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symbolic AI

7 articles · Page 1 of 1
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Jun 8, 2026 · Artificial Intelligence

Seven Ontology Engineering Techniques to Stop AI Hallucinations and Noise

The article distinguishes noise from hallucination in AI decision systems and presents a seven‑layer ontology‑based defense—including ontological firewalls, range guards, axiom checks, confidence decay, assumption closure, provenance tracking, and external validation—that pre‑emptively blocks false reasoning, compares this approach with large‑model methods, and cites recent research showing substantial hallucination reduction.

AI SafetyOntologyhallucination mitigation
0 likes · 13 min read
Seven Ontology Engineering Techniques to Stop AI Hallucinations and Noise
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
May 12, 2026 · Artificial Intelligence

Silicon Brain: Neural Connections, Symbolic Reasoning, and Reinforcement Learning in AGI

This article analyses DeepMind’s three‑pronged AGI paradigm—combining neural networks, symbolic systems, and reinforcement learning—by dissecting AlphaGo, AlphaFold 2, Gemini, and the Genie‑Sima loop, mapping the biological inspiration, outlining engineering and safety challenges, and proposing research directions for large‑scale deployment in communication scenarios.

AGIDeepMindEngineering Challenges
0 likes · 21 min read
Silicon Brain: Neural Connections, Symbolic Reasoning, and Reinforcement Learning in AGI
Data Party THU
Data Party THU
Dec 11, 2025 · Artificial Intelligence

Why Symbolic AI Is Making a Comeback: From Logic Foundations to Modern Applications

This article traces the seventy‑year evolution of Symbolic AI, explains its core physical symbol system hypothesis, contrasts it with connectionist approaches, examines historic milestones such as the Logic Theorist, MYCIN and XCON, discusses the symbol‑grounding problem, and shows how modern neural‑symbolic systems are reviving its relevance in high‑stakes domains requiring accuracy, interpretability and safety.

AI historyExpert Systemsformal verification
0 likes · 16 min read
Why Symbolic AI Is Making a Comeback: From Logic Foundations to Modern Applications
PaperAgent
PaperAgent
Dec 9, 2025 · Artificial Intelligence

Agentic AI Unveiled: Dual Paradigms, Architecture Battles, and Future Directions

This comprehensive survey dissects Agentic AI by contrasting symbolic/classical and neural/generative paradigms, mapping 90 peer‑reviewed papers (2018‑2025) through a PRISMA workflow, evaluating architectures, collaboration models, benchmarks, and ethical considerations, and highlighting the emerging need for hybrid systems and standardized evaluation.

Agentic AIHybrid ArchitectureLLM agents
0 likes · 8 min read
Agentic AI Unveiled: Dual Paradigms, Architecture Battles, and Future Directions
NewBeeNLP
NewBeeNLP
Jun 19, 2024 · Artificial Intelligence

Can Symbolic Chain‑of‑Thought Boost LLM Logical Reasoning?

The paper introduces SymbCoT, a Symbolic Chain‑of‑Thought framework that translates natural‑language problems into symbolic form, plans, solves, and verifies reasoning steps, achieving significantly higher logical reasoning performance than traditional CoT methods across multiple benchmark datasets.

ACL 2024Chain-of-ThoughtLLM
0 likes · 13 min read
Can Symbolic Chain‑of‑Thought Boost LLM Logical Reasoning?
DataFunTalk
DataFunTalk
May 5, 2022 · Artificial Intelligence

NLP Evolution: Symbolic Deep Parsing vs Neural Pre‑trained Models, Low‑Code Trends, and Semi‑Automated Applications

The article reviews the history and current state of NLP, compares symbolic deep‑parsing and neural pre‑trained approaches, discusses the knowledge‑bottleneck and low‑code trend, and illustrates semi‑automated, low‑code NLP deployment in the financial domain while pondering future integration of symbolic and neural methods.

NLPSemi-Automatedknowledge engineering
0 likes · 23 min read
NLP Evolution: Symbolic Deep Parsing vs Neural Pre‑trained Models, Low‑Code Trends, and Semi‑Automated Applications