Within days of being released from his seventh prison term for romance fraud, Raymond McDonald was back on a dating app looking for his next victim. Over more than 20 years he had racked up 58 convictions, mostly for fraud and theft, while telling lies on an industrial scale and taking thousands of pounds from women for holidays and weddings which were never going to happen. This time when he went looking, the BBC was waiting. He thought he was having a date with Kaye, but instead found himself being approached by a BBC reporter and camera crew. He had met Kaye online and, calling himself Rob, told her he was a deep-sea diver looking for a wife.

Shadow or no shadow, the rodents have a special set of ecological skills. Breakthroughs, discoveries, and DIY tips sent six days a week. Despite advances in meteorological technology, we still keep a close eye on a rodent's burrow every February 2 for a weather forecast. While groundhogs --also called woodchucks--have been associated with the end of winter and beginning of spring for centuries, there's more to know about our rodent friends than their amateur Al Roker'ing. Unlike bears, groundhogs are true hibernators.

To improve accuracy, reduce wait times and ease worker stress, fast-food giants McDonald's and Papa Johns are deploying artificial intelligence ordering systems.

Toffee Crisp and Blue Riband can't be called chocolate any more Toffee Crisp and Blue Riband bars can no longer be called chocolate after maker Nestle changed their recipes. To be described as milk chocolate in the UK a product needs to have at least 20% cocoa solids and 20% milk solids, a level each product fell below once a higher amount of cheaper vegetable fat was used. Nestle said its reformulations were needed due to higher input costs but were carefully developed and sensory tested and there were no plans to alter the recipes of other chocolate products. As many ingredient costs, such as cocoa and butter, increased food companies have altered recipes to use less of the expensive ingredients, as well as shrinking serving sizes. Nestle now describes the treats as being encased in a smooth milk chocolate flavour coating rather than being covered in milk chocolate.

McDonald's pulls AI Christmas ad after backlash McDonald's has taken down a Christmas advert made with Artificial Intelligence (AI) following online backlash. The 45-second advert was produced with generative AI clips and released publicly on McDonald's Netherlands YouTube channel on 6 December. Viewers on social media denounced the use of AI in the film, with one commenter calling it the most god-awful ad I've seen this year . On 9 December McDonald's Netherlands removed the video, adding in a statement to BBC News that the moment served as an important learning as the company explored the effective use of AI. The advert was created for McDonald's by Dutch company TBWA\Neboko and US production company The Sweetshop.

Recent advances in Multimodal Large Language Models (MLLMs) have enabled their use as intelligent agents for smartphone operation. However, existing methods depend on the Android Debug Bridge (ADB) for data transmission and action execution, limiting their applicability to Android devices. In this work, we introduce the novel Embodied Smartphone Operation (ESO) task and present See-Control, a framework that enables smartphone operation via direct physical interaction with a low-DoF robotic arm, offering a platform-agnostic solution. See-Control comprises three key components: (1) an ESO benchmark with 155 tasks and corresponding evaluation metrics; (2) an MLLM-based embodied agent that generates robotic control commands without requiring ADB or system back-end access; and (3) a richly annotated dataset of operation episodes, offering valuable resources for future research. By bridging the gap between digital agents and the physical world, See-Control provides a concrete step toward enabling home robots to perform smartphone-dependent tasks in realistic environments.

Backdoor attacks pose a serious threat to the secure deployment of large language models (LLMs), enabling adversaries to implant hidden behaviors triggered by specific inputs. However, existing methods often rely on manually crafted triggers and static data pipelines, which are rigid, labor-intensive, and inadequate for systematically evaluating modern defense robustness. As AI agents become increasingly capable, there is a growing need for more rigorous, diverse, and scalable \textit{red-teaming frameworks} that can realistically simulate backdoor threats and assess model resilience under adversarial conditions. In this work, we introduce \textsc{AutoBackdoor}, a general framework for automating backdoor injection, encompassing trigger generation, poisoned data construction, and model fine-tuning via an autonomous agent-driven pipeline. Unlike prior approaches, AutoBackdoor uses a powerful language model agent to generate semantically coherent, context-aware trigger phrases, enabling scalable poisoning across arbitrary topics with minimal human effort. We evaluate AutoBackdoor under three realistic threat scenarios, including \textit{Bias Recommendation}, \textit{Hallucination Injection}, and \textit{Peer Review Manipulation}, to simulate a broad range of attacks. Experiments on both open-source and commercial models, including LLaMA-3, Mistral, Qwen, and GPT-4o, demonstrate that our method achieves over 90\% attack success with only a small number of poisoned samples. More importantly, we find that existing defenses often fail to mitigate these attacks, underscoring the need for more rigorous and adaptive evaluation techniques against agent-driven threats as explored in this work. All code, datasets, and experimental configurations will be merged into our primary repository at https://github.com/bboylyg/BackdoorLLM.

This study uses double/debiased machine learning (DML) to evaluate the impact of transitioning from lecture-based blended teaching to a flipped classroom concept. Our findings indicate effects on students' self-conception, procrastination, and enjoyment. We do not find significant positive effects on exam scores, passing rates, or knowledge retention. This can be explained by the insufficient use of the instructional approach that we can identify with uniquely detailed usage data and highlights the need for additional teaching strategies. Methodologically, we propose a powerful DML approach that acknowledges the latent structure inherent in Likert scale variables and, hence, aligns with psychometric principles.

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