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DataFunTalk
DataFunTalk
Feb 11, 2025 · Artificial Intelligence

Roundtable on Enhancing Large Model Effectiveness: RAG, Tool Use, and Knowledge Engineering

Experts from Dipu, Ant Financial, iKang, and Zhihu discuss practical strategies for improving large model performance, covering RAG, tool‑using, offline knowledge engineering, multimodal training, evaluation metrics, and future trends, while sharing case studies from manufacturing, healthcare, retail, and C‑end applications.

AI evaluationRAGknowledge engineering
0 likes · 9 min read
Roundtable on Enhancing Large Model Effectiveness: RAG, Tool Use, and Knowledge Engineering
AntTech
AntTech
Aug 12, 2024 · Artificial Intelligence

DKCF Trustworthy Framework for Large Model Applications and AI Security Practices

The article outlines the DKCF (Data‑Knowledge‑Collaboration‑Feedback) trustworthy framework presented at the 2024 Shanghai Cybersecurity Expo, detailing challenges of large AI models, four key trust factors, and Ant Group's practical security implementations for professional AI deployments.

AI safetyDKCFfeedback loops
0 likes · 10 min read
DKCF Trustworthy Framework for Large Model Applications and AI Security Practices
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-AutomatedSymbolic AI
0 likes · 23 min read
NLP Evolution: Symbolic Deep Parsing vs Neural Pre‑trained Models, Low‑Code Trends, and Semi‑Automated Applications
DataFunTalk
DataFunTalk
Jan 20, 2020 · Artificial Intelligence

The Second Half of Knowledge Graphs: Opportunities and Challenges

This comprehensive report analyzes the evolution of knowledge graphs, reviews achievements of the first half, and examines the challenges and opportunities of the emerging second half, highlighting shifts from large‑scale simple applications to complex, expert‑driven scenarios, and outlining strategies for representation, acquisition, and application in the era of big data and AI.

AIBig Dataknowledge engineering
0 likes · 30 min read
The Second Half of Knowledge Graphs: Opportunities and Challenges