With all the buzz around generative AI in business, I am lifting the cover on how GenAI is changing the way we do science. I was glad to give a talk at AWS’s event on Generative AI in Research. Key messages include

1. GenAI and foundation models are having a profound impact on the way we do science and how the value and nature of scientific expertise are changing.
2. How to enable scientists to build their AI Copilots, which integrate domain-specific workflows, trustworthiness criteria, and interpretability. The knowledge from human scientists goes far beyond what is captured in data and academic papers.
3. How we are engaging in international collaborations for the training and usage of science foundation models.
4. How we are building responsible AI throughout the entire life cycle of scientific discovery and impact pathways.

References

Zhu, L., Xu, X., Lu, Q., Governatori, G., Whittle, J., 2022. AI and Ethics—Operationalizing Responsible AI, in: Chen, F., Zhou, J. (Eds.), Humanity Driven AI. Springer International Publishing, Cham, pp. 15–33. https://doi.org/10.1007/978-3-030-72188-6_2

Lu, Q., Zhu, L., Xu, X., Whittle, J., Xing, Z., 2022. Towards a Roadmap on Software Engineering for Responsible AI, in: 1st International Conference on AI Engineering – Software Engineering for AI (CAIN). https://dl.acm.org/doi/abs/10.1145/3522664.3528607

Lu, Q., 2023. Towards Responsible AI in the Era of ChatGPT: Pattern-Oriented Reference Architecture for Designing Foundation Model based AI Systems. https://arxiv.org/abs/2304.11090

Lu, Q., Zhu, L., Xu, X., Xing, Z., Whittle, J., 2023. A Framework for Designing Foundation Model based Systems URL https://arxiv.org/abs/2305.05352v1

Lo, S.K., Liu, Y., Lu, Q., Wang, C., Xu, X., Paik, H.-Y., Zhu, L., 2023. Toward Trustworthy AI: Blockchain-Based Architecture Design for Accountability and Fairness of Federated Learning Systems. IEEE Internet of Things Journal 10, 3276–3284. https://doi.org/10.1109/JIOT.2022.3144450

Lo, S.K., Liu, Y., Lu, Q., Wang, C., Xu, X., Paik, H.-Y., Zhu, L., 2021. Blockchain-based Trustworthy Federated Learning Architecture. https://doi.org/10.48550/arXiv.2108.06912

Xia, B., Bi, T., Xing, Z., Lu, Q., Zhu, L., 2023. An Empirical Study on Software Bill of Materials: Where We Stand and the Road Ahead. Presented at the ICSE, arXiv. https://doi.org/10.48550/arXiv.2301.05362

Xu, X., Wang, C., Wang, Z. (Jef), Lu, Q., Zhu, L., 2022. Dependency tracking for risk mitigation in machine learning (ML) systems, in: Proceedings of the 44th International Conference on Software Engineering: Software Engineering in Practice,https://doi.org/10.1145/3510457.3513058