AI Interaction and Learning
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- Microsoft Research podcast: what AI evaluation gets wrong
In a Microsoft Research podcast episode, host Chad Atalla speaks with Jennifer Neville, who leads the AI Interaction and Learning team at Microsoft Research and is a professor at Purdue, about how evaluation pushes the performance boundaries of today's AI systems and about the “surprising failures” that appear when models are tested beyond traditional benchmarks. Neville argues that the benchmarks commonly used in ML/AI are fairly simple relative to real-world use, so her team designs evaluations around multiturn behavior, collaborative settings and long-horizon tasks to expose performance gaps and then drive algorithmic and model improvements. She also offers practical guidance for working with current AI systems and stresses examining the data closely when results defy expectations.
Microsoft Research Blog · 🔥 5
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