John Bruton, founder of Data Squared AI, discusses his journey into the field of AI and the solutions his company is working on. He highlights the importance of traceability, trustworthiness, and explainability in AI models, especially in high reliability industries like defense and energy. The conversation also touches on the future of large language models, the potential challenges of copyright infringement, and the monetization opportunities in the AI space.

KeywordsAI, data, traceability, trustworthiness, explainability, high reliability industries, large language models, copyright infringement, monetization

Takeaways

• Traceability, trustworthiness, and explainability are crucial in AI models, particularly in high reliability industries.
• The future of large language models may involve localization to specific industries or providers.
• Combining knowledge graphs with large language models can enhance the scalability and adaptability of generative AI solutions.
• The AI space offers monetization opportunities, particularly in industries that are text or data intensive.
• Prompt engineering and context are essential for grounding models in the problem they are trying to solve.

Titles

• Monetization Opportunities in the AI Space
• The Role of Prompt Engineering and Context in AI

Sound Bites

• "The ground shifting underneath our feet almost on an hourly basis."
• "We're leveraging the available technology of the day to do better, more explainable, higher efficacy rate returns from using large language models."
• "We can control the outcomes. We can really test the efficacy of the training data that underlies these models as we approach them."

Chapters

00:00Introduction and Background

07:06The Future of Large Language Models and Localization

13:17Using Data Models for Lease Acquisitions

23:17Org Capability Multipliers and Conclusion