Environment Animals Insects This bug uses'red flags' to tell predators to buzz off The tropical Matador bug's fancy dance is not a mating ritual. Breakthroughs, discoveries, and DIY tips sent every weekday. In the forests of Panama, the matador bug () frequently looks like it is waving . The insect uses the red flag-looking appendages on its hind legs to perform an intricate and mysterious display. Now, we have even more evidence that this funky leg dance has a serious purpose-telling predators to buzz off.

The chatbot-enabled Friend necklace eavesdrops on your life and provides a running commentary that's snarky and unhelpful. Worse, it can also make the people around you uneasy. The AI-powered Friend pendant is now out in the world. If you live in the US or Canada, you can buy one for $129. The smooth plastic disc is just under 2 inches in diameter; it looks and feels a little like a beefy Apple AirTag. Inside are some LEDs and a Bluetooth radio that connects you (through your iPhone) to a chatbot in the cloud that's powered by Google's Gemini 2.5 model. You can tap on the disc to ask your Friend questions as it dangles around your neck, and it responds to your voice prompts by sending you text messages through the companion app.

Buying something in the shops used to be as simple as choosing the item and handing over the money. But in recent years, the great British shopping experience has dramatically changed. In 2025, artificial intelligence (AI) is patrolling Britain's retail stores to keep an eye on customers as they stock up on essentials. Now, people are subjected to a slew of AI-powered tech, including intelligent surveillance cameras, robots, facial recognition systems and online age checks. Home Bargains is the latest to follow the trend, with a new AI-enabled security system that watches you while you scan your own items.

Pre-trained on massive amounts of code and text data, large language models (LLMs) have demonstrated remarkable achievements in performing code generation tasks. With additional execution-based feedback, these models can act as agents with capabilities to self-refine and improve generated code autonomously. However, on challenging coding tasks with extremely large search space, current agentic approaches still struggle with multi-stage planning, generating, and debugging. To address this problem, we propose CodeTree, a framework for LLM agents to efficiently explore the search space in different stages of the code generation process. Specifically, we adopted a unified tree structure to explicitly explore different coding strategies, generate corresponding coding solutions, and subsequently refine the solutions. In each stage, critical decision-making (ranking, termination, expanding) of the exploration process is guided by both the environmental execution-based feedback and LLM-agent-generated feedback. We comprehensively evaluated CodeTree on 7 code generation benchmarks and demonstrated the significant performance gains of CodeTree against strong baselines. Using GPT-4o as the base model, we consistently achieved top results of 95.1 on HumanEval, 98.7 on MBPP, and 43.0 on CodeContests. On the challenging SWEBench benchmark, our approach led to significant performance gains.

Large language models (LLMs) are essential in natural language processing but often struggle with inference speed and computational efficiency, limiting real-time deployment. The key-value (KV) cache mechanism reduces computational overhead in transformer models, but challenges in maintaining contextual understanding remain. In this paper, we propose BUZZ, a novel KV caching algorithm that leverages structured contextual information to minimize cache memory usage while enhancing inference speed. BUZZ employs a beehive-structured sparse cache, incorporating a sliding window to capture recent information and dynamically segmenting historical tokens into chunks to prioritize important tokens in local neighborhoods. We evaluate BUZZ on four real-world datasets: CNN/Daily Mail, XSUM, Wikitext, and 10-QA. Our results demonstrate that BUZZ (1) reduces cache memory usage by $\textbf{2.5}\times$ in LLM inference while maintaining over 99% accuracy in long-text summarization, and (2) surpasses state-of-the-art performance in multi-document question answering by $\textbf{7.69%}$ under the same memory limit, where full cache methods encounter out-of-memory issues. Additionally, BUZZ achieves significant inference speedup with a $\log{n}$ time complexity. The code is available at https://github.com/JunqiZhao888/buzz-llm.

As the summer begins to wane, cases of mosquito-borne diseases are creeping up in some parts of the United States. In other regions, the threat of malaria is a more constant issue even as vaccines continue to roll out. However, some new research on how they mate may help develop better improved techniques for controlling the mosquitoes that carry malaria. For male mosquitoes–who do not bite–the high-pitched buzzing of females is siren call that signals it is time to mate. However, there is even more to that signal than scientists first realized.

MILAN – The rapid adoption of generative artificial intelligence has boosted markets this year, but after the initial euphoria, investors are waking up to the possible risks, including the need to be highly selective in stock-picking. Businesses ranging from IT services and consulting to media, information and education are now under portfolio managers' microscopes to assess the potential for AI disruption. The overall impact for corporate profitability is seen as hugely positive. Yet beyond Nvidia and other obvious winners in the chip sector, analysts warn there might also be losers across Europe and the United States. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

Generative AI has clearly made an impression on the world in the past few months with the release of large language models like ChatGPT. Generative AI has several uses inside and outside the enterprise that I would love to discuss in this article. In simple terminology, generative AI is a class of artificial intelligence models that can create new data, such as images, music, or text, that resembles and often expands upon the patterns present in the training data it was trained on. ChatGPT's case contains content from across the web in many different file types, such as audio, text, video, images, 3D models, code, and so on. The model learns patterns from this data and data that is input by the 100 million users the system accounted for in the beginning of January 2023 – tallying about 13 million unique visitors per day in the same month.

In the face of so much ChatGPT news and buzz, Google announced on Monday its own chatbot AI project, Bard, will be unveiled with more details at Wednesday's Google Presents event in Paris. Bard will serve as an "experimental conversational AI service," according to a blog post by Google CEO Sundar Pichai, Monday. It uses Google's existing Language Model for Dialogue Applications (LaMDA) platform, which the company has been developing for the past two years. However, it won't be open to everyone, like ChatGPT currently is, which ruins the hype a little. Google is starting with a lightweight version of LaMDA, open to a select group of trusted users before scaling up.

Reports have surfaced that Microsoft Corp. is in advanced negotiations to make a massive investment in OpenAI, the company behind the revolutionary ChatGPT chatbot. Insiders reveal that the deal could see Microsoft pumping in a whopping $10 billion, which would value OpenAI at an impressive $29 billion. This means that the non-profit OpenAI Inc. could own a measly 2% while 49% will remain for other investors as Microsoft eats up the lion's share. This potential investment has far-reaching implications for the future of OpenAI and the broader AI sector. The decision to make Microsoft the majority shareholder could potentially change OpenAI's initial goals of being open and accessible to the public, its namesake. It could also raise questions about the future direction and priorities of the organization and its products.