Enterprises are becoming more comfortable with using hosted AI models, as security, privacy, and compliance measures are being taken seriously by providers like OpenAI.

Hallucinations are a significant challenge for AI models, and we have been studying how to improve their truthfulness and express uncertainty.

Despite concerns about AI hallucinations and inaccuracies, there are pathways to improve these technologies, and the jobs they create will evolve to be more interesting rather than disappear.

The reliability of AI systems is a major concern as we transition from consumer applications to mission-critical systems like healthcare and defense.

As AI technology matures, enterprises are increasingly willing to trust these systems with their data, leading to a greater adoption of AI solutions.

It's crucial to understand the complexities of working with AI systems, including the need to manage issues like hallucinations and model evaluations effectively.

In medicine, hallucinations from AI can be life-threatening, so we ensure our models are trained exclusively on peer-reviewed medical knowledge to maintain accuracy.

Enterprises are now more focused on the value AI can deliver rather than the specific models or tools being used, seeking stable and reliable solutions.

Incorporating a human-in-the-loop approach is essential for assessing AI outputs and ensuring accuracy, especially in preventing hallucinations.