  Computing in the life sciences has undergone a transformative evolution, from
early computational models in the 1950s to the applications of artificial
intelligence (AI) and machine learning (ML) seen today. This paper highlights
key milestones and technological advancements through the historical
development of computing in the life sciences. The discussion includes the
inception of computational models for biological processes, the advent of
bioinformatics tools, and the integration of AI/ML in modern life sciences
research. Attention is given to AI-enabled tools used in the life sciences,
such as scientific large language models and bio-AI tools, examining their
capabilities, limitations, and impact to biological risk. This paper seeks to
clarify and establish essential terminology and concepts to ensure informed
decision-making and effective communication across disciplines.
