4.8 • 4.4K Ratings
🗓️ 24 June 2024
⏱️ 102 minutes
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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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0:00.0 | Hello everyone and welcome to the Mindscape podcast. I'm your host Sean Carroll. You know that artificial intelligence is in the news. |
0:07.0 | We've talked about AI in various different ways here on the podcast, especially over the last couple years, where chat gPT and other large |
0:15.7 | language models have really become an enormous study of interest to many people |
0:21.6 | for financial reasons for intellectual |
0:24.2 | reasons they're becoming everywhere right Google has put them on the first |
0:29.4 | page of its search results lots of people are using large language models to write texts. You can write |
0:36.7 | programs using large language models. You can write the syllabus for your college course, |
0:41.0 | etc. It's clear that this technology is going to have an enormous impact |
0:45.7 | on how humans behave and live going forward. But there are subtleties. One of the things that I've talked about is the idea that large language models are amazing |
0:57.9 | because they are able to mimic human speech and behavior, right? They are able to sound enormously human without actually thinking in the same way that human beings do. |
1:11.0 | Large language models in some sense memorize lots of things. They know a lot of facts about |
1:16.6 | the world and they're super good at interpolating between things that they know. That includes interpolating different kinds of things that have never |
1:25.1 | been interpolated before, so they can seem creative. They can do things that have never |
1:30.5 | been done based on the training data of things that have been done before. |
1:36.0 | They're less good at going outside of the range of that training data, and one can argue that |
1:42.0 | the processes by which they come up with their outputs are very different |
1:46.2 | than what a human being does in actually thinking reasoning about the problem presented to it. |
1:51.8 | And many people, especially people who are experts in AI, |
1:56.4 | understand this attitude perfectly well. It's certainly not new with me. It's well known to many people, |
2:01.6 | but it is denied by other people who are much more |
2:05.9 | impressed with the progress in large language models and think that we're close to |
2:10.0 | AGI artificial general intelligence. |
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