Challenges with Large Language Models in Problem Solving

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This chapter explores the limitations of using Large Language Models (LLMs) in competitions, emphasizing the importance of reasoning over memorization in truly intelligent problem-solving scenarios. The discussion highlights how LLMs struggle to adapt and generate new solutions on the fly, exposing the difference between mere pattern recognition and genuine reasoning abilities.

Which is more intelligent, ChatGPT or a 3-year old? Of course this depends on what we mean by "intelligence." A modern LLM is certainly able to answer all sorts of questions that require knowledge far past the capacity of a 3-year old, and even to perform synthetic tasks that seem remarkable to many human grown-ups. But is that really intelligence? François Chollet argues that it is not, and that LLMs are not ever going to be truly "intelligent" in the usual sense -- although other approaches to AI might get there.

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Blog post with transcript: https://www.preposterousuniverse.com/podcast/2024/06/24/280-francois-chollet-on-deep-learning-and-the-meaning-of-intelligence/

François Chollet received his Diplôme d'Ingénieur from École Nationale Supérieure de Techniques Avancées, Paris. He is currently a Senior Staff Engineer at Google. He has been awarded the Global Swiss AI award for breakthroughs in artificial intelligence. He is the author of Deep Learning with Python, and developer of the Keras software library for neural networks. He is the creator of the ARC (Abstraction and Reasoning Corpus) Challenge.

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