Vaccines are an especially risky proposition for pharmaceutical manufacturers since the costs are higher than for other types of drugs and demand could drop drastically once a crisis is over.
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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Integrating robots into a manufacturing system is often prohibitively expensive. A new approach could change that.
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MIT researchers have developed a methodology for systematically tracking the outcomes of academic licensing transactions as a means of increasing funding to bridging the “valley of death.”
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A new study led by MIT Sloan Prof. Andrew W. Lo finds that borrowing ideas and tools from the gaming community can improve online teaching techniques and improve learning outcomes for students.
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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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Cloud misconfigurations, more sophisticated ransomware, and exploitation of vendors are contributing to rising cyberattacks.
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Organizations must base their cybersecurity strategies on resilience — the ability to weather an attack with minimal damage to data, finances, and reputation.
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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.
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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.
