At seventy-three, the former front man of Talking Heads is still asking questions about what it means to be alive. "When you step onstage, it's a very artificial situation," Byrne said. "To pretend it's not--that isn't being authentic." If you spend enough time wandering around downtown Manhattan, the odds are that you'll eventually encounter the musician David Byrne riding a bicycle. One day this past June, pedalling alongside Byrne from his apartment in Chelsea to the Governors Island ferry, I watched at least a dozen New Yorkers clock his profile, whipping around to squint, softly pinching the arm of their companion and whispering, "Was that . . . By then, Byrne was gone, a tuft of white hair whizzing toward the horizon. Spotting Byrne on two wheels has become a New York City rite of passage, like sussing out the best halal cart in midtown, or dropping something important onto the subway tracks. During the few months that Byrne and I spent together, I never saw him traverse the ...

Think back through your day and consider all the amazing tasks your brain has helped you perform. From brushing your teeth to eating your lunch and reading the words on this page, your thoughts, feelings and actions may appear to be the product of a finely tuned machine. Simply telling someone your name is a small miracle for electrical signals zapping across a 1.3-kilogram lump of jelly. "You're pulling off one of the most complicated and exquisite acts of computation in the universe," says Keith Hengen, a biologist at Washington University in St Louis. Exactly how we achieve this complexity has puzzled philosophers and neuroscientists for centuries, and now it seems precision isn't the answer. Instead it could all come down to the brain's inherent messiness.

Recent advances in neural methods have led to substantial improvement in the quality of Neural Machine Translation (NMT) systems. However, these systems frequently produce translations with inaccurate gender (Stanovsky et al., 2019), which can be traced to bias in training data. Saunders and Byrne (2020) tackle this problem with a handcrafted dataset containing balanced gendered profession words. By using this data to fine-tune an existing NMT model, they show that gender bias can be significantly mitigated, albeit at the expense of translation quality due to catastrophic forgetting. They recover some of the lost quality with modified training objectives or additional models at inference. We find, however, that simply supplementing the handcrafted dataset with a random sample from the base model training corpus is enough to significantly reduce the catastrophic forgetting. We also propose a novel domain-adaptation technique that leverages in-domain data created with the counterfactual data generation techniques proposed by Zmigrod et al. (2019) to further improve accuracy on the WinoMT challenge test set without significant loss in translation quality. We show its effectiveness in NMT systems from English into three morphologically rich languages French, Spanish, and Italian. The relevant dataset and code will be available at Github.

When it first opened in theatres, in the fall of 1984, "Stop Making Sense," directed by Jonathan Demme and starring the rock group Talking Heads, was quickly recognized as one of the finest concert films ever made. Reviewer after reviewer settled on the word "exhilarating" to describe the experience of watching an expanded nine-member iteration of the four-piece group perform sixteen of their best-known songs in an uninterrupted sequence of dynamically staged and photographed musical vignettes. In the pages of this magazine, Pauline Kael praised the film as "close to perfection," and described the Heads front man, David Byrne, as "a stupefying performer." "He's so white he's almost mock-white," Kael wrote, "and so are his jerky, long-necked, mechanical-man movements. He seems fleshless, bloodless; he might almost be a Black man's parody of how a clean-cut white man moves. But Byrne himself is the parodist, and he commands the stage by his hollow-eyed, frosty verve."

As artificial intelligence systems play a bigger role in everyday life, they're changing the world of education, too. I am a literacy educator and researcher, and here are four ways I believe these kinds of systems can be used to help students learn. Teachers are taught to identify the learning goals of all students in a class and adapt instruction for the specific needs of individual students. But with 20 or more students in a classroom, fully customized lessons aren't always realistic. An AI system can observe how a student proceeds through an assigned task, how much time they take and whether they are successful.

Recently, eXplainable AI (XAI) research has focused on counterfactual explanations as post-hoc justifications for AI-system decisions (e.g. a customer refused a loan might be told: If you asked for a loan with a shorter term, it would have been approved). Counterfactuals explain what changes to the input-features of an AI system change the output-decision. However, there is a sub-type of counterfactual, semi-factuals, that have received less attention in AI (though the Cognitive Sciences have studied them extensively). This paper surveys these literatures to summarise historical and recent breakthroughs in this area. It defines key desiderata for semi-factual XAI and reports benchmark tests of historical algorithms (along with a novel, naieve method) to provide a solid basis for future algorithmic developments.

The United States is on the brink of its most consequential transformation since the New Deal. Read more about what it takes to decarbonize the economy, and what stands in the way, here. This story was originally published by Hakai Magazine and is reproduced here as part of our Climate Desk collaboration. Imagination is a powerful thing. Mary Shelley predicted organ transplantation in her novel Frankenstein, published in 1818.

The onus is falling upon AI, Data and Analytics to save the NHS in a post-pandemic world. A dedicated stage at Digital Health Rewired 2023 will showcase the best of AI in action and explore opportunities for data and analytics in patient care. The AI, Data and Analytics stage will bring together data scientists and researchers, clinicians, and healthcare IT professionals to discuss the latest uses of new analytical and predictive tools. Highlights of the programme include Dominic Cushnan, director of AI, imaging and deployment at NHS England, who will ask in his keynote speech whether AI can save the NHS. Cushnan will be joined on the two-day stage by speakers including Dr Nicola Byrne, national data guardian (NDG) for health and adult social care in England.

"We live in an age of Apple and Tesla," says Marcus Byrne of creative agency Thinkerbell in Australia. "Minimal design has taken charge and logos are stripped back to live on mobile devices." He's challenged that ideal by asking: "What would Gaudí do?" – using the Midjourney AI software and Photoshop to create a set of modern gadgets in the Spanish architect's colourful, curvilinear style. "It turns minimal appliances into art-nouveau styled sculptures," he says. Byrne is uncomfortable calling the Gaudí gadgets his work, though.

This blog post is an excerpt from my doctoral dissertation, "'What makes a great story?': Multidisciplinary and international perspectives on digital stories created by youth formerly in foster care in Canada" (York University, 11 Apr. The debate about whether or not to work with Artificial Intelligence (AI) technologies because they are "good," or "bad" is overly simplistic and lacks a critical lens. As history conveys, one can infer that artists and technologists are going to try working with AI despite, or because of these challenges. AI systems like "Dall:E" that translate words and sentences into images, have solidified concerns about representation, specifically of gender and race raised early in the advent of AI-based data visualization (Nicholas, 2022). Nevertheless, I believe that it is necessary for women and non-binary people -- especially racialized women -- to begin to explore these technologies as a way of claiming this space and defining the ethical implications of AI.