Simulating stochastic differential equations (SDEs) in bounded domains, presents significant computational challenges due to particle exit phenomena, which requires accurate modeling of interior stochastic dynamics and boundary interactions. Despite the success of machine learning-based methods in learning SDEs, existing learning methods are not applicable to SDEs in bounded domains because they cannot accurately capture the particle exit dynamics. We present a unified hybrid data-driven approach that combines a conditional diffusion model with an exit prediction neural network to capture both interior stochastic dynamics and boundary exit phenomena. Our ML model consists of two major components: a neural network that learns exit probabilities using binary cross-entropy loss with rigorous convergence guarantees, and a training-free diffusion model that generates state transitions for non-exiting particles using closed-form score functions. The two components are integrated through a probabilistic sampling algorithm that determines particle exit at each time step and generates appropriate state transitions. The performance of the proposed approach is demonstrated via three test cases: a one-dimensional simplified problem for theoretical verification, a two-dimensional advection-diffusion problem in a bounded domain, and a three-dimensional problem of interest to magnetically confined fusion plasmas.

Human intelligence exhibits a remarkable capacity for rapid adaptation and effective problem-solving in novel and unfamiliar contexts. We argue that this profound adaptability is fundamentally linked to the efficient construction and refinement of internal representations of the environment, commonly referred to as world models, and we refer to this adaptation mechanism as world model induction . However, current understanding and evaluation of world models in artificial intelligence (AI) remains narrow, often focusing on static representations learned from training on massive corpora of data, instead of the efficiency and efficacy in learning these representations through interaction and exploration within a novel environment. In this Perspective, we provide a view of world model induction drawing on decades of research in cognitive science on how humans learn and adapt so efficiently; we then call for a new evaluation framework for assessing adaptive world models in AI. Concretely, we propose a new benchmarking paradigm based on suites of carefully designed games with genuine, deep and continually refreshing novelty in the underlying game structures -- we refer to this class of games as novel games . We detail key desiderata for constructing these games and propose appropriate metrics to explicitly challenge and evaluate the agent's ability for rapid world model induction. We hope that this new evaluation framework will inspire future evaluation efforts on world models in AI and provide a crucial step towards developing AI systems capable of human-like rapid adaptation and robust generalization -- a critical component of artificial general intelligence.

Digital twins are models of real-world systems that can simulate their dynamics in response to potential actions. In complex settings, the state and action variables, and available data and knowledge relevant to a system can constantly change, requiring digital twins to continuously update with these changes to remain relevant. Current approaches struggle in this regard, as they require fixed, well-defined modelling environments, and they cannot adapt to novel variables without re-designs, or incorporate new information without re-training. To address this, we frame digital twinning as an in-context learning problem using large language models, enabling seamless updates to the twin at inference time. We develop CALM-DT, a Context-Adaptive Language Model-based Digital Twin that can accurately simulate across diverse state-action spaces using in-context learning alone by utilising fine-tuned encoders for sample retrieval. We empirically demonstrate CALM-DT's competitive performance with existing digital twin approaches, and its unique ability to adapt to changes in its modelling environment without parameter updates.

Acting ICE Director Todd Lyons explains how far-left groups are using'reverse technology' to reveal the identities of federal immigration officers. Far-left organizations could be using artificial intelligence and other technology to reveal the identity of Immigration and Customs Enforcement agents, acting ICE Director Todd Lyons told Fox News Digital in an interview. Lyons' remarks come as Democrats in Congress recently proposed the VISIBLE Act, which would require clear identification of ICE agents and prevent masking of federal immigration authorities in public-facing circumstances. "If legislation passes to try to unmask ICE agents, they are not allowed to wear them, it runs the risk of agitators, different groups, you know, these fringe organizations using reverse technology, AI, to try to dox their families, try to get their identity, their home addresses," Lyons said of the reaction from agents on the ground. "We've heard elected officials say there shouldn't be any rest for ICE agents or their families. "So they're definitely concerned about that.

During his latest trip to Washington, OpenAI's chief executive, Sam Altman, painted a sweeping vision of an AI-dominated future in which entire job categories disappear, presidents follow ChatGPT's recommendations and hostile nations wield artificial intelligence as a weapon of mass destruction, all while positioning his company as the indispensable architect of humanity's technological destiny. Speaking at the Capital Framework for Large Banks conference at the Federal Reserve board of governors, Altman told the crowd that certain job categories would be completely eliminated by AI advancement. "Some areas, again, I think just like totally, totally gone," he said, singling out customer support roles. "That's a category where I just say, you know what, when you call customer support, you're on target and AI, and that's fine." The OpenAI founder described the transformation of customer service as already complete, telling the Federal Reserve vice-chair for supervision, Michelle Bowman: "Now you call one of these things and AI answers. It can do everything that any customer support agent at that company could do. It does not make mistakes. You call once, the thing just happens, it's done."

Madeline Gardner is the youngest American person to get a Hero Arm, the world's first multi-grip bionic arm for children. Kicking a ball or climbing stairs with ease after a leg amputation above the knee is now within reach. Researchers at MIT have developed a bionic knee that redefines mobility for above-the-knee amputees. The result is faster, smoother, and more natural movement. Led by Professor Hugh Herr, the MIT team created a solution that doesn't just mimic motion, it responds to intent.

On Sunday, our thoughtful and reserved president reposted on his Truth Social site a video generated by artificial intelligence that falsely showed former President Obama being arrested and imprisoned. There are those among you who think this is high humor; those among you who who find it as tiresome as it is offensive; and those among you blissfully unaware of the mental morass that is Truth Social. Whatever camp you fall into, the video crosses all demographics by being expected -- just another crazy Trump stunt in a repetitive cycle of division and diversion so frequent it makes Groundhog Day seem fresh. But there are three reasons why this particular video -- not made by the president but amplified to thousands -- is worth noting, and maybe even worth fearing. First, it is flat-out racist. In it, Obama is ripped out of a chair in the Oval Office and forced onto his knees, almost bowing, to a laughing Trump.

The previous Conservative government introduced a plan to examine the bones and teeth of some migrants in order to verify their age. But Labour ministers are thought to be sceptical about the plan because it relied on people being taken to separate facilities and instead wanted a verification system that could be used at the border. Mr Bolt's report noted the safeguarding risk of a child incorrectly assessed to be an adult having to share a room with an adult stranger – as well as an adult incorrectly assessed as a child being placed with other children. The inspector highlighted the case of a male small boat arrival who claimed they were 17, who the Home Office assessed to be 22 due to physical characteristics such as his "deep voice", "fully developed facial structure" and "thick black stubble". He criticised the Home Office's use of "generic physical characteristics" and "failing to take into account the young person's individual circumstances".

Ministers are facing calls for greater transparency about public data that may be shared with the US tech company OpenAI after the government signed a wide-ranging agreement with the 300m ( 222m) company that critics compared to letting a fox into a henhouse. Chi Onwurah, the chair of the House of Commons select committee on science, innovation and technology, warned that Monday's sweeping memorandum of understanding between OpenAI's chief executive, Sam Altman, and the technology secretary, Peter Kyle, was "very thin on detail" and called for guarantees that public data would remain in the UK and clarity about how much of it OpenAI would have access to. The deal paves the way for the Silicon Valley firm behind ChatGPT to explore deploying advanced AI technology in areas including justice, defence and security, and education. It includes OpenAI and the government "partnering to develop safeguards that protect the public and uphold democratic values". Kyle said he wanted Britain to be "front and centre when it comes to developing and deploying AI" and "this can't be achieved without companies like OpenAI".

Officials are to start using artificial intelligence to help estimate the age of asylum seekers who say they are children. Angela Eagle, the immigration minister, said on Tuesday the government would test technology that judges a person's age based on their facial features. It is the latest example of Labour ministers turning to AI to help solve problems with public services without spending significant amounts of money. The decision was announced on the same day that David Bolt, the chief inspector of borders and immigration, published a highly critical report into the haphazard way in which officials estimated the age of new arrivals. Eagle said in a written statement to parliament: "We have concluded that the most cost-effective option to pursue is likely to be facial age estimation, whereby AI technology – trained on millions of images where an individual's age is verifiable – is able to produce an age estimate with a known degree of accuracy for an individual whose age is unknown or disputed.