Yann LeCun: Can Neural Networks Reason? | AI Podcast Clips | Summary and Q&A

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September 1, 2019
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Lex Fridman
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Yann LeCun: Can Neural Networks Reason? | AI Podcast Clips

TL;DR

Neural networks have the potential to reason, but the challenge lies in determining the amount of prior structure required and the compatibility of discrete mathematics with gradient-based learning.

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Key Insights

  • โ“ Neural networks require prior structure incorporation to achieve human-like reasoning abilities.
  • ๐Ÿคจ Deep learning's mathematical approach differs from traditional computer science methods and has raised skepticism.
  • ๐Ÿคณ Working memory is crucial for a reasoning system and can be simulated through memory networks or self-attention in transformers.
  • โ“ Recurrence, or the ability to iteratively update and expand knowledge, is essential for reasoning.
  • โ“ Accessing and writing into an associative memory efficiently is still a challenge for neural networks.
  • ๐Ÿ’ Energy minimization and planning are alternative forms of reasoning that utilize objective functions and models of the world.
  • โšพ Representing knowledge graphs through logic-based systems is brittle and rigid, while probabilistic approaches like Bayesian networks have been explored.

Transcript

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 put in the neural net so that something like human reasoning will emerge from it you know from running another question is all of our kind of model of... Read More

Questions & Answers

Q: How can neural networks be made to reason?

Neural networks can reason to some extent, but the level of human-like reasoning depends on incorporating prior structure into the network. The challenge lies in determining the right amount of structure.

Q: Why are traditional models of reasoning based on logic incompatible with neural networks?

Traditional models of reasoning are based on logical rules and discrete mathematics. Neural networks rely on gradient-based learning, which is incompatible with this discrete approach. Neural networks require a different mathematical framework.

Q: What is the main difference between computer science and machine learning?

Computer science focuses on precise algorithms and ensuring their correctness. In contrast, machine learning embraces a more flexible and probabilistic approach, often described as the "science of sloppiness."

Q: Is it possible for neural networks to reason without prior knowledge?

Neural networks require a form of memory, similar to the human hippocampus, to store factual episodic information. This memory allows a neural network to reason and build knowledge based on past experiences.

Q: How can neural networks be made to reason?

Neural networks can reason to some extent, but the level of human-like reasoning depends on incorporating prior structure into the network. The challenge lies in determining the right amount of structure.

Q: Why are traditional models of reasoning based on logic incompatible with neural networks?

Traditional models of reasoning are based on logical rules and discrete mathematics. Neural networks rely on gradient-based learning, which is incompatible with this discrete approach. Neural networks require a different mathematical framework.

More Insights

  • Neural networks require prior structure incorporation to achieve human-like reasoning abilities.

  • Deep learning's mathematical approach differs from traditional computer science methods and has raised skepticism.

  • Working memory is crucial for a reasoning system and can be simulated through memory networks or self-attention in transformers.

  • Recurrence, or the ability to iteratively update and expand knowledge, is essential for reasoning.

  • Accessing and writing into an associative memory efficiently is still a challenge for neural networks.

  • Energy minimization and planning are alternative forms of reasoning that utilize objective functions and models of the world.

  • Representing knowledge graphs through logic-based systems is brittle and rigid, while probabilistic approaches like Bayesian networks have been explored.

  • Symbol manipulation and logic can be replaced by continuous functions and vector representations, allowing for compatibility with learning systems.

Summary & Key Takeaways

  • Neural networks can be designed to reason, but the extent to which human-like reasoning emerges depends on the amount of prior structure incorporated into the network.

  • Traditional models of reasoning based on logic are incompatible with gradient-based learning, a fundamental aspect of neural networks.

  • Deep learning, which uses different mathematical approaches, has been met with skepticism due to its deviation from traditional computer science methods.

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