Although text-to-image generation is rapidly advancing, these AI models are mostly English-centric. Researchers at the University of Amsterdam Faculty of Science have created NeoBabel, an AI image generator that can work in six different languages. By making all elements of their research open source, anyone can build on the model and help push inclusive AI research. When you generate an image with AI, the results are often better when your prompt is in English. This is because many AI models are English at their core: if you use another language, your prompt is translated into English before the image is created.

Towardsthis end,wedesigned apreconditioned gradient solverforkernelmethods exploiting both GPU acceleration and parallelization with multiple GPUs, implementing out-of-core variants of common linear algebra operations to guarantee optimal hardware utilization.

Altman's campaigning for his company coincides with its use of enormous present resources to serve an imagined future OpenAI CEO Sam Altman poses during the Artificial Intelligence (AI) Action Summit, at the Grand Palais, in Paris, on February 11, 2025. Sam Altman has claimed over the years that the advancement of AI could solve climate change, cure cancer, create a benevolent superintelligence beyond human comprehension, provide a tutor for every student, take over nearly half of the tasks in the economy and create what he calls "universal extreme wealth". In order to bring about his utopian future, Altman is demanding enormous resources from the present. As CEO of OpenAI, the world's most valuable privately owned company, he has in recent months announced plans for $1tn of investment into datacenters and struck multibillion-dollar deals with several chipmakers. If completed, the datacenters are expected to use more power than entire European nations .

Abstract--The increasing need for robustness, reliability, and determinism in wireless networks for industrial and mission-critical applications is the driver for the growth of new innovative methods. The study presented in this work makes use of machine learning techniques to predict channel quality in a Wi-Fi network in terms of the frame delivery ratio. Predictions can be used proactively to adjust communication parameters at runtime and optimize network operations for industrial applications. Methods including convolutional neural networks and long short-term memory were analyzed on datasets acquired from a real Wi-Fi setup across multiple channels. The models were compared in terms of prediction accuracy and computational complexity. Results show that the frame delivery ratio can be reliably predicted, and convolutional neural networks, although slightly less effective than other models, are more efficient in terms of CPU usage and memory consumption. This enhances the model's usability on embedded and industrial systems. Robustness and dependability are the main challenges in next-generation communication systems, especially in wireless networks for industrial applications like Wi-Fi [1], but also in the context of smart cities and buildings, transportation, and agriculture.

Relevant to this paper are examinations of the computational power of neural networks after training, i.e., the training process is not taken into account but instead the computational power of an optimally trained network is studied. Starting already in the nineties, the expressive power of feed-forward neural networks (FNNs) has been related to Boolean threshold circuits, see, e.g., [Maass et al., 1991, Siegelmann and Sontag, 1995,

RoboCup is an international scientific initiative with the goal of advancing the state of the art of intelligent robots, AI and automation. The annual RoboCup event, where teams gather from across the globe to take part in competitions across a number of leagues, this year took place in Salvador, Brazil from 15-21 July. In a series of interviews, we've been meeting some of the RoboCup trustees, committee members, and participants, to find out more about their respective leagues. Christoph Steup is an Executive Committee member and oversees the @Work League. Ahead of the event in Brazil, we spoke to Christoph to find out more about the @Work League, the tasks that teams need to complete, and future plans for the League.

Traditional Federated Learning (FL) faces significant challenges in terms of efficiency and accuracy, particularly in heterogeneous environments where clients employ diverse model architectures and have varying computational resources. Such heterogeneity complicates the aggregation process, leading to performance bottlenecks and reduced model generalizability. To address these issues, we propose FedADP, a federated learning framework designed to adapt to client heterogeneity by dynamically adjusting model architectures during aggregation. FedADP enables effective collaboration among clients with differing capabilities, maximizing resource utilization and ensuring model quality. Our experimental results demonstrate that FedADP significantly outperforms existing methods, such as FlexiFed, achieving an accuracy improvement of up to 23.30%, thereby enhancing model adaptability and training efficiency in heterogeneous real-world settings.

Deep learning, when integrated with a large amount of training data, has the potential to outperform machine learning in terms of high accuracy. Recently, privacy-preserving deep learning has drawn significant attention of the research community. Different privacy notions in deep learning include privacy of data provided by data-owners and privacy of parameters and/or hyperparameters of the underlying neural network. Federated learning is a popular privacy-preserving execution environment where data-owners participate in learning the parameters collectively without leaking their respective data to other participants. However, federated learning suffers from certain security/privacy issues. In this paper, we propose Split-n-Chain, a variant of split learning where the layers of the network are split among several distributed nodes. Split-n-Chain achieves several privacy properties: data-owners need not share their training data with other nodes, and no nodes have access to the parameters and hyperparameters of the neural network (except that of the respective layers they hold). Moreover, Split-n-Chain uses blockchain to audit the computation done by different nodes. Our experimental results show that: Split-n-Chain is efficient, in terms of time required to execute different phases, and the training loss trend is similar to that for the same neural network when implemented in a monolithic fashion.