Multidisciplinary research will be crucial in the next decade, combining expertise in areas like AI or machine learning with fields such as biology, chemistry, or mathematics.

Building a diverse team of experts in AI, chemistry, and biology is essential for questioning traditional approaches and fostering innovation in drug design.

Building an organization with world-class experts in various fields is incredibly challenging, requiring effective communication and integration among team members.

There's significant potential for breakthroughs by merging different subject areas, and I've always aimed to be multidisciplinary in my own work, integrating neuroscience, machine learning, and computer science.

The intersection of drug design and machine learning is a new field, and we need to bring together experts from both areas to drive breakthroughs.

Integrating software and hardware effectively requires collaboration between teams that often speak different languages.

Building a successful team for spatial intelligence requires multidisciplinary expertise, including system engineering, machine learning, and computer graphics.

The barriers to training large language models are primarily capital and talent, as specialized expertise and substantial financial resources are required.

The process of developing a model involves collaboration between machine learning and systems teams, from inception to the training run, often facing unresolved issues at launch.