To effectively use AI, users must clearly define the tasks and expectations for the AI, similar to how one would instruct an intern on their job responsibilities.

Design is about problem-solving and requires a deep understanding of user needs, which current AI models struggle to replicate.

AI can help humanity by solving deep physics and mathematics problems, making it a valuable tool for scientific discovery.

To enhance the usefulness, reliability, and security of AI, we need to understand the reasoning behind their actions.

To create effective AI models for engineering, we need to generate synthetic training data that is physics-based and informed by supply chain considerations for various physical product domains.

AI can be applied to almost any subject, but it's essential to deeply understand the specific field, whether it's economics or biology, to ask the right questions for effective application.

It's important for developers to view the world through the lens of the AI model, considering what instructions would be most applicable for it to understand and operate effectively.

We've applied AI to various tasks, including chip design and scheduling, making processes more efficient, but it remains constrained to domains that are provably correct, like mathematics and coding.

To advance AI, systems need to understand the physical world, have persistent memory, and be capable of reasoning and planning.