Simulation agents are essential for designing and testing systems that interact with humans, such as autonomous vehicles (AVs). These agents serve various purposes, from benchmarking AV performance to stress-testing system limits, but all applications share one key requirement: reliability. To enable systematic experimentation, a simulation agent must behave as intended. It should minimize actions that may lead to undesired outcomes, such as collisions, which can distort the signal-to-noise ratio in analyses. As a foundation for reliable sim agents, we propose scaling self-play to thousands of scenarios on the Waymo Open Motion Dataset under semi-realistic limits on human perception and control. Training from scratch on a single GPU, our agents nearly solve the full training set within a day. They generalize effectively to unseen test scenes, achieving a 99.8% goal completion rate with less than 0.8% combined collision and off-road incidents across 10,000 held-out scenarios. Beyond in-distribution generalization, our agents show partial robustness to out-of-distribution scenes and can be fine-tuned in minutes to reach near-perfect performance in those cases. We open-source the pre-trained agents and integrate them with a batched multi-agent simulator. Demonstrations of agent behaviors can be found at https://sites.google.com/view/reliable-sim-agents.

The rivalry between Sam Altman and Elon Musk is entering its Apprentice era. Both men have the ambition to redefine how the modern world works--and both are jockeying for President Donald Trump's blessing to accelerate their plans. Altman's company, OpenAI, as well as Musk's ventures--which include SpaceX, Tesla, and xAI--all depend to some degree on federal dollars, permits, and regulatory support. The president could influence whether OpenAI or xAI produces the next major AI breakthrough, whether Musk can succeed in sending a human to Mars, and whether Altman's big bet on nuclear energy, and fusion reactors in particular, pans out. Understanding the competition between these two men helps illuminate Trump's particular style of governing--one defined by patronage and dealmaking.

Two cross-party committees of MPs have urged the government to prioritise ensuring that creators are fairly remunerated for their creative work over making it easy to train artificial intelligence models. The MPs argued there needed to be more transparency around the vast amounts of data used to train generative AI models, and urged the government not to press ahead with plans to require creators to opt out of having their data used. The chair of the culture, media and sport committee, Caroline Dinenage, said there had been a "groundswell of concern from across the creative industries" in response to the proposals, which "illustrates the scale of the threat artists face from artificial intelligence pilfering the fruits of their hard-earned success without permission". She added that making creative works "fair game unless creators say so" was akin to "burglars being allowed into your house unless there's a big sign on your front door expressly telling them that thievery isn't allowed". The letter warned that without this, "the biggest impact would be felt by the long tail of creators and journalists already operating under financial constraints".

Stretchy computers on threads that can be stitched into clothes could be used to record whole-body data that most medical sensors can't pick up. Wearable technologies, such as smartwatches, monitor signals from the body like heart rate or temperature, but typically only from a single spot. This can give an incomplete picture of how the body is functioning. Now, Yoel Fink at the Massachusetts Institute of Technology and his colleagues have developed a computer that can be stitched into clothes, made from chips that are connected in a thread of copper and elastic fibre. The thread has 256 kilobytes of on-board memory, around that of a simple calculator, as well as sensors that can detect temperature, heart rate and body movements.

When a phalanx of the top Silicon Valley executives--Mark Zuckerberg, Jeff Bezos, Elon Musk, and Google's Sundar Pichai--aligned behind President Trump during the Inauguration in January, many observers saw an allegiance based on corporate interests. The ultra-wealthy C.E.O.s were turning out to support a fellow-magnate, hoping perhaps for an era of deregulation, tax breaks, and anti-"woke" cultural shifts. The historian Janis Mimura saw something more ominous: a new, proactive union of industry and governmental power, wherein the state would drive aggressive industrial policy at the expense of liberal norms. In the second Trump Administration, a class of Silicon Valley leaders was insinuating itself into politics in a way that recalled one of Mimura's primary subjects of study: the élite bureaucrats who seized political power and drove Japan into the Second World War. "These are experts with a technological mind-set and background, often engineers, who now have a special role in the government," Mimura told me.

Fox News contributor Dr. Marc Siegel joins'Fox News Live' to discuss the FDA approving a new Alzheimer treatment drug and the FDA banning bromide vegetable oils. "Normal" levels of vitamin B12 may not be enough to ward off dementia, new research finds. Researchers at University of California San Francisco studied 231 healthy older adults (averaging 71 years of age) who did not have dementia or mild cognitive impairment. Blood tests showed that their B12 levels averaged 414.8 pmol/L, while the recommended minimum level in the U.S. is just 148 pmol/L. Participants who had lower B12 levels were found to have "slower cognitive and visual processing speeds" when taking tests, which is linked to "subtle cognitive decline," according to a UCSF press release.

Apple has promised to fix a bug in its iPhone automatic dictation tool after some users reported it had suggested to them "Trump" when they said the word "racist". The glitch was first highlighted in a viral post on TikTok, when the speech-to-text tool sometimes briefly flashed up the word "Trump" when they said "racist", and was later repeated by others on social media. "We are aware of an issue with the speech recognition model that powers dictation and we are rolling out a fix," an Apple spokesperson said. The company blamed the bug on its tool displaying words that have "phonetic overlap" before the "intended word" is identified, which in this case included words with the "r" consonant. However, the glitch caused outrage among some conservative commentators in the US, who have long accused big tech companies of political bias against those on the right.

The White House today announced the name of the acting administrator of the Department of Government Efficiency: Amy Gleason, the US government's problem solver in the early days of the data-starved response to the Covid pandemic and a seasoned worker in the health space. The White House named Gleason after it argued in court that Elon Musk is not really the head of DOGE, and faced pressure from a federal judge to say who is. How long Gleason has been the acting administrator, and if Musk was an unofficial one before today's announcement, is unclear. This is Gleason's second time working in US Digital Services, now turned DOGE. In her first tour, which started in 2018 and carried through the frenzied and chaotic pandemic response, she pushed the bounds of existing bureaucracy to meet the crisis' demand.

Critical heat flux is a key quantity in boiling system modeling due to its impact on heat transfer and component temperature and performance. This study investigates the development and validation of an uncertainty-aware hybrid modeling approach that combines machine learning with physics-based models in the prediction of critical heat flux in nuclear reactors for cases of dryout. Two empirical correlations, Biasi and Bowring, were employed with three machine learning uncertainty quantification techniques: deep neural network ensembles, Bayesian neural networks, and deep Gaussian processes. A pure machine learning model without a base model served as a baseline for comparison. This study examines the performance and uncertainty of the models under both plentiful and limited training data scenarios using parity plots, uncertainty distributions, and calibration curves. The results indicate that the Biasi hybrid deep neural network ensemble achieved the most favorable performance (with a mean absolute relative error of 1.846% and stable uncertainty estimates), particularly in the plentiful data scenario. The Bayesian neural network models showed slightly higher error and uncertainty but superior calibration. By contrast, deep Gaussian process models underperformed by most metrics. All hybrid models outperformed pure machine learning configurations, demonstrating resistance against data scarcity.

Accurate prediction of structural failure modes under seismic excitations is essential for seismic risk and resilience assessment. Traditional simulation-based approaches often result in imbalanced datasets dominated by non-failure or frequently observed failure scenarios, limiting the effectiveness in machine learning-based prediction. To address this challenge, this study proposes a framework for constructing balanced datasets that include distinct failure modes. The framework consists of three key steps. First, critical ground motion features (GMFs) are identified to effectively represent ground motion time histories. Second, an adaptive algorithm is employed to estimate the probability densities of various failure domains in the space of critical GMFs and structural parameters. Third, samples generated from these probability densities are transformed into ground motion time histories by using a scaling factor optimization process. A balanced dataset is constructed by performing nonlinear response history analyses on structural systems with parameters matching the generated samples, subjected to corresponding transformed ground motion time histories. Deep neural network models are trained on balanced and imbalanced datasets to highlight the importance of dataset balancing. To further evaluate the framework's applicability, numerical investigations are conducted using two different structural models subjected to recorded and synthetic ground motions. The results demonstrate the framework's robustness and effectiveness in addressing dataset imbalance and improving machine learning performance in seismic failure mode prediction.