Recent artificial intelligence (AI) breakthroughs in understanding and generating complex data have immense potential to accelerate biological research, but key gaps remain in applying these advances to the life sciences. This meeting will explore how AI can help decipher biological codes, uncover patterns in large-scale biological data, and develop foundation models and domain-specific methods to drive biological discovery. Scientific inquiry presents unique challenges for AI. Ensuring widespread benefit requires careful consideration of training data acquisition and inclusion, as well as model design. Understanding what our models have learned is essential to: * Verify that biological relationships, rather than experimental artifacts, are being used. * Enable exploratory analysis via visualization interfaces. * Establish solid generalizable principles that can inform experimental validation. Specific meeting goals include: * Showcase cutting-edge applications of AI to biological problems. * Identify challenges and best practices for biological data acquisition and inclusion in AI. * Discuss interpretability approaches to understand what biological relationships AI models learn. * Foster collaboration between AI and biology experts to develop innovative solutions. * Train the next generation of scientists at the interface of AI and biology. Anticipated outcomes include new interdisciplinary research directions, technical capabilities, and collaborations. Attendees will gain insight into leveraging AI to advance their research. The meeting will explore how to move this nascent field forward, highlighting research projects exploring the interface of AI and biology from visionary experts building the future of the life sciences.