Researchers at the University of Reading fooled their own professors by secretly submitting AI-generated exam answers that went undetected and got better grades than real students. The project created fake student identities to submit unedited answers generated by ChatGPT-4 in take-home online assessments for undergraduate courses. The university's markers – who were not told about the project – flagged only one of the 33 entries, with the remaining AI answers receiving higher than average grades than the students. The authors said their findings showed that AI processors such as ChatGPT were now passing the "Turing test" – named after the computing pioneer Alan Turing – of being able to pass undetected by experienced judges. Billed as "the largest and most robust blind study of its kind" to investigate if human educators could detect AI-generated responses, the authors warned that it had major implications for how universities assess students. "Our research shows it is of international importance to understand how AI will affect the integrity of educational assessments," said Dr Peter Scarfe, one of the authors and an associate professor at Reading's school of psychology and clinical language sciences.

FedEx unveiled a two-armed robot called DexR this week that's designed to automate one of the trickiest tasks facing the company's human employees--loading a van with packages. The new robot aims to use artificial intelligence to stack rows of differently sized boxes inside a delivery van as efficiently as possible, attempting to maximize how many will fit. That task is far from easy for a machine. "Packages come in different sizes, shapes, weights, and packaging materials, and they come randomized," says Rebecca Yeung, vice president of operations and advanced technology at FedEx. The robot uses cameras and lidar sensors to perceive the packages and must then plan how to configure the available boxes to make a neat wall, place them snugly without crushing anything, and react appropriately if any packages slip.

The Lords Justice and Home Affairs Committee warned that the lack of oversight meant "users are in effect making it up as they go along". The cross-party group said AI had the potential to improve people's lives but could have "serious implications" for human rights and civil liberties in the justice system. "Algorithms are being used to improve crime detection, aid the security categorisation of prisoners, streamline entry clearance processes at our borders and generate new insights that feed into the entire criminal justice pipeline," the peers said. Scrutiny was not happening to ensure new tools were "safe, necessary, proportionate and effective". "Instead, we uncovered a landscape, a new Wild West, in which new technologies are developing at a pace that public awareness, government and legislation have not kept up with."

The potential benefits of artificial intelligence to health care are enormous, but these emerging technologies also raise a number of ethical and legal considerations. These questions are particularly relevant to a subset of AI known as computer vision-based ambient intelligence, which uses a video camera or sensors to monitor activity in a physical space, such as a patient room or hospital hallway. The technology analyzes -- in real time -- the resulting video data, which can appear as standard footage, depth or thermal data captured as silhouette-like moving images, or in other forms. Such tools can potentially help nurses ensure that patients in an intensive care unit are enhancing their recovery by moving around. Computer vision technology also can be used to document whether nurses and doctors are following proper hand-washing protocol, considered the first line of defense against hospital-acquired infections.

Artificial intelligence and automation are responsible for a growing number of decisions by pubic authorities in areas like criminal justice, security and policing and public administration, despite having proven flaws and biases. Facial recognition systems are entering public spaces without any clear accountability or oversight. Lawyers must play a greater role in ensuring the safety and accountability of advanced data and analytics technologies, says Karen Yeung at the University of Birmingham. The dream of artificial intelligence stretches back seven decades, to a seminal paper by Alan Turing. But only recently has AI been commercialized and industrialized at scale, weaving its way into every nook and cranny of our lives.

Hospital intensive care units can be frightening places for patients. In the US, the ICU has a higher mortality rate than any other hospital unit--between 8% and 19%, totaling roughly 500,000 deaths a year. Those who do not die may suffer in other ways, such as long-term physical and mental impairment. For nurses, working in one can easily lead to burnout because it takes so much physical and emotional stamina to administer round-the-clock care. Now a new paper, published in Nature Digital Medicine, shows how AI might be able to help.

Arthritis is not just an ailment of old age--it can affect children too, causing lifelong pain and disability in its most severe forms. Fortunately, some kids grow out of it. Knowing which patients will develop milder forms of disease could spare them unnecessary treatment and potential medication side effects but currently doctors have no way of predicting disease course or severity. That could now change thanks to a machine learning tool developed by Quaid Morris, a professor of computer science at the Donnelly Centre for Cellular and Biomolecular Research at the University of Toronto, Dr. Rae Yeung, Professor of Paediatrics, Immunology and Medical Science at the University of Toronto, and their recently-graduated, co-supervised student Simon Eng. Morris is also faculty in the Vector Institute for Artificial Intelligence and is an inaugural AI Chair by the Canadian Institute for Advancement of Research.

Probabilistic relational PCA (PRPCA) can learn a projection matrix to perform dimensionality reduction for relational data. However, the results learned by PRPCA lack interpretability because each principal component is a linear combination of all the original variables. In this paper, we propose a novel model, called sparse probabilistic relational projection (SPRP), to learn a sparse projection matrix for relational dimensionality reduction. The sparsity in SPRP is achieved by imposing on the projection matrix a sparsity-inducing prior such as the Laplace prior or Jeffreys prior. We propose an expectation-maximization (EM) algorithm to learn the parameters of SPRP. Compared with PRPCA, the sparsity in SPRP not only makes the results more interpretable but also makes the projection operation much more efficient without compromising its accuracy. All these are verified by experiments conducted on several real applications.