Neuro-symbolic systems represent a sophisticated approach to artificial intelligence (AI) by merging symbolic AI and connectionist (neural network) AI.
Neuro-symbolic AI systems consist of two main components:
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Gradient-Based Learnable Functions (Neural Networks): These components involve neural networks capable of learning through gradients.
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Symbolic Implementation or Specification: This includes functions with a symbolic implementation or, at the very least, a symbolic specification of their functionality.
By integrating symbolic reasoning with neural network learning, these systems excel in both deductive reasoning (logical inference) and inductive learning (pattern recognition).
Some videos to start
- NeuroSymbolic AI
- The Debate Over “Understanding” in AI’s Large Language Models
- Scaling laws are explained by memorization and not intelligence
- It's Not About Scale, It's About Abstraction
- AGI in 5 Years?
- HybridAGI – Graph-Powered, Self-Programmable AI
- Core Knowledge
- Core systems of number
- Origins of knowledge
- Ontological categories guide young children's inductions of word meaning: Object terms and substance terms
- False-belief understanding in infants
- The development of causal reasoning
- The acquisition of physical knowledge in infancy: A summary in eight lessons
- Tree of Thoughts: Deliberate Problem Solving with Large Language Models
- Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents
- Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B
- Arithmetic Reasoning with LLM: Prolog Generation & Permutation
- ProSLM : A Prolog Synergized Language Model for explainable Domain Specific Knowledge Based Question Answering
- Voyager: An Open-Ended Embodied Agent with Large Language Models
- Solving olympiad geometry without human demonstrations