It is one of the world's most popular messaging apps, used by more than two billion people around the globe. But within days, WhatsApp will stop working on three popular phones that are used by millions. From May 5, anyone still using a trio of Apple devices will no longer be able to send or receive messages on the app. After this date, only devices running the iOS 15.1 operating system or newer will be supported. The affected devices are the iPhone 5s, the iPhone 6, and the iPhone 6 Plus.

In recent years, the detection of AI-generated text has become a critical area of research due to concerns about academic integrity, misinformation, and ethical AI deployment. This paper presents COT Fine-tuned, a novel framework for detecting AI-generated text and identifying the specific language model. responsible for generating the text. We propose a dual-task approach, where Task A involves classifying text as AI-generated or human-written, and Task B identifies the specific LLM behind the text. The key innovation of our method lies in the use of Chain-of-Thought reasoning, which enables the model to generate explanations for its predictions, enhancing transparency and interpretability. Our experiments demonstrate that COT Fine-tuned achieves high accuracy in both tasks, with strong performance in LLM identification and human-AI classification. We also show that the CoT reasoning process contributes significantly to the models effectiveness and interpretability.

Most datasets for sentiment analysis lack context in which an opinion was expressed, often crucial for emotion understanding, and are mainly limited by a few emotion categories. Foundation large language models (LLMs) like GPT-4 suffer from over-predicting emotions and are too resource-intensive. We design an LLM-based data synthesis pipeline and leverage a large model, Mistral-7b, for the generation of training examples for more accessible, lightweight BERT-type encoder models. We focus on enlarging the semantic diversity of examples and propose grounding the generation into a corpus of narratives to produce non-repetitive story-character-centered utterances with unique contexts over 28 emotion classes. By running 700K inferences in 450 GPU hours, we contribute with the dataset of 100K contextual and also 300K context-less examples to cover both scenarios. We use it for fine-tuning pre-trained encoders, which results in several Emo Pillars models. We show that Emo Pillars models are highly adaptive to new domains when tuned to specific tasks such as GoEmotions, ISEAR, IEMOCAP, and EmoContext, reaching the SOTA performance on the first three. We also validate our dataset, conducting statistical analysis and human evaluation, and confirm the success of our measures in utterance diversification (although less for the neutral class) and context personalization, while pointing out the need for improved handling of out-of-taxonomy labels within the pipeline.

Recommender systems (RS) have become essential in filtering information and personalizing content for users. RS techniques have traditionally relied on modeling interactions between users and items as well as the features of content using models specific to each task. The emergence of foundation models (FMs), large scale models trained on vast amounts of data such as GPT, LLaMA and CLIP, is reshaping the recommendation paradigm. This survey provides a comprehensive overview of the Foundation Models for Recommender Systems (FM4RecSys), covering their integration in three paradigms: (1) Feature-Based augmentation of representations, (2) Generative recommendation approaches, and (3) Agentic interactive systems. We first review the data foundations of RS, from traditional explicit or implicit feedback to multimodal content sources. We then introduce FMs and their capabilities for representation learning, natural language understanding, and multi-modal reasoning in RS contexts. The core of the survey discusses how FMs enhance RS under different paradigms. Afterward, we examine FM applications in various recommendation tasks. Through an analysis of recent research, we highlight key opportunities that have been realized as well as challenges encountered. Finally, we outline open research directions and technical challenges for next-generation FM4RecSys. This survey not only reviews the state-of-the-art methods but also provides a critical analysis of the trade-offs among the feature-based, the generative, and the agentic paradigms, outlining key open issues and future research directions.

In this paper we evaluate performance of the SeqSLAM algorithm for passive vision-based localization in very dark environments with low-cost cameras that result in massively blurred images. We evaluate the effect of motion blur from exposure times up to 10,000 ms from a moving car, and the performance of localization in day time from routes learned at night in two different environments. Finally we perform a statistical analysis that compares the baseline performance of matching unprocessed grayscale images to using patch normalization and local neighborhood normalization - the two key SeqSLAM components. Our results and analysis show for the first time why the SeqSLAM algorithm is effective, and demonstrate the potential for cheap camera-based localization systems that function despite extreme appearance change.

Evaluating Large Language Models (LLMs) for safety and security remains a complex task, often requiring users to navigate a fragmented landscape of ad hoc benchmarks, datasets, metrics, and reporting formats. To address this challenge, we present aiXamine, a comprehensive black-box evaluation platform for LLM safety and security. aiXamine integrates over 40 tests (i.e., benchmarks) organized into eight key services targeting specific dimensions of safety and security: adversarial robustness, code security, fairness and bias, hallucination, model and data privacy, out-of-distribution (OOD) robustness, over-refusal, and safety alignment. The platform aggregates the evaluation results into a single detailed report per model, providing a detailed breakdown of model performance, test examples, and rich visualizations. We used aiXamine to assess over 50 publicly available and proprietary LLMs, conducting over 2K examinations. Our findings reveal notable vulnerabilities in leading models, including susceptibility to adversarial attacks in OpenAI's GPT-4o, biased outputs in xAI's Grok-3, and privacy weaknesses in Google's Gemini 2.0. Additionally, we observe that open-source models can match or exceed proprietary models in specific services such as safety alignment, fairness and bias, and OOD robustness. Finally, we identify trade-offs between distillation strategies, model size, training methods, and architectural choices.

Feedback is New Scientist's popular sideways look at the latest science and technology news. You can submit items you believe may amuse readers to Feedback by emailing feedback@newscientist.com Science is one of the most fruitful sources of new terminology. There's nothing like a surfeit of terms like "mitochondrial synthesis" and "quantum fluctuations" to make your writing sound authoritative Recently there has been a spate of scientific papers containing the phrase "vegetative electron microscopy/microscope". The term suggests a device for scanning broccoli, but it is utter nonsense. There are scanning electron microscopes and tunnelling electron microscopes, but not vegetative electron microscopes.

Rep. Jasmine Crockett said she would "absolutely" take a head-to-head IQ test against President Donald Trump during an interview with late-night host Jimmy Kimmel. Rep. Jasmine Crockett, D-Texas, told late-night host Jimmy Kimmel on Tuesday that she would "absolutely" take a head-to-head IQ test against President Donald Trump. "He also called you low IQ, I'm sure you're aware of that. Would you be willing to take an IQ test publicly head-to-head against the President of the United States?" Kimmel played a clip of Trump talking about the Democratic lawmaker, during which he called Crockett the Democrats' "new star," and suggested the party was in trouble if that was the case.

Get all the books your heart desires during Amazon's Book Sale. Even though Amazon is now a global shipper of pretty much any item you can think of, their heart still lies with their original items: books! Amazon is currently running its Amazon Book Sale, April 23 – 28. During the sale, eBooks are up to 80% off, print books are up to 60% off, and you can find hundreds of audiobooks under 8. Amazon's Kindle Scribe and Colorsoft are also on sale. Right now, you can also access Kindle Unlimited for just 0.99.

But none of that would have been possible if I hadn't been bored and curious. The university computer lab may seem at first like an unlikely center for creativity. We tend to think of creativity as happening more in the artist's studio or writers' workshop. But throughout history, very often our greatest creative leaps--and I would argue that the web and its descendants represent one such leap--have been due to advances in technology. There are the big easy examples, like photography or the printing press, but it's also true of all sorts of creative inventions that we often take for granted.