New paths in AI: Rethinking LLMs and model risk strategies

The AI Fundamentalists

Are businesses ready for large language models as a path to AI? In this episode, the hosts reflect on the past year of what has changed and what hasn’t changed in the world of LLMs. Join us as we debunk the latest myths and emphasize the importance of robust risk management in AI integration. The good news is that many decisions about adoption have forced businesses to discuss their future and impact in the face of emerging technology. You won't want to miss this discussion.

• Intro and news: The veto of California's AI Safety Bill (00:00:03)
  • Can state-specific AI regulations really protect consumers, or do they risk stifling innovation? (Gov. Newsome's response)
  • Veto highlights the critical need for risk-based regulations that don't rely solely on the size and cost of language models
  • Arguments to be made for a cohesive national framework that ensures consistent AI regulation across the United States
• Are businesses ready to embrace large language models, or are they underestimating the challenges? (00:08:35)
  • The myth that acquiring a foundational model is a quick fix for productivity woes
  • The essential role of robust risk management strategies, especially in sensitive sectors handling personal data
  • Review of model cards, Open AI's system cards, and the importance of thorough testing, validation, and stricter regulations to prevent a false sense of security
  • Transparency alone is not enough; objective assessments are crucial for genuine progress in AI integration
• From hallucinations in language models to ethical energy use, we tackle some of the most pressing problems in AI today (00:16:29)
  • Reinforcement learning with annotators and the controversial use of other models for review
  • Jan LeCun's energy systems and retrieval-augmented generation (RAG) offer intriguing alternatives that could reshape modeling approaches
• The ethics of advancing AI technologies, consider the parallels with past monumental achievements and the responsible allocation of resources (00:26:49)
  • There is good news about developments and lessons learned from LLMs; but there is also a long way to go.
  • Our original predictions in episode 2 for LLMs still reigns true: “Reasonable expectations of LLMs: Where truth matters and risk tolerance is low, LLMs will not be a good fit”
  • With increased hype and awareness from LLMs came varying levels of interest in how all model types and their impacts are governed in a business.

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1. New paths in AI: Rethinking LLMs and model risk strategies (00:00:00)

2. News: Veto of CA AI Safety Bill (00:00:03)

3. Challenges of LLMs and generative AI - One year later (00:08:31)

4. Navigating the Future of AI (00:16:29)

5. Ethics and Innovation in AI (00:26:49)

6. Addressing Responsible AI Concerns (00:37:10)