Deep learning based diagnostic AI systems based on medical images are starting to provide similar performance as human experts. However these data hungry complex systems are inherently black boxes and therefore slow to be adopted for high risk applications like healthcare. This problem of lack of transparency is exacerbated in the case of recent large foundation models, which are trained in a self supervised manner on millions of data points to provide robust generalisation across a range of downstream tasks, but the embeddings generated from them happen through a process that is not interpretable, and hence not easily trustable for clinical applications. To address this timely issue, we deploy conformal analysis to quantify the predictive uncertainty of a vision transformer (ViT) based foundation model across patient demographics with respect to sex, age and ethnicity for the tasks of skin lesion classification using several public benchmark datasets. The significant advantage of this method is that conformal analysis is method independent and it not only provides a coverage guarantee at population level but also provides an uncertainty score for each individual. We used a model-agnostic dynamic F1-score-based sampling during model training, which helped to stabilize the class imbalance and we investigate the effects on uncertainty quantification (UQ) with or without this bias mitigation step. Thus we show how this can be used as a fairness metric to evaluate the robustness of the feature embeddings of the foundation model (Google DermFoundation) and thus advance the trustworthiness and fairness of clinical AI. Keywords: algorithmic fairness vision transformer (ViT) foundation models skin lesion classification conformal prediction uncertainty quantification transparent trustworthy AI class imbalance 1 Introduction Skin cancer remains a significant global health concern, with melanoma accounting for more than 5% of the total cancer cases diagnosed in the US and causing arXiv:2503.23819v1

Low-Resource Languages (LRLs) present significant challenges in natural language processing due to their limited linguistic resources and underrepresentation in standard datasets. While recent advancements in Large Language Models (LLMs) and Neural Machine Translation (NMT) have substantially improved translation capabilities for high-resource languages, performance disparities persist for LRLs, particularly impacting privacy-sensitive and resource-constrained scenarios. This paper systematically evaluates the limitations of current LLMs across 200 languages using benchmarks such as FLORES-200. We also explore alternative data sources, including news articles and bilingual dictionaries, and demonstrate how knowledge distillation from large pre-trained models can significantly improve smaller LRL translations. Additionally, we investigate various fine-tuning strategies, revealing that incremental enhancements markedly reduce performance gaps on smaller LLMs.

Instruction tuning has emerged as a paramount method for tailoring the behaviors of LLMs. Recent work has unveiled the potential for LLMs to achieve high performance through fine-tuning with a limited quantity of high-quality instruction data. Building upon this approach, we further explore the impact of prompt's robustness on the selection of high-quality instruction data. This paper proposes a pioneering framework of high-quality online instruction data mining for instruction tuning, focusing on the impact of prompt's robustness on the data mining process. Our notable innovation, is to generate the adversarial instruction data by conducting the attack for the prompt of online instruction data. Then, we introduce an Adversarial Instruction-Following Difficulty metric to measure how much help the adversarial instruction data can provide to the generation of the corresponding response. Apart from it, we propose a novel Adversarial Instruction Output Embedding Consistency approach to select high-quality online instruction data. We conduct extensive experiments on two benchmark datasets to assess the performance. The experimental results serve to underscore the effectiveness of our proposed two methods. Moreover, the results underscore the critical practical significance of considering prompt's robustness.

Deep neural networks learn structured features from complex, non-Gaussian inputs, but the mechanisms behind this process remain poorly understood. Our work is motivated by the observation that the first-layer filters learnt by deep convolutional neural networks from natural images resemble those learnt by independent component analysis (ICA), a simple unsupervised method that seeks the most non-Gaussian projections of its inputs. This similarity suggests that ICA provides a simple, yet principled model for studying feature learning. Here, we leverage this connection to investigate the interplay between data structure and optimisation in feature learning for the most popular ICA algorithm, FastICA, and stochastic gradient descent (SGD), which is used to train deep networks. We rigorously establish that FastICA requires at least $n\gtrsim d^4$ samples to recover a single non-Gaussian direction from $d$-dimensional inputs on a simple synthetic data model. We show that vanilla online SGD outperforms FastICA, and prove that the optimal sample complexity $n \gtrsim d^2$ can be reached by smoothing the loss, albeit in a data-dependent way. We finally demonstrate the existence of a search phase for FastICA on ImageNet, and discuss how the strong non-Gaussianity of said images compensates for the poor sample complexity of FastICA.

Novelty modeling and detection is a core topic in Natural Language Processing (NLP), central to numerous tasks such as recommender systems and automatic summarization. It involves identifying pieces of text that deviate in some way from previously known information. However, novelty is also a crucial determinant of the unique perception of relevance and quality of an experience, as it rests upon each individual's understanding of the world. Social factors, particularly cultural background, profoundly influence perceptions of novelty and innovation. Cultural novelty arises from differences in salience and novelty as shaped by the distance between distinct communities. While cultural diversity has garnered increasing attention in artificial intelligence (AI), the lack of robust metrics for quantifying cultural novelty hinders a deeper understanding of these divergences. This gap limits quantifying and understanding cultural differences within computational frameworks. To address this, we propose an interdisciplinary framework that integrates knowledge from sociology and management. Central to our approach is GlobalFusion, a novel dataset comprising 500 dishes and approximately 100,000 cooking recipes capturing cultural adaptation from over 150 countries. By introducing a set of Jensen-Shannon Divergence metrics for novelty, we leverage this dataset to analyze textual divergences when recipes from one community are modified by another with a different cultural background. The results reveal significant correlations between our cultural novelty metrics and established cultural measures based on linguistic, religious, and geographical distances. Our findings highlight the potential of our framework to advance the understanding and measurement of cultural diversity in AI.

Muslims around the world have begun celebrating Eid al-Fitr, one of the biggest celebrations in the Islamic calendar. Eid al-Fitr - which means "festival of the breaking of the fast" - is celebrated at the end of Ramadan, a month of fasting for many adults, as well as spiritual reflection and prayer.ReutersHere in Moscow, worshippers are seen preparing for prayer.ReutersHundreds took part in prayers at Tononoka grounds, in Mombasa, KenyaGetty ImagesPrayers were also observed at a stadium in Port Sudan in the east of the countryGetty ImagesLittle children joined adults at the Moskee Essalam in Rotterdam, NetherlandsGetty ImagesGifts are handed out to Muslim children in Lviv, Ukraine, as Russia's war on the country continuesReuters Palestinians in Jabaliya in the northern Gaza Strip pray amidst the rubble of a mosque destroyed in the current war between Israel and HamasGetty ImagesFamilies gather at al-Aqsa mosque in Jerusalem - the third holiest site in IslamReutersA boy yawns during prayers at a stadium in QatarEPAMuslims greet each-other at Martim Moniz Square in Lisbon, PortugalGetty ImagesWomen worshippers gather in Burgess Park, London, for an outdoor prayerEPAThere were also worshippers gathered outside Plebiscito Square in Naples, ItalyReutersSome women took pictures after attending prayers at the Hagia Sophia Grand Mosque in Istanbul, TurkeyGetty ImagesAfghan refugees pray at a mosque on the outskirts of Peshawar, PakistanMiddle EastEuropeEid al-FitrReligionIslamRelated'I was afraid for my life': At the scene of the attack on Palestinian Oscar winner 5 days agoMiddle EastMore8 hrs ago'In Bradford, families spend thousands on new clothes for Eid' Muslims spend large amounts in Bradford's supermarkets, clothes shops and other services before Eid.8 hrs agoEngland1 day ago The tourist has received an award from the city's mayor after restraining a man during a stabbing.1 day agoEurope1 day ago Another 21 people are injured, as a restaurant and several buildings are set ablaze in the city, local officials say.1 day agoWorld1 day ago Town's successful Ramadan lights project expanded A Scunthorpe community group says it has seen an "amazing" response to its lights display.1 day agoLincolnshire1 day ago Bishop says school that changed Easter events'valued' The BBC is not responsible for the content of external sites.

Since the release of ChatGPT in late 2022, generative artificial intelligence has trickled down from adults in their offices to university students in campus libraries to teenagers in high school hallways. Now it's reaching the youngest among us, and parents and teachers are grappling with the most responsible way to introduce their under-13s to a new technology that may fundamentally reshape the future. Though the terms of service for ChatGPT, Google's Gemini and other AI models specify that the tools are only meant for those over 13, parents and teachers are taking the matter of AI education into their own hands. Inspired by a story we published on parents who are teaching their children to use AI to set them up for success in school and at work, we asked Guardian readers how and why – or why not – others are doing the same. Though our original story only concerned parents, we have also included teachers in the responses published below, as preparing children for future studies and jobs is one of educators' responsibilities as well.

We evaluated 4 systems (ELIZA, GPT-4o, LLaMa-3.1-405B, and GPT-4.5) in two randomised, controlled, and pre-registered Turing tests on independent populations. Participants had 5 minute conversations simultaneously with another human participant and one of these systems before judging which conversational partner they thought was human. When prompted to adopt a humanlike persona, GPT-4.5 was judged to be the human 73% of the time: significantly more often than interrogators selected the real human participant. LLaMa-3.1, with the same prompt, was judged to be the human 56% of the time -- not significantly more or less often than the humans they were being compared to -- while baseline models (ELIZA and GPT-4o) achieved win rates significantly below chance (23% and 21% respectively). The results constitute the first empirical evidence that any artificial system passes a standard three-party Turing test. The results have implications for debates about what kind of intelligence is exhibited by Large Language Models (LLMs), and the social and economic impacts these systems are likely to have.

Factor analysis studies have shown that the primary dimensions of word meaning are Valence (V), Arousal (A), and Dominance (D) (also referred to in social cognition research as Competence (C)). These dimensions impact various aspects of our lives from social competence and emotion regulation to success in the work place and how we view the world. We present here the NRC VAD Lexicon v2, which has human ratings of valence, arousal, and dominance for more than 55,000 English words and phrases. Notably, it adds entries for $\sim$25k additional words to v1.0. It also now includes for the first time entries for common multi-word phrases (~10k). We show that the associations are highly reliable. The lexicon enables a wide variety of research in psychology, NLP, public health, digital humanities, and social sciences. The NRC VAD Lexicon v2 is made freely available for research through our project webpage.

Code-switching, the act of alternating between languages, emerged as a prevalent global phenomenon that needs to be addressed for building user-friendly language technologies. A main bottleneck in this pursuit is data scarcity, motivating research in the direction of code-switched data augmentation. However, current literature lacks comprehensive studies that enable us to understand the relation between the quality of synthetic data and improvements on NLP tasks. We extend previous research conducted in this direction on machine translation (MT) with results on automatic speech recognition (ASR) and cascaded speech translation (ST) to test generalizability of findings. Our experiments involve a wide range of augmentation techniques, covering lexical replacements, linguistic theories, and back-translation. Based on the results of MT, ASR, and ST, we draw conclusions and insights regarding the efficacy of various augmentation techniques and the impact of quality on performance.