Digital twin and foundation models: A new frontier
conference paper
A Foundation Model (FM) possesses extensive learning capabilities; it learns from diverse datasets. This is our opportunity to enhance the functionality of Digital Twin (DT) solutions in various sectors. The integration of FMs into the DT application is particularly relevant due to the increased prevalence of Artificial Intelligence (AI) in real-world applications. In this position paper, we begin to explain a novel perspective on this integration by exploring the potential of enhanced predictive analytics, adaptive learning, and improved handling of complex data within DTs — by way of designated purposes. Ultimately, we aim to uncover hidden value of enhanced reliable decision-making, whereby systems can make more informed, accurate and timely decisions, based on comprehensive data analytics and predictive insights. Mentioning selected ongoing cases, we highlight some benefits and challenges, like computational demand, data privacy concerns, and the need for transparency in AI decision-making. Underscoring the transformative implications of integrating FMs into the DT paradigm, a shift towards more intelligent, versatile and dynamic systems becomes clearer. We caution against the challenges of computational resources, safety considerations and interpretability. This step is pivotal towards unlocking unprecedented potential for advanced data-driven solutions in various industries.
TNO Identifier
994243
Source title
ICAART 2024 16th International Conference on Agents and Artificial Intelligence, Rome, Italy, 24-26 February 2024
Collation
7 p.
Files
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