The grand challenge facing global agriculture today is the need to increase food production to feed a rapidly growing population, amid diminishing natural and human resources and climate pressures. With the global population expected to exceed 9.5 billion by 2050, and with several key resources being depleted (see sidebar), the agricultural community is turning to a digital revolution to secure the future of our food production. Touted Agriculture 4.0, this new movement is deploying digital technologies at scale, including field and aerial sensing, automation, and other smart devices to monitor and track resources and to improve operational efficiency. Artificial intelligence (AI) technologies are playing a central role in driving this revolution: enabling real-time decision support using spatiotemporal data collected on farms, augmenting human labor with automated decision making and robotics, estimating and forecasting risks due to extreme weather, and aiding in longer-term planning under climate-imposed uncertainties. To propel the development and deployment of AI tools and technologies for U.S. agriculture, since 2020 the U.S. Department of Agriculture's National Institute of Food and Agriculture (USDA NIFA) has made a strategic investment in five AI institutes.

Explainable AI (XAI) aims to provide insight into opaque model reasoning to humans and as such is an interdisciplinary field by nature. In this paper, we interviewed 10 practitioners to understand the possible usability of training data attribution (TDA) explanations and to explore the design space of such an approach. We confirmed that training data quality is often the most important factor for high model performance in practice and model developers mainly rely on their own experience to curate data. End-users expect explanations to enhance their interaction with the model and do not necessarily prioritise but are open to training data as a means of explanation. Within our participants, we found that TDA explanations are not well-known and therefore not used. We urge the community to focus on the utility of TDA techniques from the human-machine collaboration perspective and broaden the TDA evaluation to reflect common use cases in practice.

If you look at the UN Sustainable Development Goals, it's clear that robots have a huge role to play in advancing the SDGs. However the field of Sustainable Robotics is more than just the application area. For every application that robotics can improve in sustainability, you have to also address the question – what are the additional costs or benefits all the way along the supply chain. What are the'externalities', or additional costs/benefits, of using robots to solve the problem. Solving our economic and environmental global challenges should not involve adding to the existing problems or creating new ones.

The rise of AI conversational agents has broadened opportunities to enhance human capabilities across various domains. As these agents become more prevalent, it is crucial to investigate the impact of different affective abilities on their performance and user experience. In this study, we surveyed 745 respondents to understand the expectations and preferences regarding affective skills in various applications. Specifically, we assessed preferences concerning AI agents that can perceive, respond to, and simulate emotions across 32 distinct scenarios. Our results indicate a preference for scenarios that involve human interaction, emotional support, and creative tasks, with influences from factors such as emotional reappraisal and personality traits. Overall, the desired affective skills in AI agents depend largely on the application's context and nature, emphasizing the need for adaptability and context-awareness in the design of affective AI conversational agents.

Generative artificial intelligence (AI) is a widely popular technology that will have a profound impact on society and individuals. Less than a decade ago, it was thought that creative work would be among the last to be automated - yet today, we see AI encroaching on many creative domains. In this paper, we present the findings of a survey study on people's perceptions of text-to-image generation. We touch on participants' technical understanding of the emerging technology, their fears and concerns, and thoughts about risks and dangers of text-to-image generation to the individual and society. We find that while participants were aware of the risks and dangers associated with the technology, only few participants considered the technology to be a personal risk. The risks for others were more easy to recognize for participants. Artists were particularly seen at risk. Interestingly, participants who had tried the technology rated its future importance lower than those who had not tried it. This result shows that many people are still oblivious of the potential personal risks of generative artificial intelligence and the impending societal changes associated with this technology.

Despite recent efforts by the Artificial Intelligence (AI) community to move towards standardised procedures for documenting models, methods, systems or datasets, there is currently no methodology focused on use cases aligned with the risk-based approach of the European AI Act (AI Act). In this paper, we propose a new framework for the documentation of use cases, that we call "use case cards", based on the use case modelling included in the Unified Markup Language (UML) standard. Unlike other documentation methodologies, we focus on the intended purpose and operational use of an AI system. It consists of two main parts. Firstly, a UML-based template, tailored to allow implicitly assessing the risk level of the AI system and defining relevant requirements. Secondly, a supporting UML diagram designed to provide information about the system-user interactions and relationships. The proposed framework is the result of a co-design process involving a relevant team of EU policy experts and scientists. We have validated our proposal with 11 experts with different backgrounds and a reasonable knowledge of the AI Act as a prerequisite. We provide the 5 "use case cards" used in the co-design and validation process. "Use case cards" allows framing and contextualising use cases in an effective way, and we hope this methodology can be a useful tool for policy makers and providers for documenting use cases, assessing the risk level, adapting the different requirements and building a catalogue of existing usages of AI.

The verification and validation of autonomous driving vehicles remains a major challenge due to the high complexity of autonomous driving functions. Scenario-based testing is a promising method for validating such a complex system. Ontologies can be utilized to produce test scenarios that are both meaningful and relevant. One crucial aspect of this process is selecting the appropriate method for describing the entities involved. The level of detail and specific entity classes required will vary depending on the system being tested. It is important to choose an ontology that properly reflects these needs. This paper summarizes key representative ontologies for scenario-based testing and related use cases in the field of autonomous driving. The considered ontologies are classified according to their level of detail for both static facts and dynamic aspects. Furthermore, the ontologies are evaluated based on the presence of important entity classes and the relations between them.

Deutsche Bank is looking to deploy artificial intelligence (AI) acceleration technology from Nvidia to power financial services applications. The bank hopes AI will improve its efforts to serve customers worldwide and enable it to build new data-driven products and services, increase efficiency and recruit tech talent. Using Nvidia AI Enterprise software, Deutsche Bank said its AI developers, data scientists and IT professionals would be able to build and run AI workflows in hosted on-premise datacentres as well as on Google Cloud, which the bank uses as its public cloud provider. The bank plans to use the latest version of Nvidia's enterprise AI tool – AI Enterprise 3.0. This introduces workflows for contact centre intelligent virtual assistants, audio transcription and digital fingerprinting for cyber security.