It's been instructive to see "Ballerina," which opens this week, so soon after the new "Mission: Impossible" installment. In the latter, it's hard to top Tom Cruise's intrepid stunt work, which reaches its zenith in a pair of extended sequences (one in a submarine, the other on biplanes), but the story, involving a diabolical scheme using A.I. to commandeer and launch the world's nuclear weaponry, is a mere pretext. Going to "Mission: Impossible" for the story is like going to Casablanca for the waters. In contrast, "Ballerina"--like the four John Wick films that it's spun off from--is, strangely, far better at story than at action. The first John Wick film is the weakest, because the framework for the franchise was still unformed: a retired hit man (Keanu Reeves) gets back into action to respond to a mobster's attacks.

The modern enterprise is facing an unprecedented surge in digital identities, with machine identities now significantly outnumbering human identities. This paper examines the cybersecurity risks emerging from what we define as the "human-machine identity blur" - the point at which human and machine identities intersect, delegate authority, and create new attack surfaces. Drawing from industry data, expert insights, and real-world incident analysis, we identify key governance gaps in current identity management models that treat human and machine entities as separate domains. To address these challenges, we propose a Unified Identity Governance Framework based on four core principles: treating identity as a continuum rather than a binary distinction, applying consistent risk evaluation across all identity types, implementing continuous verification guided by zero trust principles, and maintaining governance throughout the entire identity lifecycle. Our research shows that organizations adopting this unified approach experience a 47 percent reduction in identity-related security incidents and a 62 percent improvement in incident response time. We conclude by offering a practical implementation roadmap and outlining future research directions as AI-driven systems become increasingly autonomous.

Named Entity Recognition (NER) is a critical component of Natural Language Processing (NLP) for extracting structured information from unstructured text. However, for low-resource languages like Catalan, the performance of NER systems often suffers due to the lack of high-quality annotated datasets. This paper introduces NERCat, a fine-tuned version of the GLiNER[1] model, designed to improve NER performance specifically for Catalan text. We used a dataset of manually annotated Catalan television transcriptions to train and fine-tune the model, focusing on domains such as politics, sports, and culture. The evaluation results show significant improvements in precision, recall, and F1-score, particularly for underrepresented named entity categories such as Law, Product, and Facility. This study demonstrates the effectiveness of domain-specific fine-tuning in low-resource languages and highlights the potential for enhancing Catalan NLP applications through manual annotation and high-quality datasets.

Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs), has significantly advanced Natural Language Processing (NLP) tasks, such as Named Entity Recognition (NER), which involves identifying entities like person, location, and organization names in text. LLMs are especially promising for low-resource languages due to their ability to learn from limited data. However, the performance of GenAI models for Nepali, a low-resource language, has not been thoroughly evaluated. This paper investigates the application of state-of-the-art LLMs for Nepali NER, conducting experiments with various prompting techniques to assess their effectiveness. Our results provide insights into the challenges and opportunities of using LLMs for NER in low-resource settings and offer valuable contributions to the advancement of NLP research in languages like Nepali.

While Large Language Models (LLMs) are fundamentally next-token prediction systems, their practical applications extend far beyond this basic function. From natural language processing and text generation to conversational assistants and software use, LLMs have numerous use-cases, and have already acquired a significant degree of enterprise adoption. To evaluate such models, static evaluation datasets, consisting of a set of prompts and their corresponding ground truths, are often used to benchmark the efficacy of the model for a particular task. In this paper, we provide the basis for a more comprehensive evaluation framework, based upon a traditional game and tool-based architecture that enables a more overarching measurement of a model's capabilities. For simplicity, we provide a generalized foundation that can be extended, without significant alteration, to numerous scenarios, from specific use cases such as supply chain management or financial reasoning, to abstract measurements such as ethics or safety.

Will.i.am has been embracing innovative technology for years. Now he is using artificial intelligence in an effort to transform how we listen to the radio. The musician, entrepreneur and tech investor has launched RAiDiO.FYI, a set of interactive radio stations themed around topics like sport, pop culture, and politics. Each station is fundamentally interactive: tune in and you'll be welcomed by name by an AI host "live from the ether," the Black Eyed Peas frontman tells TIME. Hosts talk about their given topic before playing some music.

Leaders of the G7 meet in the southern Japanese city of Hiroshima for their annual summit from May 19 – 21. The are expected to discuss not only economics, but politics, and Russia's February 2022 full-scale invasion of Ukraine. China, which has become increasingly assertive in its claims in the disputed South China Sea and over self-ruled Taiwan, is also likely to be an issue along with North Korea's weapons testing. Here's a look at the G7 and what to expect: The Group of Seven (G7) is an informal group of leading industrialised democracies with no permanent secretariat or legal status. It consists of Canada, France, Germany, Italy, Japan, the United Kingdom and the United States.

We are in the early days of generative AI, and there's a gold rush to gain position and prominence in the sector as it takes off. But with this rush to implementation across a bewildering range of use cases come associated risks. Get artificial intelligence (AI) right, and it can be an incredibly creative, labor-saving, and efficiency-improving solution. Employ it badly, and you risk social, financial, and even legal consequences. First, there's the difficulty of predicting their value or likely success, given the unpredictability of AI outputs.

Deep-sea mining would cause "extensive and irreversible" damage to sensitive habitats.NOAA This story was originally published by the Guardian and is reproduced here as part of the Climate Desk collaboration. An investigation by conservationists has found evidence that deep-seabed mining of rare minerals could cause "extensive and irreversible" damage to the planet. The report, published on Monday by the international wildlife charity Fauna & Flora, adds to the growing controversy that surrounds proposals to sweep the ocean floor of rare minerals that include cobalt, manganese and nickel. Mining companies want to exploit these deposits--which are crucial to the alternative energy sector--because land supplies are running low, they say.

Read on to learn several key ways AI can increase business productivity, including customer service, project management, and overall operations. Over the last few decades, we have seen a significant rise in technology adoption in everyday business. Organizations worldwide have undergone the digital revolution, embracing technologies that promise improved efficiency, productivity, and profitability. As a result, organizations have evinced interest in Artificial Intelligence (AI) and Machine Learning (ML). AI increases overall efficiency while assisting them to save the valuable time of their employees.