After a brief historical overview of the history of AI and how it has been used to date in basic and translational biomedical research, we will review the fundamental concepts of AI and Machine Learning (ML), outlining key methods, algorithms, and capabilities. The critical concept of the AI Foundational Model will be explored, leading naturally to a explanation and demonstration of Generative AI in the multi-model context. Several AI platforms will be examined, driven by relevant biomedical use cases, ranging from biomedical imaging, single cell analysis, spatial transcriptomics, bioinformatics analysis, and Generative AI-enabled literature review(s). We will then review the need for data volume, quality, and integrity; as well as the required privacy and security considerations. Key concepts of fairness, bias, safety, and ethical use will be emphasized. Current strengths and limitations of AI methods will be reviewed, with consideration given to current and future potential uses. We will leave adequate time for Q&A and discussion.