A forward looking discussion on safe AI for science, addressing risks and how labs can safely adopt new technologies.
In many labs, data is still fragmented across multiple systems, making it difficult for AI to access the context it needs without manual effort. As a result, much of today’s AI remains disconnected from everyday scientific work.
At the same time, adopting AI in research raises important questions around data security, IP protection, and responsible use. How can labs innovate with AI while maintaining control and compliance?
You can expect:
- Practical perspectives on connecting AI to scientific data and workflows
- Real-world examples of how this shift can improve efficiency and discovery
- Discussion on how to approach AI adoption while addressing security, IP, and governance concerns
