A path towards AGI: Neuro-symbolic systems Sometimes when you can't find the solution it's useful to look back and reflect on past approaches. Since 2015, various neuro-symbolic systems have emerged, including: - IBM's researches - MIT's Neuro-Symbolic Concept Learner (NS-CL) - Logic Tensor Networks (LTN) - Graph Neural Networks - Neural-symbolic visual question answering (NS-VQA) - Neuro-symbolic programming (NSP) and more. DeepMind was also working a lot with similar approaches and developed deep reinforcement learning in 2016, combining reinforcement learning with neural networks. And now, in 2024, we got significant achievement of AlphaProf and AlphaGeometry in the International Math Olympiad. AlphaGeometry notably solved 25/30 geometry problems within competition time limits. And what is also notable - it's a neuro-symbolic system. So how does neuro-symbolic system work? Neuro-symbolic AI systems are hybrid AI architectures that combine neural networks (neuro) with symbolic reasoning methods (symbolic). Neural Networks: - Excel at identifying patterns and relationships in large amount of data - Are strong in perception tasks like image and speech recognition, NLP and others - Are effective for generating predictions and “intuitive” ideas But they are bad at reasoning and explaining their decisions Symbolic Reasoning: - Emphasizes logic, rules, and structured knowledge - For reasoning tasks, it uses human-readable symbols to represent objects, concepts, and relationships in the world - Provide clear and interpretable explanations for their decisions However, they are slow and inflexible and can’t deal well with large amount of data. Advantages of neuro-symbolic hybrid AI: ▪️ Robustness: Combines neural learning with logical reasoning. ▪️ Versatility: Handles a wide range of tasks. ▪️ Interpretability: Enhances trust in model decisions. ▪️ Generalization: Integrates data-driven learning with rule-based reasoning Can this hybrid approach lead to AGI? While it's uncertain, neuro-symbolic systems deserve more exploration as they mimic the human use of both logic and intuition in decision-making. Here's a scheme of neuro-symbolic AI methods from "Neurosymbolic AI - Why, What, and How" paper:

GPT-4o and Copilot both _really_ want to write NF code with DSL1, regardless of how aggressively my prompts are.