The recent rise in relevance and diffusion of Artificial Intelligence (AI)-based systems and the increasing number and power of applications of AI methods invites a profound reflection on the impact of these innovative systems on scientific research and society at large. The Universal Scientific Education and Research Network (USERN), an organization that promotes initiatives to support interdisciplinary science and education across borders and actively works to improve science policy, collects here the vision of its Advisory Board members, together with a selection of AI experts, to summarize how we see developments in this exciting technology impacting science and society in the foreseeable future. In this review, we first attempt to establish clear definitions of intelligence and consciousness, then provide an overviewof AI’s state of the art and its applications. A discussion of the implications, opportunities, and liabilities of the diffusion of AI for research in a few representative fields of science follows this. Finally, we address the potential risks of AI to modern society, suggest strategies for mitigating those risks, and present our conclusions and recommendations.

The electron transport layer (ETL) is linchpin in perovskite solar cells (PSCs). It offers potent and discriminatory electron elicitation, minute resistivity, and lofty strength along with optimal device performance. In this study combined DFT and SCAPS-1D framework are used to investigate the optimized designs of CH₃NH₃Pb(I_1-xCl_x)₃ organic-inorganic perovskite-based solar cells. The analysis of structural stability, mechanical strength and optoelectronic traits was done by employing first-principle calculations with three different exchange-correlation functionals for A₂SnO₄(A = Sr, Ba) Ruddlesden-popper (RP) compounds. SCAPS-1D was used to analyze device performance by employing different ETLs in PSC architecture. The structural analysis reveal that Sr₂SnO₄ possesses a more stable structure in tetragonal phase with space group I4/mmm (139) than Ba₂SnO₄. Mechanical stability is corroborated through the reckoning of elastic constants, with Sr-based RP perovskite showing better mechanical properties as compared to Ba-based RP perovskite enunciating it auspicious for device fabrication. Electronic properties, analyzed through the band structure (BS) and density of state (DOS), confirm the semiconducting nature of both materials, with indirect band gap of 4.59 eV (Ba₂SnO₄) and 4.21 eV (Sr₂SnO₄). The optical analysis has stipulated that both materials are found to be good absorbers of ultraviolet (UV) radiation. An optimized device FTO/Sr₂SnO₄/MAPb(I_1-xCl_x)₃/Cu₂O/Au is contemplated here with an open-circuit voltage (Voc) of 1.257 V, a short-circuit current (Jsc) of 23.06 mA/cm2, fill factors (FF) of 83.57 %, and a theoretical power conversion efficiency (PCE) of 24.25 %. Overall, our findings reveal that Sr₂SnO₄ RP material have promising and potential features as a novel ETL material for employment in organic-inorganic PSC as a source of renewable energy.

Recent advances in machine learning have opened new avenues for optimizing detector designs in high-energy physics, where the complex interplay of geometry, materials, and physics processes has traditionally posed a significant challenge. In this work, we introduce the end-to-end. AI Detector Optimization framework (AIDO), which leverages a diffusion model as a surrogate for the full simulation and reconstruction chain, enabling gradient-based design exploration in both continuous and discrete parameter spaces. Although this framework is applicable to a broad range of detectors, we illustrate its power using the specific example of a sampling calorimeter, focusing on charged pions and photons as representative incident particles. Our results demonstrate that the diffusion model effectively captures critical performance metrics for calorimeter design, guiding the automatic search for a layer arrangement and material composition that align with known calorimeter principles. The success of this proof-of-concept study provides a foundation for the future applications of end-to-end optimization to more complex detector systems, offering a promising path toward systematically exploring the vast design space in next-generation experiments.

In this work we consider the problem of determining the identity of hadrons at high energies based on the topology of their energy depositions in dense matter, along with the time of the interactions. Using GEANT4 simulations of a homogeneous lead tungstate calorimeter with high transverse and longitudinal segmentation, we investigated the discrimination of protons, positive pions, and positive kaons at 100 GeV. The analysis focuses on the impact of calorimeter granularity by progressively merging detector cells and extracting features like energy deposition patterns and timing information. Two machine learning approaches, XGBoost and fully connected deep neural networks, were employed to assess the classification performance across particle pairs. The results indicate that fine segmentation improves particle discrimination, with higher granularity yielding more detailed characterization of energy showers. Additionally, the results highlight the importance of shower radius, energy fractions, and timing variables in distinguishing particle types. The XGBoost model demonstrated computational efficiency and interpretability advantages over deep learning for tabular data structures, while achieving similar classification performance. This motivates further work required to combine high- and low-level feature analysis, e.g., using convolutional and graph-based neural networks, and extending the study to a broader range of particle energies and types.

In this work, we proposed a novel approach for identifying quantum phase transitions in one-dimensional quantum many-body systems using AutoEncoder (AE), an unsupervised machine learning technique, with minimal prior knowledge. The training of the AEs is done with reduced density matrix (RDM) data obtained by Exact Diagonalization (ED) across the entire range of the driving parameter and thus no prior knowledge of the phase diagram is required. With this method, we successfully detect the phase transitions in a wide range of models with multiple phase transitions of different types, including the topological and the Berezinskii-Kosterlitz-Thouless transitions by tracking the changes in the reconstruction loss of the AE. The learned representation of the AE is used to characterize the physical phenomena underlying different quantum phases. Our methodology demonstrates a new approach to studying quantum phase transitions with minimal knowledge, small amount of needed data, and produces compressed representations of the quantum states.

We simulate hadrons impinging on a homogeneous lead tungstate (PbWO4PbWO4) calorimeter using GEANT4 software to investigate how the resulting light yield and its temporal structure, as detected by an array of light-sensitive sensors, can be processed by a neuromorphic computing system. Our model encodes temporal photon distributions as spike trains and employs a fully connected spiking neural network to estimate the total deposited energy, as well as the position and spatial distribution of the light emissions within the sensitive material. The extracted primitives offer valuable topological information about the shower development in the material, achieved without requiring a segmentation of the active medium. A potential nanophotonic implementation using III-V semiconductor nanowires is discussed. It can be both fast and energy efficient.

In this article we examine recent developments in the research area concerning the creation of end-to-end models for the complete optimization of measuring instruments. The models we consider rely on differentiable programming methods and on the specification of a software pipeline including all factors impacting performance — from the data-generating processes to their reconstruction and the inference on the parameters of interest — along with the careful specification of a utility function well aligned with the end goals of the experiment.

Building on previous studies originated within the MODE Collaboration, we focus specifically on applications involving instruments for particle physics experimentation, as well as industrial and medical applications that share the detection of radiation as their data-generating mechanism.

This report illustrates the most recent advancements in the area, and outlines, for each of the discussed applications as well as for automatic differentiation itself, ongoing and future work.

We study the application of a neural network architecture for identifying charged particle trajectories via unsupervised learning of delays and synaptic weights using a spike-time-dependent plasticity rule. In the considered model, the neurons receive time-encoded information on the position of particle hits in a tracking detector for a particle collider, modeled according to the geometry of the Compact Muon Solenoid Phase-2 detector. We show how a spiking neural network is capable of successfully identifying in a completely unsupervised way the signal left by charged particles in the presence of conspicuous noise from accidental or combinatorial hits, opening the way to applications of neuromorphic computing to particle tracking. The presented results motivate further studies investigating neuromorphic computing as a potential solution for real-time, low-power particle tracking in future high-energy physics experiments.

Interoperability is a central problem in digitization and sos engineering, which concerns the capacity of systems to exchange information and cooperate. The task to dynamically establish interoperability between heterogeneous cps at run-time is a challenging problem. Different aspects of the interoperability problem have been studied in fields such as sos, neural translation, and agent-based systems, but there are no unifying solutions beyond domain-specific standardization efforts. The problem is complicated by the uncertain and variable relations between physical processes and human-centric symbols, which result from, e.g., latent physical degrees of freedom, maintenance, re-configurations, and software updates. Therefore, we surveyed the literature for concepts and methods needed to automatically establish sos with purposeful cps communication, focusing on machine learning and connecting approaches that are not integrated in the present literature. Here, we summarize recent developments relevant to the dynamic interoperability problem, such as representation learning for ontology alignment and inference on heterogeneous linked data; neural networks for transcoding of text and code; concept learning-based reasoning; and emergent communication. We find that there has been a recent interest in deep learning approaches to establishing communication under different assumptions about the environment, language, and nature of the communicating entities. Furthermore, we present examples of architectures and discuss open problems associated with ai-enabled solutions in relation to sos interoperability requirements. Although these developments open new avenues for research, there are still no examples that bridge the concepts necessary to establish dynamic interoperability in complex sos, and realistic testbeds are needed.

The short-loading cycle is a construction task where a wheel loader scoops material from a nearby pile in order to move that material to the tipping body of a dump truck. The short-loading cycle is a vital task performed in high quantities and is often part of a more extensive never-ending process to move material for further refinement. This, together with the highly repetitive nature of the short-loading cycle, makes it a suitable candidate for automation. However, the short-loading cycle is a complex task where the mechanics of the wheel loader together with the interaction between the wheel loader and the environment needs to be considered. This must be achieved while maintaining some productivity goal and, concurrently, minimizing the used energy. The main objective of this work is to analyze the short-loading cycle, assess the current state of research in this field, and discuss the steps required to progress towards a minimal viable product consisting of individual automation solutions that can perform the short-loading cycle well enough to be used by early adopters. This is achieved through a comprehensive literature study and consequent analysis of the review results. From this analysis, the requirements of an MVP are defined and some gaps which are currently hindering the realization of the MVP are presented.