Mitchell critiques popular benchmarks for evaluating AI systems, arguing that they often rely on flawed assumptions and should evolve as capabilities improve.
The podcast episodes discuss the importance of AI benchmarks in evaluating the capabilities of large language models (LLMs) and other AI systems.
Several episodes, such as Francois Chollet, Mike Knoop - LLMs won't lead to AGI - $1,000,000 Prize to find true solution and Prof. Melanie Mitchell 2.0 - AI Benchmarks are Broken!, critique existing benchmarks for their limitations in truly assessing AI systems' understanding and reasoning abilities.
The episodes also discuss the introduction of new benchmarks, like MMLU-Pro, GPQA, and MuSR, that aim to better evaluate instruction-tuned models, as mentioned in 📅 ThursdAI - Gemma 2, AI Engineer 24', AI Wearables, New LLM leaderboard.