From Machine Learning to Machine Reasoning: Deterministic Neural Syllogistic Reasoning

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• Tiansi Dong
• Monday 17 March 2025, 17:00-17:45
• Lecture Theatre 2, Computer Laboratory, William Gates Building.

If you have a question about this talk, please contact Pietro Lio.

In my last talk (https://talks.cam.ac.uk/talk/index/228790), I show four methodological limitations that prevent machine learning systems from reaching the rigour of syllogistic reasoning. They cannot achieve the rigour, not because of insufficient amount of training data, instead, to achieve the rigour, they shall not use training data. What kind of neural networks can be? Neural networks use vector embedding, which is a sphere embedding with zero radius. In this talk, I will show the four limitations can be completely avoided by promoting vector embedding into sphere embedding with non-zero radius. I will introduce a novel neural network, Sphere Neural Network (SphNN), which explicitly represents geometric objects, here spheres, and introduces the method of syllogistic reasoning by constructing Euler diagrams in the vector space. Instead of using training data, SphNN uses a neighbourhood transition map to transform the current sphere configuration into the target. SphNN is the first neural network that achieves deterministic human-like syllogistic reasoning in one epoch with the worst computational complexity of O(N) (where N is the length of the chain).

This talk is part of the Foundation AI series.

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• Lecture Theatre 2, Computer Laboratory, William Gates Building

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