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SUMMARY:From Machine Learning to Machine Reasoning: Deterministic Neural S
 yllogistic Reasoning (Part 1) - Tiansi Dong
DTSTART:20250325T170000Z
DTEND:20250325T174500Z
UID:TALK231421@talks.cam.ac.uk
CONTACT:Pietro Lio
DESCRIPTION:This talk was recorded https://www.youtube.com/watch?v=9hnM9C4
 xHeM\n\nIn my last talk (https://talks.cam.ac.uk/talk/index/228790)\, I sh
 ow four methodological limitations that prevent machine learning systems f
 rom reaching the rigour of syllogistic reasoning. They cannot achieve the 
 rigour\, not because of insufficient amount of training data\, instead\, t
 o achieve the rigour\, they shall not use training data. What kind of neur
 al networks can be? Neural networks use vector embedding\, which is a sphe
 re embedding with zero radius. In this talk\, I will show the four limitat
 ions can be completely avoided by promoting vector embedding into sphere e
 mbedding with non-zero radius and the criterion of achieving deterministic
  neural reasoning\, namely\, for any satisfiable reasoning\, there is a co
 nstant number of M that the neural network shall correctly construct a mod
 el within M epochs. I will introduce a novel neural network\, Sphere Neura
 l Network (SphNN)\, which explicitly represents geometric objects\, here s
 pheres\, and introduces the method of syllogistic reasoning by constructin
 g Euler diagrams in the vector space. Instead of using training data\, Sph
 NN uses a neighbourhood transition map to transform the current sphere con
 figuration into the target. SphNN is the first neural network that achieve
 s deterministic human-like syllogistic reasoning in one epoch (M=1).
LOCATION:Lecture Theatre 2\, Computer Laboratory\, William Gates Building
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