January 18, 2024 7:00 PM PST
This meeting discusses the role of Large Language Models (LLMs) in Enterprise Knowledge Management (KM), focusing on their capabilities, current challenges, and potential solutions in various industries.
Presenter: Wenguang Wang, Vice President of Daguan Data
System Design Presentation - Databricks
Key Concepts
- LLM: Can understand knowledge across domains.
- KMS Formula: KMS = (K x M) ^ S
- K: Knowledge
- M: Management
- S: Spreading
Generations of Knowledge Management
- Knowledge database
- Knowledge domain
- Collaborative editing
- Knowledge labeling
- Knowledge graph
- Big model / LLM
- AGI (Artificial General Intelligence)
LLM and AGI
- LLM includes models like GPT and ChatGPT, which are seen as steps toward AGI.
- Reinforced learning and alignment are crucial for developing these models.
Current Challenges in Knowledge Management
- Inability to accumulate knowledge.
- Difficulty in finding and understanding knowledge.
Proposed Solutions
- Accumulation of knowledge.
- Making knowledge visible and valuable.
- Implementing long-term memory systems.
- Learning from mistakes to grow employees and improve productivity.
Applications
- Example: High-speed rail industry.
- Solutions include automated analysis of knowledge and the use of knowledge graphs.
Knowledge Management System Components
- Knowledge architecture
- Knowledge search
- Knowledge community
- Knowledge chain
- Q&A systems
- Knowledge delivery
Industry Applications
- Medical
- Automobile
- Financial
Knowledge Organization
- Knowledge search and delivery.
- Use of retrieval augmented generation (RAG) for enhanced knowledge management.
Challenges in Knowledge Management
- Hallucination in responses.
- Need for knowledge refreshment.
- Multi-tier solutions for knowledge enhancement.
Future Considerations
- The role of software engineers in a future where software may become less relevant.
- The challenges of adopting LLMs in medium-sized companies.
- Potential for SaaS-based LLM solutions.
Customer Case Studies
- Electrical utility company and GM troubleshooting scenarios.
Market Insights
- Current limitations in GPU availability.
- Opportunities in the Chinese market, with a focus on vertical specialization.
- The impact of price competition and the startup ecosystem in China versus Silicon Valley.
Integration and Development
- Strategies for updating knowledge and integrating with existing systems.
- Development and customization efforts required for effective knowledge management solutions.
- Discussion on knowledge graph technologies like GensGraph and Nebula graph.
Conclusion
- The market for LLM and knowledge management systems is significant, but competition is expected.
- Effective integration and customization are key to overcoming existing challenges in knowledge management.