What AI gets wrong and what failure teaches us

What AI gets wrong and what failure teaches us - Microsoft Research

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Jennifer Neville is a partner research manager at Microsoft who’s built a career around understanding and advancing AI for real-world use, and much like the human-AI interactions she’s been studying, her early-career path was multiturn: math, then physics; cognitive science, then work; and finally computer science—despite her best efforts to avoid the field.

In this conversation with Principal Applied Scientist Chad Atalla , she explores the role evaluation plays in pushing the performance boundaries of today’s AI systems to meet user needs and the “surprising failures” that emerge when models are tested beyond traditional benchmarks. Neville also shares practical guidance for working with current AI systems and discusses why looking closely at data matters when results defy expectations, and what decades of AI progress have taught her about predicting what comes next.

From an unexpected career trajectory to the frontier of AI interaction and learning, this episode asks a larger question: what can we learn when the path—whether human or artificial—doesn’t unfold the way we expect?

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Transcript

[MUSIC]

JENNIFER NEVILLE: There were times that I felt like I was spinning my wheels. Things weren’t working. I would get, kind of, dejected. And then we’d get to a point where we did learn something, and it was just the, the emotional thrill of that was, that was really what hooked me. The being able to, kind of, understand something that no one else understood yet …

CHAD ATALLA: Sure …

NEVILLE: … because it’s just at the frontier of what we know about things.

STANDARD INTRODUCTION: This is the Microsoft Research Podcast , where Microsoft researchers—driving advancement through fundamental science and technology research—explore the who, how, and what’s next in computing and AI.

[MUSIC ENDS]

CHAD ATALLA: Hello, and welcome. I’m Chad Atalla, an applied scientist here at Microsoft Research.

Today, I’m joined by Jennifer Neville, a partner research manager at Microsoft Research and the Samuel D. Conte chair professor of computer science and statistics at Purdue.

Her research examines machine learning and AI for interactive domains and structured data, looking at how the data points that AI systems are trained on affect their behaviors and how that aligns with what users actually want.

Across that research, she has published more than 130 papers with over 10,000 citations and received honors such as a National Science Foundation CAREER Award, a spot on IEEE’s “10 to Watch” in AI list (opens in new tab) , and best paper awards from the International Conference on Data Mining (opens in new tab) and the International Conference on Learning Representations .

What amazes me most about Jen’s work is her foresight and ability to have a through line that stays consistent as the field of AI develops, and I’m excited to hear more about how she got to where she is today.

Jen, thank you for joining me.

JENNIFER NEVILLE: Thanks for having me.

ATALLA: Awesome. Well, I would love to learn about how you got here today. And let’s rewind way back. When I was a kid, I wanted to be an astrophysicist, but of course, here I am with a computer science background. When did you know that you wanted to go into computer science?

NEVILLE: That’s a good question. I … when I was growing up, I wanted to do anything but computer science because that’s what my dad was in, and I wanted to do anything but what my dad did. So when I went to college, first I majored in math and then in physics, but I really just couldn’t vibe with those majors.

So I dropped out of college for a while, and when I went back to college again, I majored in cognitive science instead, which also was too squishy for me. Didn’t have enough math in it. At that point, if somebody had told me, “AI is the thing you should be doing because it combines the cognitive science with the math and computational thinking,” I think I would have saved myself a lot of time, [LAUGHTER] but that didn’t happen.

And so I worked for a while. And then when I went back to school, I decided to major in computer science because what I wanted to do was think about working with data and dealing with data. And that’s when I found AI. Just by happenstance. I didn’t even really realize that it was part of computer science.

ATALLA: Awesome. Well, it’s one thing to go into computer science and want to work on data or AI and another thing to want to be involved in research on that front. So what sorts of questions or big ideas sparked your research drive?

NEVILLE: Yeah, that’s actually an interesting story, as well. I only got into research because I was in the honors program in my computer science degree, and as part of the honors program, you have to do a research...