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Yann LeCun: Can Neural Networks Reason? | AI Podcast Clips
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This is a clip from a conversation with Yann LeCun on the Artificial Intelligence podcast. You can watch the full conversation here: http://bit.ly/2NJiCov If you enjoy these, consider subscribing, sharing, and commenting below. Full episode: http://bit.ly/2NJiCov Full episodes playlist: http://bit.ly/2EcbaKf Clips playlist: http://bit.ly/2JYkbfZ Podcast website: https://lexfridman.com/ai Yann LeCun is one of the fathers of deep learning, the recent revolution in AI that has captivated the world with the possibility of what machines can learn from data. He is a professor at New York University, a Vice President & Chief AI Scientist at Facebook, co-recipient of the Turing Award for his work on deep learning. He is probably best known as the founder of convolutional neural networks, in particular their early application to optical character recognition. Subscribe to this YouTube channel or connect on: - Twitter: https://twitter.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/lexfridman - Instagram: https://www.instagram.com/lexfridman - Medium: https://medium.com/@lexfridman - Support on Patreon: https://www.patreon.com/lexfridman
Segments Timeline
Can Neural Networks Reason?
Yann LeCun discusses the potential for neural networks to reason, emphasizing the importance of prior structure in enabling human-like reasoning. He contrasts discrete logic-based models with gradient-based learning, highlighting the challenges of integrating traditional logic into machine learning frameworks.
"do you think neural networks can be made to reason yes there's no question about that again we have a good example right the question is is how so the question is how much prior structure you have to ..."
The Role of Memory in Reasoning
LeCun explores the necessity of a working memory for neural networks to reason effectively. He compares human memory types, such as short-term and long-term memory, to the requirements for AI systems, suggesting that memory networks and transformers could serve as foundational elements for reasoning capabilities.
"kind of looked at with suspicion by a lot of computer scientists because the math is very different the math that you use for deep running you know we kind of as more to do with you know cybernetics t..."
Building Knowledge Through Reasoning
In this segment, LeCun elaborates on how reasoning in AI could involve building knowledge from previous experiences and generalizing beyond training data. He discusses the iterative process of reasoning and the need for systems that can access and update knowledge effectively.
"look like if yeah they may be do you have Inklings of thoughts of what that look like well yeah I mean yes or no if I had precise ideas about this I think you know we'd be building it right now but an..."
Energy Minimization in AI Reasoning
LeCun introduces the concept of energy minimization as a form of reasoning in AI. He explains how this method can be applied to planning and decision-making, drawing parallels to human survival strategies and the evolutionary aspects of reasoning capabilities.
"that you want the hippocampus like thing right and that's what people have tried to do with memory networks and you know in altering machines and stuff like that right and and now with transformers wh..."
Challenges of Knowledge Representation
LeCun critiques traditional knowledge representation methods in AI, such as logic systems and knowledge graphs, for their rigidity. He discusses the limitations of these approaches and the need for more flexible systems that can incorporate probabilistic reasoning and adapt to new information.
"knowledge yeah but is is this something that just can emerge with size because it seems like everything we have now is just no it's not it's not it's not clear how you access and write into an associa..."
Replacing Symbols with Vectors
In this segment, LeCun advocates for a shift from symbolic logic to vector-based representations in AI. He references Geoff Hinton's ideas on continuous functions and working memory, suggesting that this approach could enhance the compatibility of learning systems with reasoning tasks.
"that's performer reasoning planning is a form of reasoning and perhaps what led to the ability of humans to reason is the fact that or you know species you know that appear before us had to do some so..."