In the era of AI-driven transformations, foundation models (FMs), like large-scale language and vision models, have become pivotal in various applications, from natural language processing to computer vision. These models, with their immense capabilities, reshape the future of scientific research and the broader human society, but also introduce challenges in their in-the-wild/real-world deployments. The Workshop on FMs in the wild delves into the urgent need for these models to be useful when deployed in our societies. The significance of this topic cannot be overstated, as the real-world implications of these models impact everything from daily information access to critical decision-making in fields like medicine and finance. Stakeholders, from developers to end-users, care deeply about this because the successful integration of FMs into in-the-wild frameworks necessitates a careful consideration of adaptivity, reliability and efficiency. Some of the fundamental questions that this workshop aims to address are:$$\textbf{1. Real-world Adaptation:}$$ In practical applications, how can we leverage the comprehensive knowledge in FMs to adapt them for specific domains, such as drug discovery, education, or clinical health?$$\textbf{2. Reliability and Responsibility:}$$ How can foundation models work reliably outside their training distribution? And how can we address issues like hallucination and privacy?$$\textbf{3. Safety, Ethics, and Fairness in Society:}$$ How do we ensure that the deployment of FMs preserving safety, ethics, and fairness within society, safeguarding against biases and unethical use?$$\textbf{4. Practical Limitations in Deployment:}$$ How can FMs tackle challenges in practical applications, such as system constraints, computational costs, data acquisition barriers, response time demands?