1096: Neuro-Symbolic Logic
Author(s): Scott Douglas Jacobsen
Publication (Outlet/Website): Medium (Personal)
Publication Date (yyyy/mm/dd): 2025/04/21
AlphaGeometry, Quoc V. Le, “This is another example that reinforces how AI can help us advance science and better understand the underlying processes that determine how the world works.”
Logical Neural Networks, Ryan Riegel et al., “We propose a novel framework seamlessly providing key properties of both neural nets (learning) and symbolic logic (knowledge and reasoning).”
DeepStochLog, Thomas Winters et al., “Can be trained end-to-end” and “achieves state-of-the-art results on challenging neural-symbolic learning tasks.”
NeuroQL, Nikolaos Papoylias: “Baseline solution for Inter-Subjective Reasoning” that “extends logical unification with neural primitives for extraction and retrieval.”
Braid, Aditya Kalyanpur et al.: “Novel FOL-based reasoner” that “supports probabilistic rules” and uses “custom unification functions and dynamic rule generation.”
Neuro-Vector-Symbolic Architectures, Gary Marcus et al: “We cannot construct rich cognitive models in an adequate, automated way without the triumvirate of hybrid architecture, rich prior knowledge, and sophisticated techniques for reasoning.”
Integration of Large Language Models, Amy Stapleton: “We are entering a new world. The technologies of machine learning, speech recognition, and natural language understanding are reaching a nexus of capability. The end result is that we’ll soon have artificially intelligent assistants to help us in every aspect of our lives.”
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