The £5.30 orange juice that tells the story of why supermarket prices are sky high There has been more than a bitter twang in the glasses at British breakfast tables. Only five years ago, a typical supermarket own-label carton of orange juice could be bought for 76p for 1 litre. One colleague was outraged to be sent a bill for £9 for a glass of hangover-busting orange juice and lemonade at an unassuming little restaurant in Kent. Asked why so much, she was told that the orange juice - albeit freshly squeezed - accounted for £5.30 of the price. Yet as costs have surged, the taste is changing too, with certain manufacturers substituting oranges for mandarins to cut costs.

Our ideas of what qualifies as breakfast food are cultural distinctions, not scientific ones. Breakthroughs, discoveries, and DIY tips sent every weekday. If you live in the United States, there's a good chance you were picturing some combination of eggs, bacon, cereal, and/or pancakes. Of course, we also know that a classic British breakfast consists of beans and fried bread--two savory foods most Americans don't associate with their first meal of the day. Is there a science behind why we think of some foods as breakfast and others as not?

It's the job that puts the average 9–5 to shame. But while being an astronaut is a career many dream of, you might wonder how well it pays. Compared to office workers – who may complain about their commute – these highly–trained individuals are regularly launched into space at 17,500mph. While Earth-based employees might not rate their office canteen or grumble about the lack of toilets in the workplace, astronauts live off dehydrated food packets and must use specially–designed bathrooms. So you'd be forgiven for thinking that astronauts get paid a hefty wage for their daredevil profession.

We introduce VAULT, a fully automated adversarial RAG pipeline that systematically uncovers and remedies weaknesses in NLI models through three stages: retrieval, adversarial generation, and iterative retraining. First, we perform balanced few-shot retrieval by embedding premises with both semantic (BGE) and lexical (BM25) similarity. Next, we assemble these contexts into LLM prompts to generate adversarial hypotheses, which are then validated by an LLM ensemble for label fidelity. Finally, the validated adversarial examples are injected back into the training set at increasing mixing ratios, progressively fortifying a zero-shot RoBERTa-base model.On standard benchmarks, VAULT elevates RoBERTa-base accuracy from 88.48% to 92.60% on SNLI +4.12%, from 75.04% to 80.95% on ANLI +5.91%, and from 54.67% to 71.99% on MultiNLI +17.32%. It also consistently outperforms prior in-context adversarial methods by up to 2.0% across datasets. By automating high-quality adversarial data curation at scale, VAULT enables rapid, human-independent robustness improvements in NLI inference tasks.

EXCLUSIVE: An AI whizz has reimagined the Duke and Duchess of Sussex starring in a range of romantic comedies from Notting Hill to Love Actually.

It all starts with Efficiency, the first level of the framework. Level-one AI can carry out specific functions through automation that make user interactions more efficient and effective. Users can employ simple and easy voice commands for various tasks like stocking up on orange juice or simply asking for the time. Currently available with many voice recognition and AI-enabled products on the market today, such as the LG ThinQ air conditioner, many people may find some scenes depicted in this video familiar. Personalization is represented in the second level, at which AI can recognize patterns and regularities from past user interactions and use them to optimize and personalize functions for a specific person.

Various acronyms and words are thrown around while talking about Chatbots and at first glance it seems they're all interchangeable with each other. To understand what the future of chatbots holds, let's familiarize ourselves with three basic acronyms. I. NLP, or Natural Language Processing is a blanket term used to describe a machine's ability to ingest what is said to it, break it down, comprehend its meaning, determine appropriate action, and respond back in language the user will understand. NLU, or Natural Language Understanding is a subset of NLP that deals with the much narrower, but equally important facet of how to best handle unstructured inputs and convert them into a structured form that a machine can understand and act upon. While humans are able to effortlessly handle mispronunciations, swapped words, contractions, colloquialisms, and other quirks, machines are less adept at handling unpredictable inputs.