People tend to use language to mention surprising properties of events: for example, when a banana is blue, we are more likely to mention color than when it is yellow. This fact is taken to suggest that yellowness is somehow a typical feature of bananas, and blueness is exceptional. Similar to how a yellow color is typical of bananas, there may also be genders that are typical of occupations. In this work, we explore this question using information theoretic techniques coupled with corpus statistic analysis. In two distinct large corpora, we do not find strong evidence that occupations and gender display the same patterns of mentioning as do bananas and color. Instead, we find that gender mentioning is correlated with femaleness of occupation in particular, suggesting perhaps that woman-dominated occupations are seen as somehow ``more gendered'' than male-dominated ones, and thereby they encourage more gender mentioning overall.

Studying low-likelihood high-impact extreme weather events in a warming world is a significant and challenging task for current ensemble forecasting systems. While these systems presently use up to 100 members, larger ensembles could enrich the sampling of internal variability. They may capture the long tails associated with climate hazards better than traditional ensemble sizes. Due to computational constraints, it is infeasible to generate huge ensembles (comprised of 1,000-10,000 members) with traditional, physics-based numerical models. In this two-part paper, we replace traditional numerical simulations with machine learning (ML) to generate hindcasts of huge ensembles. In Part I, we construct an ensemble weather forecasting system based on Spherical Fourier Neural Operators (SFNO), and we discuss important design decisions for constructing such an ensemble. The ensemble represents model uncertainty through perturbed-parameter techniques, and it represents initial condition uncertainty through bred vectors, which sample the fastest growing modes of the forecast. Using the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) as a baseline, we develop an evaluation pipeline composed of mean, spectral, and extreme diagnostics. Using large-scale, distributed SFNOs with 1.1 billion learned parameters, we achieve calibrated probabilistic forecasts. As the trajectories of the individual members diverge, the ML ensemble mean spectra degrade with lead time, consistent with physical expectations. However, the individual ensemble members' spectra stay constant with lead time. Therefore, these members simulate realistic weather states, and the ML ensemble thus passes a crucial spectral test in the literature. The IFS and ML ensembles have similar Extreme Forecast Indices, and we show that the ML extreme weather forecasts are reliable and discriminating.

Autonomous lander missions on extraterrestrial bodies need to sample granular materials while coping with domain shifts, even when sampling strategies are extensively tuned on Earth. To tackle this challenge, this paper studies the few-shot scooping problem and proposes a vision-based adaptive scooping strategy that uses the deep kernel Gaussian process method trained with a novel meta-training strategy to learn online from very limited experience on out-of-distribution target terrains. Our Deep Kernel Calibration with Maximal Deployment Gaps (kCMD) strategy explicitly trains a deep kernel model to adapt to large domain shifts by creating simulated maximal deployment gaps from an offline training dataset and training models to overcome these deployment gaps during training. Employed in a Bayesian Optimization sequential decision-making framework, the proposed method allows the robot to perform high-quality scooping actions on out-of-distribution terrains after a few attempts, significantly outperforming non-adaptive methods proposed in the excavation literature as well as other state-of-the-art meta-learning methods. The proposed method also demonstrates zero-shot transfer capability, successfully adapting to the NASA OWLAT platform, which serves as a state-of-the-art simulator for potential future planetary missions. These results demonstrate the potential of training deep models with simulated deployment gaps for more generalizable meta-learning in high-capacity models. Furthermore, they highlight the promise of our method in autonomous lander sampling missions by enabling landers to overcome the deployment gap between Earth and extraterrestrial bodies.

For simple classification tasks, we show that users can benefit from the advantages of using small, local, generative language models instead of large commercial models without a trade-off in performance or introducing extra labelling costs. These advantages, including those around privacy, availability, cost, and explainability, are important both in commercial applications and in the broader democratisation of AI. Through experiments on 17 sentence classification tasks (2-4 classes), we show that penalised logistic regression on the embeddings from a small LLM equals (and usually betters) the performance of a large LLM in the "tens-of-shot" regime. This requires no more labelled instances than are needed to validate the performance of the large LLM. Finally, we extract stable and sensible explanations for classification decisions.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The U.S. handed over its last military base in Niger -- one of two crucial hubs for American counterterrorism operations in the country -- to local authorities, the U.S. Department of Defense and Niger's Ministry of Defense announced in a joint statement on Monday. The handing over of Airbase 201 in the city of Agadez came after the U.S. troops withdrew earlier this month from Airbase 101, a small drone base in Niger's capital of Niamey. U.S. troops have until Sept. 15 to leave the Sahel country following an agreement with Nigerien authorities.

Former President Trump told FOX Business' Maria Bartiromo last week that Meta CEO Mark Zuckerberg called him to apologize after Facebook wrongly mislabeled a now-viral photo of the former president. The photo showing Trump raising a fist after a July 13 assassination attempt at his campaign rally in Butler, Pennsylvania, sliced his ear was initially labeled as misinformation on the social media site. "So, Mark Zuckerberg called me. First of all, he called me two times. He called me after the event and he said that was really amazing," Trump told Bartiromo in a "Mornings with Maria" interview that aired Thursday.

Five secretaries of state plan to send a letter to Elon Musk calling on the billionaire owner of X to make changes to the social media platform's Grok AI chatbot after it gave users misinformation about Kamala Harris appearing on the 2024 White House ballot in certain states. Grok told users that the ballots were "locked and loaded" and that "the ballot deadline has passed for several states". "So, if you're planning to run for president in any of these states, you might want to check if you've already missed the boat. But hey, there's always 2028, right?" the chatbot told users. But the ballot deadlines in the nine states listed by Grok – Alabama, Indiana, Michigan, Minnesota, New Mexico, Ohio, Pennsylvania, Texas and Washington – have not passed.

Four days after the attempt on his life, the voice of Donald Trump booms from the speakers in Montego Bay, Jamaica: "If they needed an assassin, they should have sent for Bodyguard … about to commit a quadruple murder at Sumfest in Montego Bay." The audience are taken by surprise, having been primed for a reggae riddim to drop, and laugh. The Bodyguard crew have just taken to the stage at Sumfest Global Sound Clash, a musical gladiatorial contest where sound systems battle against one another with creative mixing, hyped-up MCs and exclusive – often incendiary – recordings featuring star guests and in-jokes. AI vocalists such as this fake Trump, however, are sending shockwaves through a decades-old musical tradition in which authenticity and originality are paramount, and sound systems pay premium rates to artists to get vocals for clashes. "AI is going to mash up the industry," says Fabian Anderson, a dub agent who liaises between artists and sound systems to secure those exclusive tracks.

Neuralink's brain chip has been implanted into a second patient as part of early human trials, Elon Musk told podcast host Lex Fridman on Saturday. The company hasn't disclosed when the surgery took place or the name of the recipient, according to Reuters. Musk said 400 of the electrodes on the second patient's brain are working out of 1,024 implanted. "I don't want to jinx it but it seems to have gone extremely well," he said. The device allows patients with spinal cord injuries to play video games, use the internet and control electronic devices using their thoughts alone.

Will it be easier to retreat to a replicant of a deceased partner than to navigate the confusing and painful realities of human relationships? Indeed, the AI companionship provider Replika was born from an attempt to resurrect a deceased best friend and now provides companions to millions of users. Even the CTO of OpenAI warns that AI has the potential to be "extremely addictive." We're seeing a giant, real-world experiment unfold, uncertain what impact these AI companions will have either on us individually or on society as a whole. Will Grandma spend her final neglected days chatting with her grandson's digital double, while her real grandson is mentored by an edgy simulated elder?