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Q. How is AI changing the way research is done? What does that mean for Columbia?

A. In traditional computing, people write programs. In machine learning, people feed the computer data, and the computer itself writes the program; itlearnsthe program from data. The termmachine learningis germane here. The machine learns the rules on its own. Because the machine, not the human, is writing the program, the program is not easily interpretable to us. In the case of deep learning, the most successful machine-learning technique to date, we dont really understand the science of how it works or why its so successful. Its an example of applications coming ahead of theory.

These tools are already in our daily lives. AI systems recommend movies and books, respond to our voice commands, and translate web pages from one language to another. AI also adds to our repertoire of scientific methods. In medicine, deep-learning models are processing medical scans faster than humans and catching warning signs that even the experts sometimes miss. And they dont get tired! In astronomy, theyre analyzing images from telescopes and space probes to make new discoveries about our universe. In climate modeling, theyre helping to reduce the uncertainty around climate change and its impacts.

These tools are accelerating science, and I expect the trend to continue. AI holds great promise for the social sciences, too. At Microsoft, I saw how bringing economists together with machine learning experts helped the company better forecast sales of some products.

Q. What are you most proud of accomplishing at the Data Science Institute?

Creating bridges. Everything I did was about building collaboration across schools and disciplines. The Data Science Institute connected a lot of dots across campuses and beyond Columbias gates. When people from different perspectives and areas of expertise come together, sparks fly. Through data science, researchers and educators asked questions they never would have thought to ask, let alone answer.

I also feel good about creating theTrustworthy AIinitiative to investigate some of machine learnings unintended consequences. Our goal is to find out whether the AI systems making decisions about peoples lives can be trusted: Do I really have cancer? Is the moving object in front of my car a ball or a child? Will the bank approve my loan? It turns out that its hard to formally define the properties of trustworthiness, let alone prove and guarantee that an AI system has any of them.

A. Columbia Engineering and the Data Science Institute built the IBM Center on Blockchain and Data Transparency under your tenure. And Columbia continues to court corporate funders. Why is industry collaboration so vital?

In certain areas of research, AI especially, industry is ahead. They have the data, which is mostly proprietary consumer data. They also have vast amounts of computing power. Amazon, Microsoft, Google have nearly limitless computing power through their cloud infrastructure. They have GPU clusters academia could never afford. I see enormous potential for collaboration. If faculty could gain access to data and compute, they could validate their algorithms at scale and identify new research directions.

Its a mutually beneficial relationship. Industry looks to academia for new ideas and talent.Academia looks to industry for real-world problems to solve, and opportunities to scale solutions. Its an important way to broaden our impact.

Q. Youve held leadership roles in academia, industry, and the federal government. What skills allowed you to succeed in such different cultures?

A. To be able to listen and learn. To know what you dont know, and to surround yourself with superb talent.

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Meet the Computer Scientist Overseeing Columbia's $1 Billion Research Portfolio - Columbia University