Research on legged robots design and locomotion has mainly been fueled by the desire to deploy teleoperated or autonomous systems in otherwise inaccessible terrains. While wheeled and tracked vehicles are broadly used on paved surfaces (currently no more than 7% of Earth's land surface) and fields for agriculture and forestry (roughly 46.6%) (Hooke et al., 2013), they have poor performance on sandy, rocky, and other unmodified natural terrains (roughly 46.5%). Nearly half of Earth's ice-free land area has not been modified by humans (Hooke et al., 2013), and is often inhabited by animals that use their legs to survive by walking (Schroer et al., 2004), running (Raibert et al., 2008), climbing (Grieco et al., 1998), jumping (Zhang et al., 2017), and swimming (Song et al., 2016). In addition to accessing natural terrains, legs can also enable improved access to human environments by climbing stairs, changing body height to crawl through confined spaces, or carefully stepping around clutter or hazards. Potential real world applications such as industrial inspection can also rely on legged robots to regularly validate visual, thermal, and acoustic data at waypoints in monitoring and exploration tasks (see recent relevant review Bellicoso et al. (2018)). Furthermore, legged robots can deliver payloads such as medkits in search-and-rescue scenarios to hard-to-reach locations, e.g., after standard transport routes have been compromised by a natural disaster. Impressive recent advances across different fields of robotics now bring us closer to future legged robots that more actively interact with new environments.

Stochastic gradient descent samples uniformly the training set to build an unbiased gradient estimate with a limited number of samples. However, at a given step of the training process, some data are more helpful than others to continue learning. Importance sampling for training deep neural networks has been widely studied to propose sampling schemes yielding better performance than the uniform sampling scheme. After recalling the theory of importance sampling for deep learning, this paper reviews the challenges inherent to this research area. In particular, we propose a metric allowing the assessment of the quality of a given sampling scheme; and we study the interplay between the sampling scheme and the optimizer used.

Distributed ledger systems have become more prominent and successful in recent years, with a focus on blockchains and cryptocurrency. This has led to various misunderstandings about both the technology itself and its capabilities, as in many cases blockchain and cryptocurrency is used synonymously and other applications are often overlooked. Therefore, as a whole, the view of distributed ledger technology beyond blockchains and cryptocurrencies is very limited. Existing vocabularies and ontologies often focus on single aspects of the technology, or in some cases even just on one product. This potentially leads to other types of distributed ledgers and their possible use cases being neglected. In this paper, we present a knowledge graph and an ontology for distributed ledger technologies, which includes security considerations to model aspects such as threats and vulnerabilities, application domains, as well as relevant standards and regulations. Such a knowledge graph improves the overall understanding of distributed ledgers, reveals their strengths, and supports the work of security personnel, i.e. analysts and system architects. We discuss potential uses and follow semantic web best practices to evaluate and publish the ontology and knowledge graph.

The Auto'Mate technologies were tested on several DT-25 target drones, and during almost six hours of flight testing, the four successively launched receivers were sequentially controlled and commanded without human interaction. These cutting-edge technologies demonstrate a significant breakthrough for future aerial operations involving manned and unmanned assets, and could reduce crew fatigue, minimize crew-training costs, and provide more effective operations. A second campaign is planned towards the end of 2023, which will explore the use of navigation sensors based on artificial intelligence and enhanced algorithms for autonomous formation flight. This groundbreaking achievement is a significant step towards autonomous formation flight and autonomous air-to-air refueling (A4R), and holds great potential for future aerial operations involving both manned and unmanned assets. "The success of this first flight-test campaign paves the way for developing autonomous and unmanned air-to-air refuelling technologies," said Jean Brice Dumont, Head of Military Air Systems at Airbus Defence and Space.

With the surging demand for generative AI, this week saw preparatory developments for the next wave of AI. Companies are fast-tracking the development of AI products, and generative AI tools are closer to becoming consumer products than ever before. They are already becoming powerful assistants for writers and programmers and rapidly taking on more challenges. The open-source community is also making significant progress in running local LLMs. For instance, Facebook's LLama model has continued to be a focal point for building in the academic and open source community following the leaked weights on 4Chan.

This paper explores a novel application of textual semantic similarity to user-preference representation for rating prediction. The approach represents a user's preferences as a graph of textual snippets from review text, where the edges are defined by semantic similarity. This textual, memory-based approach to rating prediction enables review-based explanations for recommendations. The method is evaluated quantitatively, highlighting that leveraging text in this way outperforms both strong memory-based and model-based collaborative filtering baselines.

Explainable AI (XAI) has established itself as an important component of AI-driven interactive systems. With Augmented Reality (AR) becoming more integrated in daily lives, the role of XAI also becomes essential in AR because end-users will frequently interact with intelligent services. However, it is unclear how to design effective XAI experiences for AR. We propose XAIR, a design framework that addresses "when", "what", and "how" to provide explanations of AI output in AR. The framework was based on a multi-disciplinary literature review of XAI and HCI research, a large-scale survey probing 500+ end-users' preferences for AR-based explanations, and three workshops with 12 experts collecting their insights about XAI design in AR. XAIR's utility and effectiveness was verified via a study with 10 designers and another study with 12 end-users. XAIR can provide guidelines for designers, inspiring them to identify new design opportunities and achieve effective XAI designs in AR.

Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do. To achieve this, AGI researchers draw inspiration from the human brain and seek to replicate its principles in intelligent machines. Brain-inspired artificial intelligence is a field that has emerged from this endeavor, combining insights from neuroscience, psychology, and computer science to develop more efficient and powerful AI systems. In this article, we provide a comprehensive overview of brain-inspired AI from the perspective of AGI. We begin with the current progress in brain-inspired AI and its extensive connection with AGI. We then cover the important characteristics for both human intelligence and AGI (e.g., scaling, multimodality, and reasoning). We discuss important technologies toward achieving AGI in current AI systems, such as in-context learning and prompt tuning. We also investigate the evolution of AGI systems from both algorithmic and infrastructural perspectives. Finally, we explore the limitations and future of AGI.

We present a novel approach for modeling vegetation response to weather in Europe as measured by the Sentinel 2 satellite. Existing satellite imagery forecasting approaches focus on photorealistic quality of the multispectral images, while derived vegetation dynamics have not yet received as much attention. We leverage both spatial and temporal context by extending state-of-the-art video prediction methods with weather guidance. We extend the EarthNet2021 dataset to be suitable for vegetation modeling by introducing a learned cloud mask and an appropriate evaluation scheme. Qualitative and quantitative experiments demonstrate superior performance of our approach over a wide variety of baseline methods, including leading approaches to satellite imagery forecasting. Additionally, we show how our modeled vegetation dynamics can be leveraged in a downstream task: inferring gross primary productivity for carbon monitoring. To the best of our knowledge, this work presents the first models for continental-scale vegetation modeling at fine resolution able to capture anomalies beyond the seasonal cycle, thereby paving the way for predictive assessments of vegetation status.

Emerging events, such as the COVID pandemic and the Ukraine Crisis, require a time-sensitive comprehensive understanding of the situation to allow for appropriate decision-making and effective action response. Automated generation of situation reports can significantly reduce the time, effort, and cost for domain experts when preparing their official human-curated reports. However, AI research toward this goal has been very limited, and no successful trials have yet been conducted to automate such report generation. We propose SmartBook, a novel task formulation targeting situation report generation, which consumes large volumes of news data to produce a structured situation report with multiple hypotheses (claims) summarized and grounded with rich links to factual evidence. We realize SmartBook for the Ukraine-Russia crisis by automatically generating intelligence analysis reports to assist expert analysts. The machine-generated reports are structured in the form of timelines, with each timeline organized by major events (or chapters), corresponding strategic questions (or sections) and their grounded summaries (or section content). Our proposed framework automatically detects real-time event-related strategic questions, which are more directed than manually-crafted analyst questions, which tend to be too complex, hard to parse, vague and high-level. Results from thorough qualitative evaluations show that roughly 82% of the questions in Smartbook have strategic importance, with at least 93% of the sections in the report being tactically useful. Further, experiments show that expert analysts tend to add more information into the SmartBook reports, with only 2.3% of the existing tokens being deleted, meaning SmartBook can serve as a useful foundation for analysts to build upon when creating intelligence reports.