Isaac Triguero

Isaac Triguero, Daniel Molina, Javier Poyatos et al.

Most applications of Artificial Intelligence (AI) are designed for a confined and specific task. However, there are many scenarios that call for a more general AI, capable of solving a wide array of tasks without being specifically designed for them. The term General-Purpose Artificial Intelligence Systems (GPAIS) has been defined to refer to these AI systems. To date, the possibility of an Artificial General Intelligence, powerful enough to perform any intellectual task as if it were human, or even improve it, has remained an aspiration, fiction, and considered a risk for our society. Whilst we might still be far from achieving that, GPAIS is a reality and sitting at the forefront of AI research. This work discusses existing definitions for GPAIS and proposes a new definition that allows for a gradual differentiation among types of GPAIS according to their properties and limitations. We distinguish between closed-world and open-world GPAIS, characterising their degree of autonomy and ability based on several factors such as adaptation to new tasks, competence in domains not intentionally trained for, ability to learn from few data, or proactive acknowledgment of their own limitations. We propose a taxonomy of approaches to realise GPAIS, describing research trends such as the use of AI techniques to improve another AI (AI-powered AI) or (single) foundation models. As a prime example, we delve into GenAI, aligning them with the concepts presented in the taxonomy. We explore multi-modality, which involves fusing various types of data sources to expand the capabilities of GPAIS. Through the proposed definition and taxonomy, our aim is to facilitate research collaboration across different areas that are tackling general purpose tasks, as they share many common aspects. Finally, we discuss the state of GPAIS, prospects, societal implications, and the need for regulation and governance.

Alberto D. Cencillo, Leonardo Concepción, Julián Luengo et al.

Unscheduled trips of high-power pulsed converters are a leading source of downtime at large accelerator facilities. At the Spallation Neutron Source (SNS), the High Voltage Converter Modulators (HVCMs) are consistently the second-largest contributor to lost beam time. Each HVCM pulse is recorded across sensor channels spanning currents, voltages, and magnetic fluxes, whose mutual interactions encode the operating state of the system. Fault precursors do not manifest uniformly across these channels: depending on fault type, they may alter the temporal structure of individual signals, change the statistical dependencies among channels, or both. Existing deep-learning approaches typically process multi-channel signals with standard convolutional pipelines that entangle temporal and cross-channel operations from the first layer, giving the model no explicit mechanism to represent channel independence or structured inter-channel interaction. We hypothesise that architectural inductive bias, specifically the ordering of temporal filtering and cross-channel mixing, plays a central role in detection performance on this class of data. To test this, we vary the order in which these two operations are applied, and examine whether per-pulse adaptive channel reweighting further improves sensitivity. Evaluated on the public HVCM dataset across all four SNS subsystems (RFQ, DTL, CCL, SCL), our best variant achieves a pooled AUC-PR of 0.816 and AUC-ROC of 0.934, outperforming the state of the art on most subsystems and five of the six fault families. Ablations identify three dominant input channels and link per-fault-family performance to whether precursors manifest as amplitude shifts in individual channels or as subtler patterns requiring joint channel representations to surface.

Christoph Bergmeir, Frits de Nijs, Evgenii Genov et al.

Predict+Optimize frameworks integrate forecasting and optimization to address real-world challenges such as renewable energy scheduling, where variability and uncertainty are critical factors. This paper benchmarks solutions from the IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling, focusing on forecasting renewable production and demand and optimizing energy cost. The competition attracted 49 participants in total. The top-ranked method employed stochastic optimization using LightGBM ensembles, and achieved at least a 2% reduction in energy costs compared to deterministic approaches, demonstrating that the most accurate point forecast does not necessarily guarantee the best performance in downstream optimization. The published data and problem setting establish a benchmark for further research into integrated forecasting-optimization methods for energy systems, highlighting the importance of considering forecast uncertainty in optimization models to achieve cost-effective and reliable energy management. The novelty of this work lies in its comprehensive evaluation of Predict+Optimize methodologies applied to a real-world renewable energy scheduling problem, providing insights into the scalability, generalizability, and effectiveness of the proposed solutions. Potential applications extend beyond energy systems to any domain requiring integrated forecasting and optimization, such as supply chain management, transportation planning, and financial portfolio optimization.

Timothy Cargan, Dario Landa-Silva, Isaac Triguero

As the use of solar power increases, having accurate and timely forecasts will be essential for smooth grid operators. There are many proposed methods for forecasting solar irradiance / solar power production. However, many of these methods formulate the problem as a time-series, relying on near real-time access to observations at the location of interest to generate forecasts. This requires both access to a real-time stream of data and enough historical observations for these methods to be deployed. In this paper, we propose the use of Global methods to train our models in a generalised way, enabling them to generate forecasts for unseen locations. We apply this approach to both classical ML and state of the art methods. Using data from 20 locations distributed throughout the UK and widely available weather data, we show that it is possible to build systems that do not require access to this data. We utilise and compare both satellite and ground observations (e.g. temperature, pressure) of weather data. Leveraging weather observations and measurements from other locations we show it is possible to create models capable of accurately forecasting solar irradiance at new locations. This could facilitate use planning and optimisation for both newly deployed solar farms and domestic installations from the moment they come online. Additionally, we show that training a single global model for multiple locations can produce a more robust model with more consistent and accurate results across locations.

Oroel Ipas, Guillermo Gomez-Trenado, Rocío Romero-Zaliz et al.

Selecting which instances to label is a key challenge in low-label tabular learning. For recent Tabular Foundation Models such as TabPFN, context selection directly determines predictive performance. Supervised oracle experiments show that carefully chosen labeled context sets can strongly outperform random selection under the same labeling budget. However, the cold-start setting, where instances must be selected before any labels are available, has received little attention in the TFM literature. This problem is fundamentally geometric. In vision and language, foundation models induce embedding spaces where simple geometric selection methods are effective. In contrast, tabular instance selection has so far been performed predominantly in the original tabular space, which lacks a natural metric; heterogeneous types, mixed scales, and nonlinear interactions make raw-space distances unreliable for context construction, and original-space selection falls below random on the majority of datasets as the budget grows. We propose LUCoS (Latent Unsupervised Context Selection), which replaces raw-feature geometry with the latent geometry induced by embeddings from an unsupervised Prior-Fitted Network (PFN) and selects representative medoids as context. Evaluated on 67 OpenML-CC18 datasets across six low-label budgets, LUCoS ranks first under mean AUC, ACC, and F1, with conclusions stable across metrics and dataset-level robustness checks. A gain decomposition reveals a simple mechanism: at the smallest budgets, the main benefit comes from enforcing coverage; as the budget increases, the decisive factor becomes the representation space in which coverage is measured. LUCoS mitigates failures of original feature space selection, showing that reliable unsupervised context selection depends less on selector sophistication than on defining representativeness in a meaningful representation geometry.

Direnc Pekaslan, Jose Maria Alonso-Moral, Kasun Bandara et al.

This paper presents the real-world smart-meter dataset and offers an analysis of solutions derived from the Energy Prediction Technical Challenges, focusing primarily on two key competitions: the IEEE Computational Intelligence Society (IEEE-CIS) Technical Challenge on Energy Prediction from Smart Meter data in 2020 (named EP) and its follow-up challenge at the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) in 2021 (named as XEP). These competitions focus on accurate energy consumption forecasting and the importance of interpretability in understanding the underlying factors. The challenge aims to predict monthly and yearly estimated consumption for households, addressing the accurate billing problem with limited historical smart meter data. The dataset comprises 3,248 smart meters, with varying data availability ranging from a minimum of one month to a year. This paper delves into the challenges, solutions and analysing issues related to the provided real-world smart meter data, developing accurate predictions at the household level, and introducing evaluation criteria for assessing interpretability. Additionally, this paper discusses aspects beyond the competitions: opportunities for energy disaggregation and pattern detection applications at the household level, significance of communicating energy-driven factors for optimised billing, and emphasising the importance of responsible AI and data privacy considerations. These aspects provide insights into the broader implications and potential advancements in energy consumption prediction. Overall, these competitions provide a dataset for residential energy research and serve as a catalyst for exploring accurate forecasting, enhancing interpretability, and driving progress towards the discussion of various aspects such as energy disaggregation, demand response programs or behavioural interventions.

Juan S. Angarita-Zapata, Antonio D. Masegosa, Isaac Triguero

Intelligent Transportation Systems are producing tons of hardly manageable traffic data, which motivates the use of Machine Learning (ML) for data-driven applications, such as Traffic Forecasting (TF). TF is gaining relevance due to its ability to mitigate traffic congestion by forecasting future traffic states. However, TF poses one big challenge to the ML paradigm, known as the Model Selection Problem (MSP): deciding the most suitable combination of data preprocessing techniques and ML method for traffic data collected under different transportation circumstances. In this context, Automated Machine Learning (AutoML), the automation of the ML workflow from data preprocessing to model validation, arises as a promising strategy to deal with the MSP in problem domains wherein expert ML knowledge is not always an available or affordable asset, such as TF. Various AutoML frameworks have been used to approach the MSP in TF. Most are based on online optimisation processes to search for the best-performing pipeline on a given dataset. This online optimisation could be complemented with meta-learning to warm-start the search phase and/or the construction of ensembles using pipelines derived from the optimisation process. However, given the complexity of the search space and the high computational cost of tuning-evaluating pipelines generated, online optimisation is only beneficial when there is a long time to obtain the final model. Thus, we introduce AutoEn, which is a simple and efficient method for automatically generating multi-classifier ensembles from a predefined set of ML pipelines. We compare AutoEn against Auto-WEKA and Auto-sklearn, two AutoML methods commonly used in TF. Experimental results demonstrate that AutoEn can lead to better or more competitive results in the general-purpose domain and in TF.

Alberto D. Cencillo, Leonardo Concepción, Isaac Triguero et al.

Anomaly detection in multivariate time series is a critical task across a wide range of real-world applications, where abnormal behaviour is rare, labels are unavailable, and the cost of a miss is high. The central challenge is learning a characterisation of normality precise enough to flag deviations. Representation self-supervised learning, typically through contrastive approaches, addresses this by embedding temporal patches into a latent space where normality occupies a well-defined region, with anomalies detected by geometric deviation. However, contrastive approaches shape this space indirectly through pair-sampling heuristics, providing no explicit control over the geometric structure that distance-based scoring requires. This means how tightly normal representations are grouped, and whether distances are directionally meaningful. We present VACE (Velocity-Aligned Channel Embeddings), a self-supervised anomaly detection method that represents normality as a compact, directionally coherent region in the embedding space. To this end, VACE trains a channel-aware encoder through a velocity-consistency objective, with no negatives and no synthetic anomalies, so that normal trajectories are locally smooth and aligned. At test time, a Mahalanobis positional score and a velocity-bank directional score are combined multiplicatively, flagging points that are simultaneously off-distribution and dynamically atypical. Despite its simplicity, VACE achieves state-of-the-art performance on TSB-AD-M under rigorous evaluation, significantly outperforming more complex methods trained on substantially larger budgets.

Juan Jose Herrera-Aranda, Guillermo Gomez-Trenado, Francisco Herrera et al.

Zero-Shot Learning is an important paradigm within General-Purpose Artificial Intelligence Systems, particularly in those that operate in open-world scenarios where systems must adapt to new tasks dynamically. Semantic spaces play a pivotal role as they bridge seen and unseen classes, but whether human-annotated or generated by a machine learning model, they often contain noisy, redundant, or irrelevant attributes that hinder performance. To address this, we introduce a partitioning scheme that simulates unseen conditions in an inductive setting (which is the most challenging), allowing attribute relevance to be assessed without access to semantic information from unseen classes. Within this framework, we study two complementary feature-selection strategies and assess their generalisation. The first adapts embedded feature selection to the particular demands of ZSL, turning model-driven rankings into meaningful semantic pruning; the second leverages evolutionary computation to directly explore the space of attribute subsets more broadly. Experiments on five benchmark datasets (AWA2, CUB, SUN, aPY, FLO) show that both methods consistently improve accuracy on unseen classes by reducing redundancy, but in complementary ways: RFS is efficient and competitive though dependent on critical hyperparameters, whereas GA is more costly yet explores the search space more broadly and avoids such dependence. These results confirm that semantic spaces are inherently redundant and highlight the proposed partitioning scheme as an effective tool to refine them under inductive conditions.

Nasser Alkhulaifi, Ismail Gokay Dogan, Timothy R. Cargan et al.

Decision-making under uncertainty in energy management is complicated by unknown parameters hindering optimal strategies, particularly in Battery Energy Storage System (BESS) operations. Predict-Then-Optimise (PTO) approaches treat forecasting and optimisation as separate processes, allowing prediction errors to cascade into suboptimal decisions as models minimise forecasting errors rather than optimising downstream tasks. The emerging Decision-Focused Learning (DFL) methods overcome this limitation by integrating prediction and optimisation; however, they are relatively new and have been tested primarily on synthetic datasets or small-scale problems, with limited evidence of their practical viability. Real-world BESS applications present additional challenges, including greater variability and data scarcity due to collection constraints and operational limitations. Because of these challenges, this work leverages Automated Feature Engineering (AFE) to extract richer representations and improve the nascent approach of DFL. We propose an AFE-DFL framework suitable for small datasets that forecasts electricity prices and demand while optimising BESS operations to minimise costs. We validate its effectiveness on a novel real-world UK property dataset. The evaluation compares DFL methods against PTO, with and without AFE. The results show that, on average, DFL yields lower operating costs than PTO and adding AFE further improves the performance of DFL methods by 22.9-56.5% compared to the same models without AFE. These findings provide empirical evidence for DFL's practical viability in real-world settings, indicating that domain-specific AFE enhances DFL and reduces reliance on domain expertise for BESS optimisation, yielding economic benefits with broader implications for energy management systems facing similar challenges.

Javier Poyatos, Javier Del Ser, Salvador Garcia et al.

In Artificial Intelligence, there is an increasing demand for adaptive models capable of dealing with a diverse spectrum of learning tasks, surpassing the limitations of systems devised to cope with a single task. The recent emergence of General-Purpose Artificial Intelligence Systems (GPAIS) poses model configuration and adaptability challenges at far greater complexity scales than the optimal design of traditional Machine Learning models. Evolutionary Computation (EC) has been a useful tool for both the design and optimization of Machine Learning models, endowing them with the capability to configure and/or adapt themselves to the task under consideration. Therefore, their application to GPAIS is a natural choice. This paper aims to analyze the role of EC in the field of GPAIS, exploring the use of EC for their design or enrichment. We also match GPAIS properties to Machine Learning areas in which EC has had a notable contribution, highlighting recent milestones of EC for GPAIS. Furthermore, we discuss the challenges of harnessing the benefits of EC for GPAIS, presenting different strategies to both design and improve GPAIS with EC, covering tangential areas, identifying research niches, and outlining potential research directions for EC and GPAIS.

Heda Song, Mercedes Torres Torres, Ender Özcan et al.

Few-shot learning focuses on learning a new visual concept with very limited labelled examples. A successful approach to tackle this problem is to compare the similarity between examples in a learned metric space based on convolutional neural networks. However, existing methods typically suffer from meta-level overfitting due to the limited amount of training tasks and do not normally consider the importance of the convolutional features of different examples within the same channel. To address these limitations, we make the following two contributions: (a) We propose a novel meta-learning approach for aggregating useful convolutional features and suppressing noisy ones based on a channel-wise attention mechanism to improve class representations. The proposed model does not require fine-tuning and can be trained in an end-to-end manner. The main novelty lies in incorporating a shared weight generation module that learns to assign different weights to the feature maps of different examples within the same channel. (b) We also introduce a simple meta-level dropout technique that reduces meta-level overfitting in several few-shot learning approaches. In our experiments, we find that this simple technique significantly improves the performance of the proposed method as well as various state-of-the-art meta-learning algorithms. Applying our method to few-shot image recognition using Omniglot and miniImageNet datasets shows that it is capable of delivering a state-of-the-art classification performance.

Daniel Peralta, Isaac Triguero, Salvador García et al.

Fingerprint classification is one of the most common approaches to accelerate the identification in large databases of fingerprints. Fingerprints are grouped into disjoint classes, so that an input fingerprint is compared only with those belonging to the predicted class, reducing the penetration rate of the search. The classification procedure usually starts by the extraction of features from the fingerprint image, frequently based on visual characteristics. In this work, we propose an approach to fingerprint classification using convolutional neural networks, which avoid the necessity of an explicit feature extraction process by incorporating the image processing within the training of the classifier. Furthermore, such an approach is able to predict a class even for low-quality fingerprints that are rejected by commonly used algorithms, such as FingerCode. The study gives special importance to the robustness of the classification for different impressions of the same fingerprint, aiming to minimize the penetration in the database. In our experiments, convolutional neural networks yielded better accuracy and penetration rate than state-of-the-art classifiers based on explicit feature extraction. The tested networks also improved on the runtime, as a result of the joint optimization of both feature extraction and classification.