AI Is Getting Better at Science. Demis Hassabis founded DeepMind to "solve intelligence" and then use that to "solve everything else." Sam Altman promised that "the gains to quality of life from AI driving faster scientific progress will be enormous." Dario Amodei of Anthropic predicted that as soon as 2026, AI progress could produce a "country of geniuses in a data center." Of all the foundational myths driving the AI boom, the hope that AI might help humanity understand the universe is among the most enduring. FrontierScience, a new benchmark published Tuesday by OpenAI, suggests that AI models are advancing toward that goal--and highlights the difficulty of testing models' capabilities as they become ever more competitive with human scientists.

Abstract--Training effective artificial intelligence models for telecommunications is challenging due to the scarcity of deployment-specific data. Real data collection is expensive, and available datasets often fail to capture the unique operational conditions and contextual variability of the network environment. Digital twinning provides a potential solution to this problem, as simulators tailored to the current network deployment can generate site-specific data to augment the available training datasets. However, there is a need to develop solutions to bridge the inherent simulation-to-reality (sim-to-real) gap between synthetic and real-world data. This paper reviews recent advances on two complementary strategies: 1) the calibration of digital twins (DTs) through real-world measurements, and 2) the use of sim-to-real gap-aware training strategies to robustly handle residual discrepancies between digital twin-generated and real data. For the latter, we evaluate two conceptually distinct methods that model the sim-to-real gap either at the level of the environment via Bayesian learning or at the level of the training loss via prediction-powered inference. Driven by the continued growth of computing resources and training datasets, artificial intelligence (AI) research is widely considered to be in the scaling era, which is focused on the development of general-purpose models that exhibit emergent capabilities. While this trend has yielded impressive results for many tasks, particularly in the domain of language modeling, it poses unique challenges when applied to engineering domains such as telecommunication networks.

Health Medicine Stinky'rotten egg' gas could fight nail infections Don't worry, scientists are working on the odor. Breakthroughs, discoveries, and DIY tips sent every weekday. If you have ever let a container of hardboiled eggs spoil or visited a volcano that is spewing lava and gas, you've likely taken a whiff of hydrogen sulfide. This colorless and flammable gas has a uniquely unpleasant rotten egg smell. However that nasty smell (and the gas it belongs to) could have a new use treating pesky infections.

ChatGPT's Horny Era Could Be Its Stickiest Yet OpenAI will soon let adults create erotic content in ChatGPT. Experts say that could lead to "emotional commodification," or horniness as a revenue stream. In May of 2024, while I was combing through OpenAI's "Model Spec" laying out how ChatGPT should act, one comment buried in the document struck me as peculiar. It said OpenAI was "exploring" how to let adult ChatGPT users generate content with mature themes such as "erotica, extreme gore, slurs, and unsolicited profanity." Seems like the exploration phase is over.

Autonomous driving systems (ADS) have been an active area of research, with the potential to deliver significant benefits to society. However, before large-scale deployment on public roads, extensive testing is necessary to validate their functionality and safety under diverse driving conditions. Therefore, different testing approaches are required, and achieving effective and efficient testing of ADS remains an open challenge. Recently, generative AI has emerged as a powerful tool across many domains, and it is increasingly being applied to ADS testing due to its ability to interpret context, reason about complex tasks, and generate diverse outputs. To gain a deeper understanding of its role in ADS testing, we systematically analyzed 91 relevant studies and synthesized their findings into six major application categories, primarily centered on scenario-based testing of ADS. We also reviewed their effectiveness and compiled a wide range of datasets, simulators, ADS, metrics, and benchmarks used for evaluation, while identifying 27 limitations. This survey provides an overview and practical insights into the use of generative AI for testing ADS, highlights existing challenges, and outlines directions for future research in this rapidly evolving field.

Oral assessments, more security checks and speedier marking are all on the cards as generative artificial intelligence (AI) could transform exams for the next generation of students. As the 2025 exam season drew to a close with GCSE students picking up their results on Thursday, after mostly sitting traditional pen and paper exams, AI is already changing the landscape. Exam preparation is undergoing a revolution, with students increasingly creating personal AI tutors, available around the clock to generate learning materials to suit individual needs that potentially lead to better results. "Using AI can give a student a much better understanding of a subject because they can ask those questions they wouldn't ask in class, or at odd hours, without being judged," said Dr Andrew Rogoyski of the Surrey Institute for People-Centred AI. "It really took off this summer," said Sandra Leaton Gray, a professor of education futures at University College London's Institute of Education. "So they're able to talk to it about the marking frameworks that are in use and upload those, and then they're able to do sample answers on their own. And then they're able to say to the AI: 'How would you improve the answer?' It's like having a tireless tutor."

An AI-powered robot was able to remove a gall bladder from a dead pig in what researchers claim is the first realistic surgery by a machine with almost no human intervention. The robot is powered by a two-tier AI system trained on 17 hours of video encompassing 16,000 motions made in operations by human surgeons. When put to work, the first layer of the AI system watches video from an endoscope monitoring the surgery and issues plain-language instructions, such as "clip the second duct", while the second AI layer turns each instruction into three-dimensional tool motions. In all, the gall bladder surgery required 17 separate tasks. The robotic system performed the operation eight times, achieving 100 per cent success in all of the tasks.

An artificial intelligence model trained on the medical data of 57 million people who have used the National Health Service in England could one day assist doctors in predicting disease or forecast hospitalisation rates, its creators have claimed. However, other researchers say there are still significant privacy and data protection concerns around such large-scale use of health data, while even the AI's architects say they can't guarantee that it won't inadvertently reveal sensitive patient data. The model, called Foresight, was first developed in 2023. That initial version used OpenAI's GPT-3, the large language model (LLM) behind the first version of ChatGPT, and trained on 1.5 million real patient records from two London hospitals. Now, Chris Tomlinson at University College London and his colleagues have scaled up Foresight to create what they say is the world's first "national-scale generative AI model of health data" and the largest of its kind.

Google's AI "co-scientist" is based on the firm's Gemini large language models Google has unveiled an experimental artificial intelligence system that "uses advanced reasoning to help scientists synthesize vast amounts of literature, generate novel hypotheses, and suggest detailed research plans", according to its press release. "The idea with [the] 'AI co-scientist' is to give scientists superpowers," says Alan Karthikesalingam at Google. The tool, which doesn't have an official name yet, builds on Google's Gemini large language models. When a researcher asks a question or specifies a goal – to find a new drug, say – the tool comes up with initial ideas within 15 minutes. Several Gemini agents then "debate" these hypotheses with each other, ranking them and improving them over the following hours and days, says Vivek Natarajan at Google. During this process, the agents can search the scientific literature, access databases and use tools such as Google's AlphaFold system for predicting the structure of proteins.

Claire chatted to Christos Bergeles from King's College London about micro-surgical robots to deliver therapies deep inside the body. Christos Bergeles received his PhD in Robotics from ETH Zurich in Switzerland in 2011. As a Professor at King's College London, he directs the "Robotics and Vision in Medicine Lab" whose mission is to develop micro-surgical robots that deliver regenerative therapies deep inside the human body. He holds funding for the development of instrumentation that delivers stem cells to diseased retinal layers in the eye. He and his team are very active in public engagement and patient involvement activities, such as New Scientist Live and the Royal Society Summer Science Exhibition.