###### Abstract  
Artificial intelligence (AI) has become deeply intertwined with scientific inquiry and experimentation. This Special Issue, \"AI in Experiments: Present Status and Future Prospects\", provides a timely snapshot of AI's evolving role across diverse experimental domains. The symbiotic relationship between AI and experiments is reshaping scientific workflows. From physics to biology and materials science, machine learning and statistical models have woven themselves into every facet of research--from hypothesis generation to experimental design, data analysis, and communication of results. Techniques like deep neural networks, reinforcement learning, and generative models are augmenting human capabilities and redefining the boundaries of achievable science. The integration of AI into experiments has an interesting timeline, beginning in the 1990s, when machine learning methods like neural networks were first applied to analyze physics detector data. In the 2000s, breakthroughs in statistical learning approaches ushered in wider adoption across scientific fields. The 2010s saw rapid advances in deep learning fuel the proliferation of AI techniques. And in the past few years, generative models have opened new frontiers in simulation and data augmentation. As computing power continues to grow exponentially, the future promises more creative and accelerated scientific practices driven by AI. In physics, AI has emerged as an invaluable tool for analyzing the multi-dimensional data from detectors and telescopes searching for rare events like cosmic rays, photons from distant pulsars, or elusive dark matter. Machine learning models help distinguish signals from overwhelming background noise in these experiments. In particular, dimensionality reduction techniques like principal component analysis and deep neural networks enhance the discrimination power of direct cosmic ray detection [1, 2, 3]. Fundamental physics experiments also rely on AI to discern anomalous particle signatures from collider data and pick out gravitational wave signals buried in detector noise. Unsupervised neural networks are gaining traction in both fields by learning from raw data in a self-directed manner without the need for training labels [4, 5, 6]. In gamma-ray astronomy, with the advent of sparsely distributed arrays of Imaging Air Cherenkov Telescopes, exemplified by the INAF ASTRI (Astrophysics with Italian Replicating Technology Mirrors) Mini-Array [7, 8], AI promises to boost the sensitivity. Sophisticated image processing and classification algorithms help reduce interference from cosmic rays and enhance gamma-ray signals. This entails harnessing the power of artificial intelligence (AI) through advanced image and pattern recognition techniques. The strategic application of AI promises substantial advancements in gamma-ray astronomy, leading to improved instrument sensitivities and further insight into the mysteries of the universe [9, 10]. Shifting our attention to the domain of satellite instruments, present AI research embarks on an innovative journey into the realm of cloud masking for satellite images. Departing from conventional techniques used in atmospheric monitoring from space, it explores groundbreaking algorithms designed for binary cloud masking in satellite imagery. These innovations hold the potential to revolutionize not only weather prediction, climate modeling, and environmental monitoring but also the precision calibration of instruments dedicated to measuring cosmic rays, as exemplified by the JEM-EUSO project [11]. Across experiments, AI has become indispensable for tasks like experimental design, real-time monitoring, simulation, and data analysis. AI-based simulations allow rapid, economical modeling of complex phenomena. Generative models transform noisy, low-resolution data into realistic, high-fidelity outputs. For example, in the ARGO-YBJ cosmic ray experiment, a multiscale AI method was used to help discriminate gamma ray signals from hadronic background events [12; 13].