Overview
Syllabus
Intro
Logical Neural Networks
Neuro-symbolic methods so far
Neuro-symbolic methods: another category
Original (McCulloch and Pitts 1943) neuron as logic gate
Weighted neuron (perceptron, 1958) as logic gate
Differentiable neuron (MLPs, deep learning) as logic gate
b. Constrained differentiable neuron (LNN) as logic gate
a. Neuron (LNN) as real-valued logic gate
a. Neural network inference as logical reasoning
a. Data and learning
7b. Data and learning
Equivalence between neural networks and symbolic logic
Comparison to other common neuro-symbolic ideas
Use case: Knowledge base question answering (KBQA)
KBQA: Why it challenges default Al (end-to-end deep learning)
KBQA: an approach via understanding
Making the model & inference process human-understandable
Learning to reason
Logical rule induction (ILP)
Optimization/learning
Reinforcement learning
Policy induction via rule learning
AGI: Bengio-Marcus Desiderata
Ongoing directions
Philosophical shift
Summary
Taught by
Georgia Tech Research