1. Retrieval Augmented Generation
- An effective way to make generative AI relevant in the workplace.
- Impacts on inference relevance and efficiency.
2. Holistic AI Solutions
- Emphasis on complete AI solutions over standalone models.
- Encourages integration with existing systems and processes.
3. Quantized AI Models
- Trend towards smaller, more efficient AI structures.
- Potential for reduced computational costs and increased accessibility.
4. AI Management Hubs
- Centralized platforms for managing various AI models.
- Enhancement of model deployment and monitoring.
5. AI Governance Focus
- Growing importance of AI regulation and ethical standards.
- Intelligent Model Registry as a tool for compliance.
6. AI Legislation Challenges
- Complexity in navigating international AI laws.
- Need for harmonization across jurisdictions.
7. Robust AI Evaluation
- Importance of comprehensive model performance metrics.
- Continuous monitoring for quality assurance.
8. Diverse AI Model Applications
- Shift towards purpose-specific AI models.
- Contrast with one-size-fits-all solutions.
9. Multimodal AI Growth
- Integration of multiple data types in large language models (LLMs).
- Enhances model versatility and application.
10. Orchestrated AI Interactions
- Use of specialized LLMs for managing complex AI ecosystems.
- Promotes efficiency in large-scale AI operations.
11. Multi-agent AI Dynamics
- Advancements in AI agents interacting autonomously.
- Enables more sophisticated AI systems.
12. Real-Time Model Analytics
- Competitive edge through instant AI model performance data.
- Informs rapid decision-making for AI deployment.
13. LLMs in Physical Spaces
- Transition of LLMs from digital-only to real-world applications.
- Opens new avenues for AI utility.
14. Demand for Explainable AI
- Need for transparent AI in enterprise and compliance.
- Increases trust and facilitates oversight.
15. Data Handling Evolution
- Innovation in data platforms to support GenAI.
- Critical for managing complex data ecosystems.