The joint lawsuit accuses the company of "capitalizing and profiting" from the unauthorized use of their content for ChatGPT. The legal action was filed in the Ontario Superior Court of Justice. The plaintiffs include CBC/Radio-Canada, Postmedia, Metroland, the Toronto Star, the Globe and Mail and The Canadian Press. They're seeking punitive damages from OpenAI, payments for any profits the ChatGPT creator made from using their news articles and a ban on further use of their content. "OpenAI is capitalizing and profiting from the use of this content, without getting permission or compensating content owners."

This paper introduces a competency-based model for generative artificial intelligence (AI) literacy covering essential skills and knowledge areas necessary to interact with generative AI. The competencies range from foundational AI literacy to prompt engineering and programming skills, including ethical and legal considerations. These twelve competencies offer a framework for individuals, policymakers, government officials, and educators looking to navigate and take advantage of the potential of generative AI responsibly. Embedding these competencies into educational programs and professional training initiatives can equip individuals to become responsible and informed users and creators of generative AI. The competencies follow a logical progression and serve as a roadmap for individuals seeking to get familiar with generative AI and for researchers and policymakers to develop assessments, educational programs, guidelines, and regulations.

We survey clinical document corpora, with focus on German textual data. Due to rigid data privacy legislation in Germany these resources, with only few exceptions, are stored in safe clinical data spaces and locked against clinic-external researchers. This situation stands in stark contrast with established workflows in the field of natural language processing where easy accessibility and reuse of data collections are common practice. Hence, alternative corpus designs have been examined to escape from this data poverty. Besides machine translation of English clinical datasets and the generation of synthetic corpora with fictitious clinical contents, several other types of domain proxies have come up as substitutes for authentic clinical documents. Common instances of close proxies are medical journal publications, clinical therapy guidelines, drug labels, etc., more distant proxies include online encyclopedic medical articles or medical contents from social media channels. After PRISM-conformant screening of 359 hits from four bibliographic systems, 75 relevant documents were finally selected for this review and 59 distinct corpora were determined. We identified 24 real clinical corpora (from 40 publications) out of which only 5 are publicly distributable. 2 translations of real corpora and 3 synthetic ones complement the set of clinical corpora. 14 corpora were categorized as close domain proxies, 16 as distant ones. There is a clear divide between the large number of non-accessible authentic clinical German-language corpora and their publicly accessible substitutes: translated or synthetic, close or more distant proxies. So on first sight, the data bottleneck seems broken. Intuitively yet, differences in genre-specific writing style, wording and medical domain expertise in this typological space are also obvious. This raises the question how valid alternative corpus designs really are.

Safety concerns of Multimodal large language models (MLLMs) have gradually become an important problem in various applications. Surprisingly, previous works indicate a counter-intuitive phenomenon that using textual unlearning to align MLLMs achieves comparable safety performances with MLLMs trained with image-text pairs. To explain such a counter-intuitive phenomenon, we discover a visual safety information leakage (VSIL) problem in existing multimodal safety benchmarks, i.e., the potentially risky and sensitive content in the image has been revealed in the textual query. In this way, MLLMs can easily refuse these sensitive text-image queries according to textual queries. However, image-text pairs without VSIL are common in real-world scenarios and are overlooked by existing multimodal safety benchmarks. To this end, we construct multimodal visual leakless safety benchmark (VLSBench) preventing visual safety leakage from image to textual query with 2.4k image-text pairs. Experimental results indicate that VLSBench poses a significant challenge to both open-source and close-source MLLMs, including LLaVA, Qwen2-VL, Llama3.2-Vision, and GPT-4o. This study demonstrates that textual alignment is enough for multimodal safety scenarios with VSIL, while multimodal alignment is a more promising solution for multimodal safety scenarios without VSIL. Please see our code and data at: http://hxhcreate.github.io/VLSBench

This paper presents a new ontology that implements the well-known Deontic Traditional Scheme in RDFs and SPARQL, fit to handle irresolvable conflicts, i.e., situations in which two or more statements prescribe conflicting obligations, prohibitions, or permissions, with none of them being "stronger" than the other one(s). In our view, this paper marks a significant advancement in standard theoretical research in formal Deontic Logic. Most contemporary approaches in this field are confined to the propositional level, mainly focus on the notion of obligation, and lack implementations. The proposed framework is encoded in RDF, which is not only a first-order language but also the most widely used knowledge representation language, as it forms the foundation of the Semantic Web. Moreover, the proposed computational ontology formalizes all deontic modalities defined in the Deontic Traditional Scheme, without specifically focusing on obligations, and offers constructs to model and reason with various types of irresolvable conflicts, violations, and the interaction between deontic modalities and contextual constraints in a given state of affairs. To the best of our knowledge, no existing approach in the literature addresses all these aspects within a unified integrated framework. All examples presented and discussed in this paper, together with Java code and clear instructions to re-execute them locally, are available at https://github.com/liviorobaldo/conflict-tolerantDeonticTraditionalScheme

Under stringent privacy constraints, whether federated recommendation systems can achieve group fairness remains an inadequately explored question. Taking gender fairness as a representative issue, we identify three phenomena in federated recommendation systems: performance difference, data imbalance, and preference disparity. We discover that the state-of-the-art methods only focus on the first phenomenon. Consequently, their imposition of inappropriate fairness constraints detrimentally affects the model training. Moreover, due to insufficient sensitive attribute protection of existing works, we can infer the gender of all users with 99.90% accuracy even with the addition of maximal noise. In this work, we propose Privacy-Preserving Orthogonal Aggregation (PPOA), which employs the secure aggregation scheme and quantization technique, to prevent the suppression of minority groups by the majority and preserve the distinct preferences for better group fairness. PPOA can assist different groups in obtaining their respective model aggregation results through a designed orthogonal mapping while keeping their attributes private. Experimental results on three real-world datasets demonstrate that PPOA enhances recommendation effectiveness for both females and males by up to 8.25% and 6.36%, respectively, with a maximum overall improvement of 7.30%, and achieves optimal fairness in most cases. Extensive ablation experiments and visualizations indicate that PPOA successfully maintains preferences for different gender groups.

This thesis is part of a CIFRE agreement between the company Othello and the LIASD laboratory. The objective is to develop an artificial intelligence system that can detect real-time dangers in a video stream. To achieve this, a novel approach combining temporal and spatial analysis has been proposed. Several avenues have been explored to improve anomaly detection by integrating object detection, human pose detection, and motion analysis. For result interpretability, techniques commonly used for image analysis, such as activation and saliency maps, have been extended to videos, and an original method has been proposed. The proposed architecture performs binary or multiclass classification depending on whether an alert or the cause needs to be identified. Numerous neural networkmodels have been tested, and three of them have been selected. You Only Looks Once (YOLO) has been used for spatial analysis, a Convolutional Recurrent Neuronal Network (CRNN) composed of VGG19 and a Gated Recurrent Unit (GRU) for temporal analysis, and a multi-layer perceptron for classification. These models handle different types of data and can be combined in parallel or in series. Although the parallel mode is faster, the serial mode is generally more reliable. For training these models, supervised learning was chosen, and two proprietary datasets were created. The first dataset focuses on objects that may play a potential role in anomalies, while the second consists of videos containing anomalies or non-anomalies. This approach allows for the processing of both continuous video streams and finite videos, providing greater flexibility in detection.

Businesses should be using more robots instead of hiring low-paid migrants, the shadow home secretary has said. The Conservative MP Chris Philp says other countries "use a lot more automation" for tasks such as picking fruit and vegetables "rather than simply importing a lot of low-wage migrant labour". Speaking on BBC Breakfast, he called for more investment in technology to reduce the UK's net migration figures. Philp said: "To give an example, in Australia and New Zealand, they are rolling out robotic and automated fruit- and vegetable-picking equipment, in South Korea they use nine times the number of robots in manufacturing processes compared to us, in America they use a lot more modular construction which is much faster and much more efficient. "There's a lot of things British industry can do to grow without needing to import large numbers of low-wage migrants." At an impromptu press conference on Wednesday, Kemi Badenoch, the Conservative leader, said her party had got it wrong on immigration. She promised a review of "every policy, treaty and part of our legal framework" including the role of the European convention on human rights (ECHR) and the Human Rights Act. Get the day's headlines and highlights emailed direct to you every morning She said her party still believed in a "deterrent" to irregular migration but did not commit to restoring the Rwanda scheme scrapped by Labour, even though Philp called for it to be reinstated two weeks ago. He said on Thursday that Labour had "cancelled the Rwanda scheme before it even started". Philp was asked about reports that under the Conservatives, ministers had been examining using a giant wave machine to deter Channel crossings. He told the BBC: "I don't recall ever having seriously looked at that idea.

Third of NI adults visit porn sites, Ofcom finds Getty ImagesA new Ofcom report finds over 430,000 adults in Northern Ireland visited "pornographic content services" online in May 2024 Adults in Northern Ireland are more likely to look at pornography online than those in any other part of the UK. That is according to new research published by the communications regulator Ofcom. It said that more than 430,000 adults in Northern Ireland visited "pornographic content services" online in May 2024 - more than one third of the adult population. That was higher than the proportion of adults viewing similar content in Wales, Scotland and England. The figures come from Ofcom's Online Nation report for 2024, which looks into the UK's digital habits.

New Delhi/Kolkata, India – A pair of black Dandy Pik Pik loafers covered in sharp, uneven spikes and shiny studs was part of the evidence before Judge Pratibha M Singh in an intellectual-property lawsuit brought by French luxury shoe brand Christian Louboutin against an Indian shoe manufacturer in a Delhi high court last year. Louboutin's lawyers had already regaled the court with anecdotes about the iconic status of their shoes. The signature stilettos, with their luxuriant red soles, had starred in movies like The Devil Wears Prada and Sex and The City, and were registered as a trademark in India and other countries, they said. Riding on the brand's reputation, the lawyers were now trying to make the point that spiked shoes, too, were unique to Christian Louboutin, and the defendant, Shutiq – The Shoe Boutique, was manufacturing and selling their designs in India illegally. Incriminating evidence presented to Judge Singh included testimony from ChatGPT, saying that Christian Louboutin is known for spiked men's shoes. Then there were photographs of Shutiq's 26 spiked and bedazzled shoes next to Louboutin originals, including Dandy Pik Pik.