The Next Alzheimer's Breakthrough Will Take More Than Just Science At WIRED Health, pioneering Alzheimer's researcher John Hardy outlined the stakes--and next steps--of where treatment is headed next. Alzheimer's research is entering a new phase, as treatments that have taken decades to develop begin to reach patients . But getting those advances to people will depend on more than scientific progress alone, according to pioneering Alzheimer's researcher John Hardy . Speaking at WIRED Health in April, Hardy, chair of the Molecular Biology of Neurological Disease at University College London, said that alongside more effective drugs, better diagnosis and political will were still needed to improve treatment of Alzheimer's disease. "We've got to get better," he said.

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AI models are increasingly prevalent in high-stakes environments, necessitating thorough assessment of their capabilities and risks. Benchmarks are popular for measuring these attributes and for comparing model performance, tracking progress, and identifying weaknesses in foundation and non-foundation models. They can inform model selection for downstream tasks and influence policy initiatives. However, not all benchmarks are the same: their quality depends on their design and usability. In this paper, we develop an assessment framework considering 40 best practices across a benchmark's life cycle and evaluate 25 AI benchmarks against it. We find that there exist large quality differences and that commonly used benchmarks suffer from significant issues. We further find that most benchmarks do not report statistical significance of their results nor can results be easily replicated. To support benchmark developers in aligning with best practices, we provide a checklist for minimum quality assurance based on our assessment. We also develop a living repository of benchmark assessments to support benchmark comparability.

AI models are increasingly prevalent in high-stakes environments, necessitating thorough assessment of their capabilities and risks. Benchmarks are popular for measuring these attributes and for comparing model performance, tracking progress, and identifying weaknesses in foundation and non-foundation models. They can inform model selection for downstream tasks and influence policy initiatives. However, not all benchmarks are the same: their quality depends on their design and usability. In this paper, we develop an assessment framework considering 40 best practices across a benchmark's life cycle and evaluate 25 AI benchmarks against it.

The last time we saw Imperator Furiosa, in the dystopian chase thriller "Mad Max: Fury Road" (2015), she had just returned from the heat of battle, her face streaked with blood, one eye swollen shut, her body so fatigued and battered that she could hardly stand. Furiosa, played by a stupendous Charlize Theron, had spent several days and nights driving an enormous truck, the War Rig, across miles of open desert, withstanding fiery assaults, a lethal sandstorm, and the surly company of a reluctant ally named Max (Tom Hardy). But triumph, at last, was hers: the vile warlord Immortan Joe (Hugh Keays-Byrne) lay dead at her feet, and hundreds of newly liberated desert dwellers were erupting in celebration. Amid the chaos, Furiosa scanned the crowd for Max and caught him slinking away. For a moment, he looked back and gave her an approving nod--then turned and vanished into the throng.

Most events in the world receive at most brief coverage by the news media. Occasionally, however, an event will trigger a media storm, with voluminous and widespread coverage lasting for weeks instead of days. In this work, we develop and apply a pairwise article similarity model, allowing us to identify story clusters in corpora covering local and national online news, and thereby create a comprehensive corpus of media storms over a nearly two year period. Using this corpus, we investigate media storms at a new level of granularity, allowing us to validate claims about storm evolution and topical distribution, and provide empirical support for previously hypothesized patterns of influence of storms on media coverage and intermedia agenda setting.

Children in British schools are using artificial intelligence (AI) to make indecent images of other children, a group of experts on child abuse and technology has warned. They said that a number of schools were reporting for the first time that pupils were using AI-generating technology to create images of children that legally constituted child sexual abuse material. Emma Hardy, UK Safer Internet Centre (UKSIC) director, said the pictures were "terrifyingly" realistic. "The quality of the images that we're seeing is comparable to professional photos taken annually of children in schools up and down the country," said Hardy, who is also the Internet Watch Foundation communications director. "The photo-realistic nature of AI-generated imagery of children means sometimes the children we see are recognisable as victims of previous sexual abuse. "Children must be warned that it can spread across the internet and end up being seen by strangers and sexual predators.

For Sharma, that meant starting from scratch, assembling a team of data scientists and building an AI pipeline. Sharma and his team then created a "smart audience platform" that puts ads touting an artist's latest release in front of listeners who are most likely to engage with that artist. The music industry might not be the first business case that comes to mind for AI and data analytics. Yet AI-based data analytics can have a transformative impact in any industry and across a wide range of use cases. Most organizations today are drowning in data.

The importance of data to modern marketers is undeniable, but most data that exists today is unusable. That's according to dotmailer, that found the challenge for marketers now is not data access, but determining which data is useful to analyse. The dotmailer ebook, Your pathway to smarter email automation, suggests most of it isn't. The report found 80 per cent of the exponentially increasing amount of data is "unstructured". And while the unstructured data can provide insights it is unable to be leveraged at scale because it cannot be catalogued or stored effectively.

A woman appears to walk down a hallway, then melts into a moonlit sky. A face appears in the dark, contorts into shapes. The animation is based on a 1929 film version of Edgar Allen Poe's story, but its inky and strange visuals are the result of something altogether more modern: machine learning. Each moment of Ridler's film has been generated by artificial intelligence. The artist took stills from the first four minutes of the 1929 movie, then drew them with ink on paper.