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"The platform's automated tools may have mistaken the visuals of the burning building for 9/11 footage, according to Vagelis Papalexakis, an assistant professor of computer science and engineering at the University of California, Riverside who studies machine learning used in similar systems."

"When Fry returned to London, she realised how mathematicians, computer engineers and physicists are so used to working on abstract problems that they rarely stop to consider the ethics of how their work might be used"

"Princeton computer science professor Arvind Narayanan calls Zoom a "privacy disaster," filled with "creepy" features that send tracking data to Facebook even if you don't have a Facebook account and tell meeting hosts if attendees aren't paying attention. Zoom's privacy policy allows it to use what it calls "customer content" for advertising purposes."

"Apart from biases in the training databases, it's hard to know how well face-recognition systems actually perform in the real world, in spite of recent gains. Anil Jain, a professor of computer science at Michigan State University who has worked on face recognition for more than thirty years, told me, "Most of the testing on the private venders' products is done in a laboratory environment under controlled settings. In real practice, you're walking around in the streets of New York. It's a cold winter day, you have a scarf around your face, a cap, maybe your coat is pulled up so your chin is partially hidden, the illumination may not be the most favorable, and the camera isn't capturing a frontal view.""

"Instead, the new work shows our brains rely on the kind of maths that an algorithm might use to perform the task. In fact, Tsao and her colleague, Steven Le Chang, stumbled on their discovery while working on computer vision. The pair had initially set themselves the challenge of coming up with a way of reliably converting facial images into a numerical representation."

"But what is pitched as a more convenient way of looking up information online has prompted scrutiny over how and where these chatbots select the information they provide. Looking into the sort of evidence that large language models (LLMs, the engines on which chatbots are built) find most convincing, three computer science researchers from the University of California, Berkeley, found current chatbots overrely on the superficial relevance of information. They tend to prioritise text that includes pertinent technical language or is stuffed with related keywords, while ignoring other features we would usually use to assess trustworthiness, such as the inclusion of scientific references or objective language free of personal bias."

"The research, conducted at Vision and AI Lab of the Department of Computational and Data Sciences, offers a novel approach to mitigating bias in popular image-generative models without the need for additional data or model retraining."

"This is a broader pattern we're seeing across other fields where LLMs are being deployed. Whether it's coding, writing, design, law, or medicine, the most effective AI users are people who already have deep domain expertise. Expertise isn't obsolete; it's more important than ever-because the value isn't just in producing outputs quickly. It's in being able to vet, steer, and improve those outputs. The future of computer science education isn't about teaching less. It's about teaching differently. We still need students who can understand how software works at a fundamental level. But we also need to train them to collaborate with AI-to become fluent in prompting, reviewing, debugging, and refining AI-generated outputs. Mastering this hybrid skillset will be critical not just for engineers, but for anyone hoping to thrive in a world where knowledge work is increasingly AI-augmented. Practically speaking, AI could dramatically lower the barrier to entry for students. When I was in high school, it would take months (if not years) of training in CS before you could create a game or app that was genuinely cool to people that aren't inherently curious and nerdy."

"It is the first time scientists have created sentences from brain activity linked to handwriting and paves the way for more sophisticated devices to help paralysed people communicate faster and more clearly. The man, known as T5, who is in his 60s and lost practically all movement below his neck after a spinal cord injury in 2007, was able to write 18 words a minute when connected to the system. On individual letters, his "mindwriting" was more than 94% accurate."

"Imagine having a digital twin that gets ill, and can be experimented on to identify the best possible treatment, without you having to go near a pill or a surgeon's knife. Scientists believe that within five to 10 years, "in silico" trials - in which hundreds of virtual organs are used to assess the safety and efficacy of drugs - could become routine, while patient-specific organ models could be used to personalise treatment and avoid medical complications. Digital twins are computational models of physical objects or processes, updated using data from their real-world counterparts. Within medicine, this means combining vast amounts of data about the workings of genes, proteins, cells and whole-body systems with patients' personal data to create virtual models of their organs - and eventually, potentially their entire body"

"AI tool GNoME finds 2.2 million new crystals, including 380,000 stable materials that could power future technologies Modern technologies from computer chips and batteries to solar panels rely on inorganic crystals. To enable new technologies, crystals must be stable otherwise they can decompose, and behind each new, stable crystal can be months of painstaking experimentation. Today, in a paper published in Nature, we share the discovery of 2.2 million new crystals - equivalent to nearly 800 years' worth of knowledge. We introduce Graph Networks for Materials Exploration (GNoME), our new deep learning tool that dramatically increases the speed and efficiency of discovery by predicting the stability of new materials."

"Researchers showed detectors can be defeated by inserting inputs called adversarial examples into every video frame. The adversarial examples are slightly manipulated inputs which cause artificial intelligence systems such as machine learning models to make a mistake. In addition, the team showed that the attack still works after videos are compressed."