Breakthroughs, discoveries, and DIY tips sent every weekday. For over a century, simple lactic acid bacteria has been one of the most reliable additives to keep food and drinks safe for over a century. It goes in butter, cheese, and other dairy products to help extend their shelf life. Now, a team in Denmark has uncovered some of the preservation aid's earliest examples. Their findings, published in the, only come after a chance discovery hidden away in the bowels of a university basement.

The roughly 350-year-old elongated skull is not Incan at all. Breakthroughs, discoveries, and DIY tips sent every weekday. While re-evaluating a centuries' old, elongated skull originally theorized to belong to an Incan "tribal chief," a team of archaeologists in Switzerland came across a startling discovery. The mummified cranium instead likely belonged to a respected member of a separate indigenous community that lived as subjects within the Incan empire. Their findings are published in the and are now helping recontextualize the remains while also addressing the field's longstanding ethical issues.

DeeperDive, a new tool that converses with readers, is an effort to beat the AI industry at its own game. The publishing company behind USA Today and 220 other publications is today rolling out a chatbot -like tool called DeeperDive that can converse with readers, summarize insights from its journalism, and suggest new content from across its sites. "Visitors now have a trusted AI answer engine on our platform for anything they want to engage with, anything they want to ask," Mike Reed, CEO of Gannett and the USA Today Network, said at the WIRED AI Power Summit in New York, an event that brought together voices from the tech industry, politics, and the world of media. "and it is performing really great." Most publishers have a fraught relationship with AI, as the chatbots that trained on their content are now summarizing it and eating the traffic that search engines used to send them.

Each year, MIT Technology Review honors 35 outstanding people under the age of 35 who are driving scientific progress and solving tough problems in their fields. Today we want to introduce you to the computing innovators on the list who are coming up with new AI chips and specialized datasets--along with smart ideas about how to assess advanced systems for safety. Earlier this year, the $800 million Vera Rubin Observatory commenced its decade-long quest to create an extremely detailed time-lapse movie of the universe. Rubin is capable of capturing many more stars than any other astronomical observatory ever built; it also sees many more satellites. Up to 40% of images captured by the observatory within its first 10 years of operation will be marred by their sunlight-reflecting streaks. Meredith Rawls, a research scientist at the telescope's flagship observation project, Vera Rubin's Legacy Survey of Space and Time, is one of the experts tasked with protecting Rubin's science mission from the satellite blight.

The 77th Primetime Emmy Awards have taken place in Los Angeles on Sunday night, with shows The Studio, The Pit and Adolescence dominating the awards. Owen Cooper became the youngest ever male Emmy winner at 15-years-old, for his breakout role in the Netflix miniseries Adolescence. Seth Rogan's comedy series The Studio scooped up four Emmys, while The Pitt beat out the likes of Severance and The White Lotus to win Best Drama. 'No doubt' Russia will cross Nato border if Ukraine falls, former US VP says Former US Vice-President Mike Pence calls for security guarantees in Ukraine to help deliver "just and lasting peace". The US House Oversight Committee has released new surveillance footage recorded hours before the convicted paedophile's death.

The Pixel 10 Pro offers the best of Google's hardware without an enormous screen, making it a contender for the top smaller phone. The Pixel 10 Pro offers the best of Google's hardware without an enormous screen, making it a contender for the top smaller phone. Mon 15 Sep 2025 02.00 EDTLast modified on Mon 15 Sep 2025 02.03 EDT The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link. The Pixel 10 Pro is Google's best phone that is still a pocketable, easy-to-handle size, taking the excellent Pixel 10 and beefing it up in the camera department.

A statue of young swordsman Tanjiro Kamado, the title character in the Demon Slayer anime franchise, is displayed at a cinema in Tokyo's Ikebukuro district on Sept. 9. | AFP-JIJI Sony Pictures' Demon Slayer: Kimetsu no Yaiba The Movie: Infinity Castle has become the top movie in U.S. and Canadian theaters, setting an opening weekend record for the Japanese anime genre. The film, about a boy who joins an organization that hunts demons after his little sister is turned into one, brought in $70 million, the division of Sony said Sunday. That topped the previous $31 million record set by Pokemon: The First Movie -- Mewtwo Strikes Back in 1999. Box Office Pro had forecast weekend sales of $60 million to $75 million. The opening underscores the surging global appeal of Japanese anime.

On one hand, recent advances in chatbots has led to a rising popularity in using these models for coding tasks. On the other hand, modern generative image models primarily rely on text encoders to translate semantic concepts into visual representations, even when there is clear evidence that audio can be employed as input as well. Given the previous, in this work, we explore whether state-of-the-art conversational agents can design effective audio encoders to replace the CLIP text encoder from Stable Diffusion 1.5, enabling image synthesis directly from sound. We prompted five publicly available chatbots to propose neural architectures to work as these audio encoders, with a set of well-explained shared conditions. Each valid suggested encoder was trained on over two million context related audio-image-text observations, and evaluated on held-out validation and test sets using various metrics, together with a qualitative analysis of their generated images. Although almost all chatbots generated valid model designs, none achieved satisfactory results, indicating that their audio embeddings failed to align reliably with those of the original text encoder. Among the proposals, the Gemini audio encoder showed the best quantitative metrics, while the Grok audio encoder produced more coherent images (particularly, when paired with the text encoder). Our findings reveal a shared architectural bias across chatbots and underscore the remaining coding gap that needs to be bridged in future versions of these models. We also created a public demo so everyone could study and try out these audio encoders. Finally, we propose research questions that should be tackled in the future, and encourage other researchers to perform more focused and highly specialized tasks like this one, so the respective chatbots cannot make use of well-known solutions and their creativity/reasoning is fully tested.

A long-standing challenge in developing accurate recommendation models is simulating user behavior, mainly due to the complex and stochastic nature of user interactions. Towards this, one promising line of work has been the use of Large Language Models (LLMs) for simulating user behavior. However, aligning these general-purpose large pre-trained models with user preferences necessitates: (i) effectively and continously parsing large-scale tabular user-item interaction data, (ii) overcoming pre-training-induced inductive biases to accurately learn user specific knowledge, and (iii) achieving the former two at scale for millions of users. While most previous works have focused on complex methods to prompt an LLM or fine-tune it on tabular interaction datasets, our approach shifts the focus to extracting robust textual user representations using a frozen LLM and simulating cost-effective, resource-efficient user agents powered by fine-tuned Small Language Models (SLMs). Further, we showcase a method for training multiple low-rank adapters for groups of users or \textit{persona}, striking an optimal balance between scalability and performance of user behavior agents. Our experiments provide compelling empirical evidence of the efficacy of our methods, demonstrating that user agents developed using our approach have the potential to bridge the gap between offline metrics and real-world performance of recommender systems.

The rapid adoption of generative AI-powered search engines like ChatGPT, Perplexity, and Gemini is fundamentally reshaping information retrieval, moving from traditional ranked lists to synthesized, citation-backed answers. This shift challenges established Search Engine Optimization (SEO) practices and necessitates a new paradigm, which we term Generative Engine Optimization (GEO). This paper presents a comprehensive comparative analysis of AI Search and traditional web search (Google). Through a series of large-scale, controlled experiments across multiple verticals, languages, and query paraphrases, we quantify critical differences in how these systems source information. Our key findings reveal that AI Search exhibit a systematic and overwhelming bias towards Earned media (third-party, authoritative sources) over Brand-owned and Social content, a stark contrast to Google's more balanced mix. We further demonstrate that AI Search services differ significantly from each other in their domain diversity, freshness, cross-language stability, and sensitivity to phrasing. Based on these empirical results, we formulate a strategic GEO agenda. We provide actionable guidance for practitioners, emphasizing the critical need to: (1) engineer content for machine scannability and justification, (2) dominate earned media to build AI-perceived authority, (3) adopt engine-specific and language-aware strategies, and (4) overcome the inherent "big brand bias" for niche players. Our work provides the foundational empirical analysis and a strategic framework for achieving visibility in the new generative search landscape.