Advancements in Earth system science have seen a surge in diverse datasets. Earth System Data Cubes (ESDCs) have been introduced to efficiently handle this influx of high-dimensional data. ESDCs offer a structured, intuitive framework for data analysis, organising information within spatio-temporal grids. The structured nature of ESDCs unlocks significant opportunities for Artificial Intelligence (AI) applications. By providing well-organised data, ESDCs are ideally suited for a wide range of sophisticated AI-driven tasks. An automated framework for creating AI-focused ESDCs with minimal user input could significantly accelerate the generation of task-specific training data. Here we introduce cubo, an open-source Python tool designed for easy generation of AI-focused ESDCs. Utilising collections in SpatioTemporal Asset Catalogs (STAC) that are stored as Cloud Optimised GeoTIFFs (COGs), cubo efficiently creates ESDCs, requiring only central coordinates, spatial resolution, edge size, and time range.

Unicode Normalization is a procedure for transforming Unicode text into different levels of equivalence, based on rules outlined by the Unicode Standard [1]. The goal is to ensure consistent treatment of certain types of text across applications and systems. Graphemes are the basic units of writing and include individual letters, symbols, or glyphs that convey meaning within a language system [2]. Each grapheme typically represents at least one phoneme or sound component of spoken language. Many speakers of Indian, Bangladeshi, and Thai languages utilize abugidas, also referred to as alphasyllabaries, consisting of over 1.3 billion individuals. Unfortunately, despite the large number of users, these languages encounter obstacles when it comes to natural language processing due to scarce resources and constraints in technology. Nevertheless, there exists vast academic and industrial interest in devising novel NLP techniques for these languages. Our group has developed a novel Indic Unicode Normalizer designed to overcome typical problems encountered in online Indic Abugida language datasets.

From the earliest experiments in the 20th century to the utilization of large language models and transformers, dialogue systems research has continued to evolve, playing crucial roles in numerous fields. This paper offers a comprehensive review of these systems, tracing their historical development and examining their fundamental operations. We analyze popular and emerging datasets for training and survey key contributions in dialogue systems research, including traditional systems and advanced machine learning methods. Finally, we consider conventional and transformer-based evaluation metrics, followed by a short discussion of prevailing challenges and future prospects in the field.

Over the past decade, machine learning (ML) has become a critical component of countless applications and services in a variety of domains. Fields ranging from healthcare to autonomous vehicles have been transformed by the use of ML techniques. Machine learning's increasing importance to real-world applications brought awareness of a new field focused on ML in practice - machine learning systems (or, as some call it, MLOps). This field acts as a bridging point between the domains of computer systems and machine learning, considering the new challenges of machine learning with a lens shaped by traditional systems research. So what are these "ML challenges"?

In 2011 the Autonomous Agents and Multiagent Systems (AAMAS) conference series celebrated its 10th anniversary, having begun as the successful merger of three related events that had run for some years previously.

Recommender systems are tools for interacting with large and complex information spaces. They provide a personalized view of such spaces, prioritizing items likely to be of interest to the user. The field, christened in 1995, has grown enormously in the variety of problems addressed and techniques employed, as well as in its practical applications. Recommender systems research has incorporated a wide variety of artificial intelligence techniques including machine learning, data mining, user modeling, case-based reasoning, and constraint satisfaction, among others. Personalized recommendations are an important part of many on-line e-commerce applications such as Amazon.com, Netflix, and Pandora. This wealth of practical application experience has provided inspiration to researchers to extend the reach of recommender systems into new and challenging areas. The purpose of the articles in this special issue is to take stock of the current landscape of recommender systems research and identify directions the field is now taking. This article provides an overview of the current state of the field and introduces the various articles in the special issue.

This article summarizes the findings of a 1992 study of knowledge-based systems research and applications in Japan. Representatives of universities and businesses were chosen by the Japan Technology Evaluation Center to investigate the state of the technology in Japan relative to the United States. The panel's report focused on applications, tools, and research and development in universities and industry and on major national projects.