We propose an ensemble score filter (EnSF) for solving high-dimensional nonlinear filtering problems with superior accuracy. A major drawback of existing filtering methods, e.g., particle filters or ensemble Kalman filters, is the low accuracy in handling high-dimensional and highly nonlinear problems. EnSF attacks this challenge by exploiting the score-based diffusion model, defined in a pseudo-temporal domain, to characterizing the evolution of the filtering density. EnSF stores the information of the recursively updated filtering density function in the score function, in stead of storing the information in a set of finite Monte Carlo samples (used in particle filters and ensemble Kalman filters). Unlike existing diffusion models that train neural networks to approximate the score function, we develop a training-free score estimation that uses mini-batch-based Monte Carlo estimator to directly approximate the score function at any pseudo-spatial-temporal location, which provides sufficient accuracy in solving high-dimensional nonlinear problems as well as saves tremendous amount of time spent on training neural networks. Another essential aspect of EnSF is its analytical update step, gradually incorporating data information into the score function, which is crucial in mitigating the degeneracy issue faced when dealing with very high-dimensional nonlinear filtering problems. High-dimensional Lorenz systems are used to demonstrate the performance of our method. EnSF provides surprisingly impressive performance in reliably tracking extremely high-dimensional Lorenz systems (up to 1,000,000 dimension) with highly nonlinear observation processes, which is a well-known challenging problem for existing filtering methods.

Americans have plenty of negative opinions about artificial intelligence - but has anyone ever stopped to think: 'What do the machines think about Americans?' Polls show that a majority worry advanced AI will become a'threat to the human race' (57 percent), half consider self-driving cars dangerous, and more than half (54 per cent) believe AI will play a role in America's decline over the coming decades. The feeling might be mutual -- judging by the responses that ChatGPT and the image-generator Midjourney gave DailyMail.com ChatGPT stated that people in Alabama are'hillbillies', Idahoans are'gun-toting survivalists', Wisconsinites are'heavy drinkers' and the citizens of Iowa are just plain'boring'. The AI could not think of anything bad to say about'friendly' Nebraskans, however. While not all 50 US states were as easy for Midjourney to caricature as they were for ChatGPT, the image-maker did manage to roast the citizens of states with notorious or outsized reputations, like California and New Jersey.

Ukrainian officials are generally tight-lipped about attacks inside Russia, says BBC World Affairs correspondent Paul Adams. But it seems that as the campaign gathers pace, officials in Kyiv are more willing to claim them as part of the country's war effort.

We are about to see the greatest redistribution of power in history. Over millennia, humanity has been shaped by successive waves of technology. The discovery of fire, the invention of the wheel, the harnessing of electricity--all were transformational moments for civilization. All were waves of technology that started small, with a few precarious experiments, but eventually they broke across the world. These waves followed a similar trajectory: breakthrough technologies were invented, delivered huge value, and so they proliferated, became more effective, cheaper, more widespread and were absorbed into the normal, ever-evolving fabric of human life.

Fox News' Eben Brown reports on how more companies are using AI technology to set retail prices based on data-driven supply and demand. A majority of Americans are more concerned than excited by the increased use of artificial intelligence, with the number of those concerned growing dramatically in recent years, according to a new survey released this week. The Pew Research survey found that 52% of Americans polled said they are "more concerned than excited" by the increased use of AI in daily life, compared to 36% who are "equally excited and concerned" and 10% who are "more excited than concerned." Just last year, 38% of those surveyed were "more concerned than excited" – and in 2021, that number was 37%. WHAT IS ARTIFICIAL INTELLIGENCE (AI)? The increase in concern comes amid greater awareness and use of the technology, concerns about retaining control over the tech, potential employment implications and how fast the technology is being adopted in key areas.

Naftali Bennett speaks exclusively with Fox News Digital about the benefits of artificial intelligence and the need to set parameters for its use. The Israeli Defense Ministry has unveiled a new surveillance aircraft that integrates artificial intelligence (AI) systems in what officials are calling a groundbreaking development for technology. "The Directorate of Defense Research & Development (DDR&D) has been leading the development of the'Oron' mission systems for over nine years," Brig. Gen. Yaniv Rotem, head of military research and development in the DDR&D of the Ministry of Defense, said in a press release. "This mission aircraft will provide the IDF (Israel Defense Forces) with unprecedented, innovative ISR (intelligence, surveillance and reconnaissance) capabilities using groundbreaking sensing systems – the onboard radar system and a variety of sensors."

Two Ukrainian drones attacked the Russian town of Kurchatov in the Kursk region, damaging administrative and residential buildings, while a third drone was shot down near Moscow, local officials said. Kursk regional Governor Roman Starovoit said emergency services were assessing the damage in Kurchatov town following the early morning attack on Friday. Starovoit wrote on the Telegram messaging app that two buildings were damaged but did not provide further details. Moscow mayor Sergei Sobyanin also reported early on Friday that Russian air defences had shot down a drone that was approaching the capital city. The drone was downed near Lyubertsy, which is located approximately 20km (12 miles) southeast of central Moscow, he wrote on Telegram.

HERMES (High Energy Rapid Modular Ensemble of Satellites) pathfinder is an in-orbit demonstration consisting of a constellation of six 3U nano-satellites hosting simple but innovative detectors for the monitoring of cosmic high-energy transients. The main objective of HERMES Pathfinder is to prove that accurate position of high-energy cosmic transients can be obtained using miniaturized hardware. The transient position is obtained by studying the delay time of arrival of the signal to different detectors hosted by nano-satellites on low Earth orbits. To this purpose, the goal is to achive an overall accuracy of a fraction of a micro-second. In this context, we need to develop novel tools to fully exploit the future scientific data output of HERMES Pathfinder. In this paper, we introduce a new framework to assess the background count rate of a space-born, high energy detector; a key step towards the identification of faint astrophysical transients. We employ a Neural Network (NN) to estimate the background lightcurves on different timescales. Subsequently, we employ a fast change-point and anomaly detection technique to isolate observation segments where statistically significant excesses in the observed count rate relative to the background estimate exist. We test the new software on archival data from the NASA Fermi Gamma-ray Burst Monitor (GBM), which has a collecting area and background level of the same order of magnitude to those of HERMES Pathfinder. The NN performances are discussed and analyzed over period of both high and low solar activity. We were able to confirm events in the Fermi/GBM catalog and found events, not present in Fermi/GBM database, that could be attributed to Solar Flares, Terrestrial Gamma-ray Flashes, Gamma-Ray Bursts, Galactic X-ray flash. Seven of these are selected and analyzed further, providing an estimate of localisation and a tentative classification.

We aim to better understand the emergence of `situational awareness' in large language models (LLMs). A model is situationally aware if it's aware that it's a model and can recognize whether it's currently in testing or deployment. Today's LLMs are tested for safety and alignment before they are deployed. An LLM could exploit situational awareness to achieve a high score on safety tests, while taking harmful actions after deployment. Situational awareness may emerge unexpectedly as a byproduct of model scaling. One way to better foresee this emergence is to run scaling experiments on abilities necessary for situational awareness. As such an ability, we propose `out-of-context reasoning' (in contrast to in-context learning). We study out-of-context reasoning experimentally. First, we finetune an LLM on a description of a test while providing no examples or demonstrations. At test time, we assess whether the model can pass the test. To our surprise, we find that LLMs succeed on this out-of-context reasoning task. Their success is sensitive to the training setup and only works when we apply data augmentation. For both GPT-3 and LLaMA-1, performance improves with model size. These findings offer a foundation for further empirical study, towards predicting and potentially controlling the emergence of situational awareness in LLMs. Code is available at: https://github.com/AsaCooperStickland/situational-awareness-evals.

In the context of unsupervised learning, Lloyd's algorithm is one of the most widely used clustering algorithms. It has inspired a plethora of work investigating the correctness of the algorithm under various settings with ground truth clusters. In particular, in 2016, Lu and Zhou have shown that the mis-clustering rate of Lloyd's algorithm on $n$ independent samples from a sub-Gaussian mixture is exponentially bounded after $O(\log(n))$ iterations, assuming proper initialization of the algorithm. However, in many applications, the true samples are unobserved and need to be learned from the data via pre-processing pipelines such as spectral methods on appropriate data matrices. We show that the mis-clustering rate of Lloyd's algorithm on perturbed samples from a sub-Gaussian mixture is also exponentially bounded after $O(\log(n))$ iterations under the assumptions of proper initialization and that the perturbation is small relative to the sub-Gaussian noise. In canonical settings with ground truth clusters, we derive bounds for algorithms such as $k$-means$++$ to find good initializations and thus leading to the correctness of clustering via the main result. We show the implications of the results for pipelines measuring the statistical significance of derived clusters from data such as SigClust. We use these general results to derive implications in providing theoretical guarantees on the misclustering rate for Lloyd's algorithm in a host of applications, including high-dimensional time series, multi-dimensional scaling, and community detection for sparse networks via spectral clustering.