PCWorld explores the reality of "vibe coding" with AI tools, where the author attempted to build four apps using Claude Code and Google's Antigravity. Only one Docker Swarm dashboard succeeded after a week of effort, while three OpenClaw replications failed due to vague prompts and poor planning. The experience reveals that AI-assisted development still requires significant human creativity, detailed blueprints, and specific instructions to avoid "garbage in, garbage out" results. Like so many others, I jumped onto the vibe coding bandwagon, entranced by the idea of building my own incredibly useful apps with nothing but an AI prompt. Over the course of about six weeks, I did manage to build my own apps-four of them, to be precise.

The space data-center wars are coming--and they're going to be ugly. Earlier this month, Google researchers released a paper about "Project Suncatcher," the company's research "moonshot" to build data centers in space. The paper's authors don't mince words when it comes to the challenges the tech giant is facing from A.I.'s energy demands, and their planned solution is to launch "fleets of satellites" into space and harvest energy from the sun. Google's space-based data centers won't be gigantic monolithic buildings like the data centers we have on Earth, but a "constellation of solar-powered satellites" carrying tensor processing units (the processors used to power Google's A.I. systems). The paper boasts that the company's data center fleet "will be significantly larger than any previous or current satellite constellations" in orbit.

Underwater pollution is one of today's most significant environmental concerns, with vast volumes of garbage found in seas, rivers, and landscapes around the world. Accurate detection of these waste materials is crucial for successful waste management, environmental monitoring, and mitigation strategies. In this study, we investigated the performance of five cutting-edge object recognition algorithms, namely YOLO (You Only Look Once) models, including YOLOv7, YOLOv8, YOLOv9, YOLOv10, and Faster Region-Convolutional Neural Network (R-CNN), to identify which model was most effective at recognizing materials in underwater situations. The models were thoroughly trained and tested on a large dataset containing fifteen different classes under diverse conditions, such as low visibility and variable depths. From the above-mentioned models, YOLOv8 outperformed the others, with a mean Average Precision (mAP) of 80.9%, indicating a significant performance. This increased performance is attributed to YOLOv8's architecture, which incorporates advanced features such as improved anchor-free mechanisms and self-supervised learning, allowing for more precise and efficient recognition of items in a variety of settings. These findings highlight the YOLOv8 model's potential as an effective tool in the global fight against pollution, improving both the detection capabilities and scalability of underwater cleanup operations.

Having your favourite TV show or movie interrupted by adverts is already frustrating, but things could soon be getting worse for Netflix users. At its'Upfront' event on Wednesday, the streaming giant revealed that it would be incorporating adverts made with'generative AI'. Arriving in 2026, these AI-generated adverts will begin to appear not only during mid-content breaks but also when users press pause. And the only way to get rid of these annoying intrusions will be to pay for the more expensive ad-free subscriptions. But in a further twist, Netflix says AI would be used'instantly marry advertisers' ads with the worlds of our shows'.

Ever since ChatGPT was released to the public in November 2022, people have been using it to generate text, from emails to blog posts to bad poetry, much of which they post online. Since that release, the companies that build the large language models (LLMs) on which such chatbots are based--such as OpenAI's GPT 3.5, the technology underlying ChatGPT--have also continued to put out newer versions of their models, training them with new text data, some of which they scraped off the Web. That means, inevitably, that some of the training data used to create LLMs did not come from humans, but from the LLMs themselves. That has led computer scientists to worry about a phenomenon they call model collapse. Basically, model collapse happens when the training data no longer matches real-world data, leading the new LLM to produce gibberish, in a 21st-century version of the classic computer aphorism "garbage in, garbage out."

If scientists had a time machine, having a conversation with a Brit from even just 250 years ago could be very confusing. Although they'd be speaking the same language as us, the meaning of many English words have dramatically changed. In fact, the mention of things like'fudge', 'meat', 'pink', 'stripe', 'flirt' and'artificial' in a certain context could send our 18th century ancestors into a muddle. Lynne Cahill, a linguistics professor at the University of Sussex, said some words change their meanings and others don't because'there are lots of things going on'. 'As our lives change, we need words for different things, so some meanings go out of use (think of different types of horse-drawn carriage) and new ones come in (think of technology, like mobile phones and computers),' she told MailOnline. 'Languages deal with these things in different ways, sometimes using existing words with related meanings to refer to new things.' MailOnline has scoured the historical records and dictionaries to find more than 40 words that once had a very different definition.

Robots can identify recyclable materials by recognizing patterns in colors, textures, shapes and logos. Ever wondered what happens to the recyclables you carefully sort and place in your bin? For years, recycling has been a crucial part of our efforts to reduce waste and protect the environment. However, the recycling industry has faced significant challenges, from rising costs to labor shortages. But what if technology could transform this process, making recycling faster, more efficient and actually effective?

SELLAS Life Sciences CEO Angelos Stergiou joined'Fox & Friends' to discuss how artificial intelligence has positively impacted medicine as his company works to develop a vaccine for leukemia. SELLAS Life Sciences CEO Angelos Stergiou says his company is already on the cusp of a finalized leukemia vaccine, but another game-changer – personalized cancer vaccines – could be on the horizon, thanks to artificial intelligence. "I think it's going to be a revolutionary decade in medicine and in clinical research," he said Thursday on "Fox & Friends." "Where AI comes into play is where it's going to allow us to do things expeditiously, and it's going to be more personalized. In other words, if you have a patient with a cancer, we can then use AI to do a genomic sequencing and, with the results, we can then either create a specific vaccine or treatment, or we can say this specific treatment will work for the patient."

In recent years, enormous progress has been made in the field of large language models (LLMs). Based on neural network architectures, specifically transformer models, they have proven highly effective in natural language processing (NLP). The models are designed to understand, generate, and work with human language. Trained on large datasets consisting of text from the Internet, books, articles, and many other data sources, the model learns to predict the next word in a sentence based on previous words. LLMs are not only able to generate human language but can also generate source code to support humans in the implementation of software systems.

These comprise the following modules: using machine learning and satellite imagery. The platform includes modules for data collection, Data Collection: This module provides tools to image augmentation, training, testing, and prediction, extract suitable image patches from pre-existing images streamlining the entire workflow for image or download satellite imagery from sources like classification tasks. This paper presents a detailed Google Earth. It also includes features for labeling overview of the capabilities of deepTerra, shows and organizing datasets efficiently. When geographic how it has been applied to various research areas, coordinates are available, they are automatically and discusses the future directions it might take.