Artificial intelligence can spot parasitic worm eggs in human faecal samples – including those from parasite species missed when lab technologists use a microscope to study the same samples. The discovery suggests AI could help us better diagnose and treat parasitic worm infections across the globe. The World Health Organization estimates that almost one-quarter of the world's population – or 1.5 billion people – are infected by parasitic worms living in their intestinal systems. The infections can lead to malnutrition, anaemia or stunted cognitive development. But diagnosis and treatment is often inaccessible because there are a limited number of experts trained to spot the infections.

Corals often provide a colorful backdrop to photographs of shimmering fish captured by amateur divers. Corals – marine invertebrates with calcium-carbonate exoskeletons – are some of the most diverse ecosystems on Earth: despite covering less than 0.1% of the ocean's surface, they provide shelter and habitats for almost one-third of known marine species. Their impact also extends to human populations in many countries around the world. According to research by the U.S. National Oceanic and Atmospheric Administration, up to half a billion people worldwide rely on coral reefs for food security and tourist income. But the world's corals are under threat from rising sea temperatures and local anthropogenic pollution, which causes them to bleach and die.

A year into Sudan's civil war, Iranian-made armed drones have helped the army turn the tide of the conflict, halting the progress of the rival paramilitary Rapid Support Force and regaining territory around the capital, a senior army source has said. Six Iranian sources, regional officials and diplomats -- who, like the army source, asked not to be identified because of the sensitivity of the information -- also said the military had acquired Iranian-made unmanned aerial vehicles (UAVs) over the past few months. The Sudanese Armed Forces (SAF) used some older UAVs in the first months of the war alongside artillery batteries and fighter jets, but had little success in rooting out RSF fighters embedded in heavily populated neighborhoods in Khartoum and other cities, more than a dozen Khartoum residents said.

There are a number of hypotheses underlying the existence of adversarial examples for classification problems. These include the high-dimensionality of the data, high codimension in the ambient space of the data manifolds of interest, and that the structure of machine learning models may encourage classifiers to develop decision boundaries close to data points. This article proposes a new framework for studying adversarial examples that does not depend directly on the distance to the decision boundary. Similarly to the smoothed classifier literature, we define a (natural or adversarial) data point to be $(\gamma,\sigma)$-stable if the probability of the same classification is at least $\gamma$ for points sampled in a Gaussian neighborhood of the point with a given standard deviation $\sigma$. We focus on studying the differences between persistence metrics along interpolants of natural and adversarial points. We show that adversarial examples have significantly lower persistence than natural examples for large neural networks in the context of the MNIST and ImageNet datasets. We connect this lack of persistence with decision boundary geometry by measuring angles of interpolants with respect to decision boundaries. Finally, we connect this approach with robustness by developing a manifold alignment gradient metric and demonstrating the increase in robustness that can be achieved when training with the addition of this metric.

Auditing of AI systems is a promising way to understand and manage ethical problems and societal risks associated with contemporary AI systems, as well as some anticipated future risks. Efforts to develop standards for auditing Artificial Intelligence (AI) systems have therefore understandably gained momentum. However, we argue that creating auditing standards is not just insufficient, but actively harmful by proliferating unheeded and inconsistent standards, especially in light of the rapid evolution and ethical and safety challenges of AI. Instead, the paper proposes the establishment of an AI Audit Standards Board, responsible for developing and updating auditing methods and standards in line with the evolving nature of AI technologies. Such a body would ensure that auditing practices remain relevant, robust, and responsive to the rapid advancements in AI. The paper argues that such a governance structure would also be helpful for maintaining public trust in AI and for promoting a culture of safety and ethical responsibility within the AI industry. Throughout the paper, we draw parallels with other industries, including safety-critical industries like aviation and nuclear energy, as well as more prosaic ones such as financial accounting and pharmaceuticals. AI auditing should emulate those fields, and extend beyond technical assessments to include ethical considerations and stakeholder engagement, but we explain that this is not enough; emulating other fields' governance mechanisms for these processes, and for audit standards creation, is a necessity. We also emphasize the importance of auditing the entire development process of AI systems, not just the final products...

The Mayan languages comprise a language family with an ancient history, millions of speakers, and immense cultural value, that, nevertheless, remains severely underrepresented in terms of resources and global exposure. In this paper we develop, curate, and publicly release a set of corpora in several Mayan languages spoken in Guatemala and Southern Mexico, which we call MayanV. The datasets are parallel with Spanish, the dominant language of the region, and are taken from official native sources focused on representing informal, day-to-day, and non-domain-specific language. As such, and according to our dialectometric analysis, they differ in register from most other available resources. Additionally, we present neural machine translation models, trained on as many resources and Mayan languages as possible, and evaluated exclusively on our datasets. We observe lexical divergences between the dialects of Spanish in our resources and the more widespread written standard of Spanish, and that resources other than the ones we present do not seem to improve translation performance, indicating that many such resources may not accurately capture common, real-life language usage. The MayanV dataset is available at https://github.com/transducens/mayanv.

Code generation models are not robust to small perturbations, which often lead to inconsistent and incorrect generations and significantly degrade the performance of these models. Improving the robustness of code generation models is crucial to better user experience when these models are deployed in real-world applications. However, existing efforts have not addressed this issue for code generation models. To fill this gap, we propose CodeFort, a framework to improve the robustness of code generation models, generalizing a large variety of code perturbations to enrich the training data and enabling various robust training strategies, mixing data augmentation, batch augmentation, adversarial logits pairing, and contrastive learning, all carefully designed to support high-throughput training. Extensive evaluations show that we improve the average robust pass rates of baseline CodeGen models from 14.79 to 21.74. Notably, the improvement in robustness against code-syntax perturbations is evidenced by a significant decrease in pass rate drop from 95.04% to 53.35%

Scientific Research, vital for improving human life, is hindered by its inherent complexity, slow pace, and the need for specialized experts. To enhance its productivity, we propose a ResearchAgent, a large language model-powered research idea writing agent, which automatically generates problems, methods, and experiment designs while iteratively refining them based on scientific literature. Specifically, starting with a core paper as the primary focus to generate ideas, our ResearchAgent is augmented not only with relevant publications through connecting information over an academic graph but also entities retrieved from an entity-centric knowledge store based on their underlying concepts, mined and shared across numerous papers. In addition, mirroring the human approach to iteratively improving ideas with peer discussions, we leverage multiple ReviewingAgents that provide reviews and feedback iteratively. Further, they are instantiated with human preference-aligned large language models whose criteria for evaluation are derived from actual human judgments. We experimentally validate our ResearchAgent on scientific publications across multiple disciplines, showcasing its effectiveness in generating novel, clear, and valid research ideas based on human and model-based evaluation results.

Large language models (LLMs) have demonstrated impressive capability in reasoning and planning when integrated with tree-search-based prompting methods. However, since these methods ignore the previous search experiences, they often make the same mistakes in the search process. To address this issue, we introduce Reflection on search Trees (RoT), an LLM reflection framework designed to improve the performance of tree-search-based prompting methods. It uses a strong LLM to summarize guidelines from previous tree search experiences to enhance the ability of a weak LLM. The guidelines are instructions about solving this task through tree search which can prevent the weak LLMs from making similar mistakes in the past search process. In addition, we proposed a novel state selection method, which identifies the critical information from historical search processes to help RoT generate more specific and meaningful guidelines. In our extensive experiments, we find that RoT significantly improves the performance of LLMs in reasoning or planning tasks with various tree-search-based prompting methods (e.g., BFS and MCTS). Non-tree-search-based prompting methods such as Chain-of-Thought (CoT) can also benefit from RoT guidelines since RoT can provide task-specific knowledge collected from the search experience.

Although the existing uncertainty-based semi-supervised medical segmentation methods have achieved excellent performance, they usually only consider a single uncertainty evaluation, which often fails to solve the problem related to credibility completely. Therefore, based on the framework of evidential deep learning, this paper integrates the evidential predictive results in the cross-region of mixed and original samples to reallocate the confidence degree and uncertainty measure of each voxel, which is realized by emphasizing uncertain information of probability assignments fusion rule of traditional evidence theory. Furthermore, we design a voxel-level asymptotic learning strategy by introducing information entropy to combine with the fused uncertainty measure to estimate voxel prediction more precisely. The model will gradually pay attention to the prediction results with high uncertainty in the learning process, to learn the features that are difficult to master. The experimental results on LA, Pancreas-CT, ACDC and TBAD datasets demonstrate the superior performance of our proposed method in comparison with the existing state of the arts.