You're only supposed to blow the bloody hooves off: AI Michael Caine narrates Odyssey audiobook AI company ElevenLabs unveils its officially licensed replica of the iconic actor's voice in a retelling of Homer's epic poem, while director who previously recorded the star recalls real-life experience Next month, Christopher Nolan's blockbuster version of The Odyssey is set to storm cinemas around the globe. Auguries suggest the almost three-hour drama will repeat the success of Nolan's previous film both at the box office (Oppenheimer took nearly a billion dollars) and the Academy Awards (it won seven Oscars). But before that, a new audiobook version of Homer's tale has been released starring one of Nolan's most frequent collaborators: Michael Caine, with whom he has worked on eight films, including the Dark Knight trilogy. Caine, now 93, announced his retirement at the Red Sea film festival in Saudi Arabia last December. It was the fourth time he has done so, and seems as unlikely to be binding as the previous three times, particularly thanks to a deal the actor struck shortly beforehand to license an AI version of his voice.

US Customs and Border Protection is paying General Dynamics to create prototype "quantum sensors," to be used with an AI database to detect fentanyl and other narcotics. United States Customs and Border Protection is paying General Dynamics to create a prototype of "quantum sensors" alongside a "database with artificial intelligence " designed "to detect illicit objects and substances (such as fentanyl) in vehicles, containers, and other devices," according to a contract justification published in a federal register last week. "This database and sensor project will integrate advanced quantum and classical sensing technologies with Artificial Intelligence and ultimately deploy proven concepts and end products anywhere in the CBP environment," the justification document reads. "Under this requirement, CBP will take additional steps to enhance its ability to detect, and thus, significantly reduce the harms of illicit contraband entering the United States of America, thus bolstering national security." The document redacts the name of the company developing the prototype; however, contract details included in the federal register entry reveal that the justification is for a $2.4 million General Dynamics contract that has been public since December 2025.

Implicit discourse relation recognition involves determining relationships that hold between spans of text that are not linked by an explicit discourse connective. In recent years, the pre-train, prompt, and predict paradigm has emerged as a promising approach for tackling this task. However, previous work solely relied on manual verbalizers for implicit discourse relation recognition, which suffer from issues of ambiguity and even incorrectness. To overcome these limitations, we leverage the prototypes that capture certain class-level semantic features and the hierarchical label structure for different classes as the verbalizer. We show that our method improves on competitive baselines. Besides, our proposed approach can be extended to enable zero-shot cross-lingual learning, facilitating the recognition of discourse relations in languages with scarce resources. These advancement validate the practicality and versatility of our approach in addressing the issues of implicit discourse relation recognition across different languages.

In this work, we address the inherent ambiguity in Implicit Discourse Relation Recognition (IDRR) by introducing a novel multi-task classification model capable of learning both multi-label and single-label representations of discourse relations. Leveraging the DiscoGeM corpus, we train and evaluate our model on both multi-label and traditional single-label classification tasks. To the best of our knowledge, our work presents the first truly multi-label classifier in IDRR, establishing a benchmark for multi-label classification and achieving SOTA results in single-label classification on DiscoGeM. Additionally, we evaluate our model on the PDTB 3.0 corpus for single-label classification without any prior exposure to its data. While the performance is below the current SOTA, our model demonstrates promising results indicating potential for effective transfer learning across both corpora.

The first attempts to regulate artificial intelligence programs that play a hidden role in hiring, housing and medical decisions for millions of Americans are facing pressure from all sides and floundering in statehouses nationwide. Only one of seven bills aimed at preventing AI's penchant to discriminate when making consequential decisions -- including who gets hired, money for a home or medical care -- has passed. Colorado Gov. Jared Polis hesitantly signed the bill on Friday. Colorado's bill and those that faltered in Washington, Connecticut and elsewhere faced battles on many fronts, including between civil rights groups and the tech industry, and lawmakers wary of wading into a technology few yet understand and governors worried about being the odd-state-out and spooking AI startups. Polis signed Colorado's bill "with reservations," saying in an statement he was wary of regulations dousing AI innovation.

Today's businesses are drowning in the volumes of data they create and collect daily. Many data-related jobs are too complex or time-consuming, which is why organizations are turning to artificial intelligence (AI) and its branches of machine learning (ML) and natural language processing (NLP). Most commonly, the ML models that steer those tasks rely on deep learning: neural networks trained with large data sets--sometimes billions of data points. While these models are proving more adept than people at conquering the data mountain, the result is high computational cost and a growing data center footprint. But what if, instead of using processing-heavy neural networks, the AI could be trained to "learn" the same way the human brain does?

Then, suddenly, both technology and businesses took a different course. Chris Monroe, co-founder of and chief scientist at quantum computing company IonQ, offers a simple explanation for the abrupt change in interest. "Supercomputers failed to catch on because, although they bring the promise of speed and ability to process large computational problems, they come with a significant physical footprint [and] energy/cooling requirements," he notes. "When it comes to mainstream adoption, supercomputers never hit the right balance of affordability, size, access, and value-add enterprise use cases." Supercomputers have traditionally been defined by the fact that they bring together a collection of parallel hardware providing a very high computational throughput and rapid interconnections.

Because of the exponential growth of text data, enterprises need to work shifting from numeric towards text information. Making sense of text information is becoming a key asset for businesses. Take an insurance company for instance: its whole business is dependent on text data since all its products are defined verbosely. All customer interactions happen in natural language. At the moment, the only way to deal with this mass of textual information is to use a human understanding of language.

Natural language understanding (NLU) is a subset of natural language processing (NLP), a technology that enables computers to extract information from human speech or text. While NLP can analyse the structure of a set of words, NLU aims to provide insight into what those words actually mean. Of course, doing this well is difficult. There are problems with slang, irony, different syntax and, especially in spoken language, fragmentary phrasing. In fact, it is known as an "AI-hard" problem – one which, if solved by artificial intelligence, would put computers on the way to being as intelligent as people.

With increases in the complexity of information that must be communicated either by or to computers comes a corresponding need to find ways to communicate that information simply and effectively. It makes little sense to force the burden of communication on a single medium, restricted to just one of spoken or written text, gestures, diagrams, or graphical animation, when in many situations information is only communicated effectively through combinations of media. In response to requests for directions, respondents often choose to provide both a sketch map (for visual indications of relative distance, spatial relationships, etc.) as well as verbal guidance as to landmarks to attend to, obstacles to watch out for, opportunities to take, etc. Instructors training a subject in a new task often choose to present the task in at least two ways: they demonstrate what motions the trainee is supposed to carry out, using direct training, film or graphic media, and they convey what intentional actions those motions are meant to represent, through naturallanguage text or speech. Graphic media (diagrams and animation) can provide a way of visualizing significant patterns in situations (cf. the current interest in Scientific Visualization), while natural-language text (either spoken or written) can provide needed information on what the patterns may mean, why they may have developed, or what may be done to deal with them. Naturallanguage narration is necessary to convey the meaning and significance of such visualizations.)