Integrating robots into a manufacturing system is often prohibitively expensive. A new approach could change that.
