QFM037: Machine Intelligence Reading List October 2024
Everything that I found interesting about machines behaving intelligently during October 2024
This month’s edition of the “Machine Intelligence Reading List” looks into recent developments in AI, exploring both the technical boundaries and practical applications of large language models (LLMs) and other machine learning tools. In Apple study exposes deep cracks in LLMs’ “reasoning” capabilities, Apple researchers reveal the limitations of LLMs in performing genuine logical reasoning, underscoring how these models struggle to adapt to minor changes in problem scenarios, often replicating patterns from their training data without true understanding. This raises essential questions about the robustness of LLMs, especially in situations that demand logical inference.
Extending the conversation on reasoning, LLMs, Theory of Mind, and Cheryl’s Birthday by Peter Norvig examines whether LLMs can grasp “theory of mind,” using a classic logic puzzle to test their ability to understand another perspective. Norvig’s findings echo Apple’s study, demonstrating that, while powerful, current LLMs lack the cognitive nuance to handle tasks that humans process intuitively.
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