Brian Uzzi

Feb 19, 2024 | insight.kellogg.northwestern.edu | Benjamin Jones |Brian Uzzi |Dashun Wang

Scientists often make sense of the world by breaking it into parts, and then studying those parts. But a newer scientific discipline is examining the whole picture—with the help of big data and artificial intelligence. It’s called complexity science. Complexity science is the study of systems that are…well, complex! Think: the human brain, the global climate, ecosystems, and the universe. It’s all about zooming out from one part to see how all the parts work in a system.

Feb 12, 2024 | insight.kellogg.northwestern.edu | Benjamin Jones |Brian Uzzi |Dashun Wang

Scientists, trying to make sense of a complicated world, have historically tried to simplify things. For instance, running an experiment generally involves holding a lot of variables constant, and then tweaking one or two at a time to see what happens. But over time, as technology has improved, we have gotten better at collecting and analyzing data. A lot better. In response, a new approach to making sense of complicated phenomena—a new scientific discipline, really—has started to take off.

Dec 16, 2023 | insight.kellogg.northwestern.edu | Brian Uzzi

Everyone’s doing it. Or at least that’s how it feels when it comes to harnessing AI for your business. The reality is that businesses across industries span the entire spectrum of AI strategy, with some executing fully on several integrated fronts while others are figuratively scratching their heads about what to do with these new technologies, whether GPT-4 or machine-learning-based models. There’s good reason for all the action around AI.

Dec 7, 2023 | forbes.com | Brian Uzzi

Everyone’s doing it. Or at least that’s how it feels when it comes to harnessing AI for your business. The reality is that businesses across industries span the entire spectrum of AI strategy, with some executing fully on several integrated fronts while others are figuratively scratching their heads about what to do with these new technologies, whether GPT-4 or machine-learning-based models. There’s good reason for all the action around AI.

Jul 1, 2023 | insight.kellogg.northwestern.edu | Brian Uzzi

Instead, they discovered that training the algorithm on the text of a paper was a more effective way of predicting replication. “A paper may have 5,000 words and only five important numbers,” Uzzi says.