The AI field is still developing its understanding of policy implications, and there's a growing need for researchers to engage in policy discussions and consider the societal impact of their work. People in both AI research and civil society are often concerned about fairness, but they operate under different constraints.

The differences between academia and industry are often overstated; both operate within overlapping distributions today, although they have fundamentally different incentives that drive their behaviors.

The AI research community generally favors openness, as it aligns with both broad interests and individual goals of researchers and companies.

I prefer the term 'engineering' over 'research' because engineering is about building and creating, while research can often be more abstract. I want XAI to be a company focused on practical applications rather than just a lab.

Thirty years ago, the interests of basic science and industry were quite different, but today they heavily overlap, with both fields focusing on similar questions and challenges.

There's increasing acceptance of transitioning from academia to industry, as innovations often stem from academic research, and pursuing entrepreneurship can be a viable path for scientists. Most drugs on the market originate from discoveries made in academia, which are then commercialized by biotech or pharmaceutical companies.

The project highlighted the importance of adapting research techniques to ensure practical usability in real-world applications.

In AI research, companies are defining their own values, but society should also participate in determining what values to imbue in future AI systems.

There's a need for better collaboration between AI researchers and policy experts to ensure that technical developments align with societal needs and ethical considerations.