Preparing the next generation for AI involves teaching them the process of learning and encouraging them to have ideas and projects.

You should be open to sharing your ideas and iterating on them with others to find the best solutions.

You should be open to sharing your ideas and iterating on them with others to find the best solutions.

While AI is currently proving mathematical concepts, the future challenge is whether AI can propose interesting new hypotheses, which remains uncertain.

There are many opportunities in interpretability projects, and we need more people to engage in this critical area of AI research.

The future of inference involves optimizing across three dimensions: quality, speed, and user concurrency, which relates to cost.

In today's AI landscape, many subject matter experts have already explored problems deeply, and collaborating with them can accelerate the validation and building process of new ideas.

The future of scaling inference will focus on heavy customization tailored to specific applications, transforming it into a multi-dimensional optimization problem.

The next major challenge in AI is teaching systems to reason and improve their performance through continuous thinking and learning.