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Lab notes
What AI gets wrong and what failure teaches us
Jennifer Neville did not want to go into computer science, but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity.
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.
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