How to Prevent AI from "Going Off the Rails" | #AI #Databricks #Hallucination #Podcast #Shorts

AI Oversight: Theoretical Loss and Potential Outcomes

Key Concepts:

• AI Governance
• Permissioning Tools
• Function Calls
• Lineage Tracking
• Unfettered Access
• Model Collapse
• Hallucination
• Grounding
• Medallion Architecture (Bronze, Silver, Gold Data)

1. Preventing AI from Going Off the Rails: Governance and Controls

• Past Vulnerability: The speaker suggests that a year and a half ago, before robust governance tools, AI systems operated in a "wild west" environment, posing a higher risk of unintended consequences. Function calls lacked clear boundaries.
• Current Approach: Explicit Controls: The current strategy involves establishing clear governance structures with explicit controls. This includes:
  • Function Call Bounds: Defining what an LLM function can and cannot do.
  • Data Access Control: Specifying which data an LLM can read or modify.
  • Lineage Tracking: Tracking the origin and history of data and actions.
• Centralized Governance: Large enterprises are recognizing the importance of centralized governance structures, particularly with tools like Databricks.
• Example: Customer Support Agent: A customer support agent should not have access to HR or marketing data, nor should it be able to alter the website.
• Schema-Based Control: These boundaries should be defined in a proper schema, making them deterministic rather than probabilistic.

2. The Threat of Unfettered Access

• Unfettered Access as a Risk: The speaker argues that a lack of governance and control leads to "unfettered access," which can cause AI systems to go off the rails.

3. The Rise of Machine-Produced Content and Model Collapse

• Shift to Machine-Produced Content: The speaker believes that more than 50% of internet content is now machine-produced, surpassing human-generated content.
• Model Collapse Scenario: A significant concern is "model collapse," where LLMs are trained on data generated by other LLMs, leading to the propagation of errors and "stupid stuff" instead of learning from real-world ground truth.
• Potential Consequences: The speaker is uncertain about the exact consequences of model collapse but suggests that it could worsen the hallucination problem and make it more difficult to detect errors, even in evaluation datasets.

4. Grounding AI in Reality: The Importance of Data Quality

• Importance of Grounding: Grounding AI systems in reality is crucial to mitigate the risks of model collapse and hallucination.
• Databricks' Approach: Medallion Architecture: Databricks advocates leveraging existing data infrastructure and the "medallion architecture" to ensure data quality.
  • Bronze Data: Raw, unprocessed data.
  • Silver Data: Partially cleaned and transformed data.
  • Gold Data: Highly refined, cleaned, and trusted data, considered to be as true as possible.
• Relevance of Data Quality: The speaker emphasizes that data quality is not just relevant but "extremely important" in the context of AI development.

5. Conclusion

The key takeaways are that robust AI governance, explicit controls, and a focus on data quality are essential to prevent AI systems from going off the rails. The rise of machine-produced content and the potential for model collapse highlight the need for grounding AI in reality and leveraging established data management practices to ensure the accuracy and reliability of AI systems.