Machine Learning Street Talk (MLST)
Prof. Melanie Mitchell 2.0 - AI Benchmarks are Broken!
Sep 10, 2023
Prof. Melanie Mitchell, Davis Professor of Complexity at the Santa Fe Institute, dives into the murky waters of AI understanding. She argues that current benchmarks are inadequate, as machines often replicate human tasks without true comprehension. Mitchell highlights the limitations of large language models, noting their lack of common sense despite impressive statistical capabilities. She emphasizes the need for evolving evaluation methods and suggests a deeper, context-specific look at intelligence, advocating for more rigorous testing to reflect genuine understanding.
01:01:47
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Quick takeaways
- AI understanding is ill-defined and multidimensional, requiring proper experimental methods for testing capabilities.
- Large language models lack common sense and do not possess human-like conceptual knowledge.
Deep dives
The importance of refining our notions of understanding in AI
In this podcast episode, the speaker discusses the need to refine our understanding of key concepts in AI, such as understanding and intelligence. The speaker explains that AI systems often exhibit specific skills and capabilities, but their understanding is often limited and context-dependent. This challenges traditional benchmarks and requires a more nuanced approach to evaluating AI systems. The importance of experimental method and rigorous testing is emphasized, particularly in determining the genuine capabilities and limitations of AI systems.
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