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. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research .
Jennifer Neville, a Microsoft Research partner research manager and Samuel D. Conte chair professor of computer science and statistics at Purdue, shares insights on the role of evaluation in advancing AI systems and the importance of data analysis when results deviate from expectations. Neville, whose career path was unconventional, merging math, physics, cognitive science, and computer science, discusses her experiences with AI failures and the lessons learned from such setbacks.
Her work focuses on understanding how data input affects AI behavior and aligning it with user needs, shedding light on the unexpected nature of AI development and the need for careful data examination.
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