Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the concept of Logical Neural Networks in this comprehensive lecture that aims to unify statistical and symbolic AI. Delve into a new neuro-symbolic framework that establishes a one-to-one correspondence between artificial neurons and logic gates in weighted real-valued logic. Discover how this approach enables logical inference within neural networks and introduces contradiction loss to maximize logical consistency. Learn about the framework's unique features, including full disentanglement, exact logical deduction, and compositional knowledge representation. Examine state-of-the-art results in question answering and other applications. Follow the evolution of neural networks from McCulloch and Pitts to modern deep learning, and understand how Logical Neural Networks bridge the gap between neural and symbolic approaches. Gain insights into knowledge base question answering, logical rule induction, and the potential implications for artificial general intelligence.