Foundation Models Beyond Generative AI: Emerging Applications Across Science, Industry and Public Services

Candace Yano

Foundation Models Beyond Generative AI: Emerging Applications Across Science, Industry and Public Services

Author(s)	Candace Yano
Country	United States
Abstract	Foundation models have emerged as a major development in Artificial Intelligence, initially gaining widespread attention through generative applications such as conversational systems, image generation, code generation, and multimodal content creation. However, the significance of foundation models extends well beyond conventional generative AI. Their ability to learn broad representations from large and diverse datasets creates opportunities for scientific discovery, industrial optimisation, healthcare, climate modelling, infrastructure management, public administration, and decision-support systems. Increasingly, foundation models are being developed for domains involving scientific measurements, biological sequences, remote sensing, time-series data, industrial processes, and multimodal information rather than only human-generated text and images. This study examines emerging applications of foundation models beyond generative AI across science, industry, and public services. A qualitative and descriptive research methodology based on secondary academic literature, technical publications, institutional reports, and policy documents is adopted. The study explores scientific foundation models, industrial time-series models, geospatial and Earth-observation models, biological and healthcare models, and public-sector decision-support applications. Particular attention is given to transfer learning, multimodal learning, domain adaptation, predictive analytics, simulation, and AI-assisted scientific discovery. The analysis indicates that foundation models can potentially reduce the cost of developing specialised AI systems, improve cross-domain knowledge transfer, accelerate scientific analysis, and support more adaptive decision-making. Nevertheless, significant challenges remain concerning data governance, model reliability, domain-specific validation, computational costs, explainability, cybersecurity, intellectual property, and responsible deployment. The study concludes that the next phase of foundation-model development will increasingly involve models designed not merely to generate content but to understand complex systems, analyse scientific observations, predict future states, and support high-impact decisions.
Keywords	Foundation Models, Artificial Intelligence, Scientific AI, Machine Learning, Multimodal AI, Industry 4.0, Public Services, Predictive Analytics, Digital Transformation, Domain-Specific AI.
Field	Engineering
Published In	Volume 5, Issue 6, November-December 2023
Published On	2023-11-01

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Foundation Models Beyond Generative AI: Emerging Applications Across Science, Industry and Public Services

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