Understanding Artificial General Intelligence (AGI) and Language Models (LLMs)

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The chapter explores the complexities of artificial general intelligence (AGI) and large language models (LLMs) by discussing their current limitations and potential future advancements. It critiques the notion that LLMs are on the path to AGI and delves into the challenges posed by data availability for training LLMs. The conversation also covers the implications of AGI in terms of creativity, problem-solving, and the potential dangers if not deliberately engineered.

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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