In many scientific disciplines, coarse-grained causal models are used to explain and predict the dynamics of more fine-grained systems. Naturally, such models require appropriate macrovariables. Automated procedures to detect suitable variables would be useful to leverage increasingly available high-dimensional observational datasets. This work introduces a novel algorithmic approach that is inspired by a new characterisation of causal macrovariables as information bottlenecks between microstates. Its general form can be adapted to address individual needs of different scientific goals. After a further transformation step, the causal relationships between learned variables can be investigated through additive noise models. Experiments on both simulated data and on a real climate dataset are reported. In a synthetic dataset, the algorithm robustly detects the ground-truth variables and correctly infers the causal relationships between them. In a real climate dataset, the algorithm robustly detects two variables that correspond to the two known variations of the El Nino phenomenon.

In the wake of the woefully insufficient Pentagon report from June in which the U.S. government admitted it could not explain the vast majority of unidentified aerial phenomena, the Department of Defense is increasing its effort, creating an official group to study these events. The announcement, made late Tuesday, will see the establishment of the Airborne Object Identification and Management Synchronization Group (AOIMSG), succeeding the U.S. Navy's Unidentified Aerial Phenomena Task Force; it will be part of the office of Under Secretary of Defense for Intelligence & Security. The AOIMSG will work across the Department of Defense and the entire U.S. government'to detect, identify and attribute objects of interests in Special Use Airspace, and to assess and mitigate any associated threats to safety of flight and national security,' according to a press release issued by the DoD. The move to formally establish the office was made the Under Secretary of Defense for Intelligence & Security Ronald S. Moultrie, who was directed by Deputy Secretary of Defense Kathleen Hicks and Director of National Intelligence Avril Haines. The Pentagon is creating a group to study unidentified aerial phenomena.

While captioning models have obtained compelling results in describing natural images, they still do not cover the entire long-tail distribution of real-world concepts. In this paper, we address the task of generating human-like descriptions with in-the-wild concepts by training on web-scale automatically collected datasets. To this end, we propose a model which can exploit noisy image-caption pairs while maintaining the descriptive style of traditional human-annotated datasets like COCO. Our model separates content from style through the usage of keywords and stylistic tokens, employing a single objective of prompt language modeling and being simpler than other recent proposals. Experimentally, our model consistently outperforms existing methods in terms of caption quality and capability of describing long-tail concepts, also in zero-shot settings. According to the CIDEr metric, we obtain a new state of the art on both COCO and nocaps when using external data.

A new tool with cutting-edge image recognition AI lets you visualize the future effects of climate change on any place in the world -- including your own home. The project, titled "This Climate Does Not Exist," lets you enter the address of your current home or your favorite travel destination and see what it could look like years later once climate change has taken its toll. You can see how Disneyland will look like covered in smog, the way extreme smog blanketed Beijing in 2014. You can see what your childhood home will look like after it is flooded by rising sea levels, the way floods devastated Indonesia in 2020 after widespread deforestation. Jakarata floods: Thousands caught in floods in Jakarta, Indonesia's sinking capital Extreme weather events due to climate change are already impacting corners of the globe.

There is an increasing demand for piloted autonomous underwater vehicles (AUVs) to operate in polar ice conditions. At present, AUVs are deployed from ships and directly human-piloted in these regions, entailing a high carbon cost and limiting the scope of operations. A key requirement for long-term autonomous missions is a long-range route planning capability that is aware of the changing ice conditions. In this paper we address the problem of automating long-range route-planning for AUVs operating in the Southern Ocean. We present the route-planning method and results showing that efficient, ice-avoiding, long-distance traverses can be planned.

San Francisco, London, Montreal, Paris, and New York have all developed a reputation for being hotbeds of artificial intelligence research over the years. Sydney and Melbourne, Australia's two biggest cities, have not. But that could be about to change. Google announced Monday that it plans to set up a new Google Research Australia lab in Sydney as part of a 1 billion Australian dollar ($729 million) investment in Australia. The lab will research everything from AI to quantum computing.

In this paper, we present an algorithm for creating a synthetic population for the Greater Melbourne area using a combination of machine learning, probabilistic, and gravity-based approaches. We combine these techniques in a hybrid model with three primary innovations: 1. when assigning activity patterns, we generate individual activity chains for every agent, tailored to their cohort; 2. when selecting destinations, we aim to strike a balance between the distance-decay of trip lengths and the activity-based attraction of destination locations; and 3. we take into account the number of trips remaining for an agent so as to ensure they do not select a destination that would be unreasonable to return home from. Our method is completely open and replicable, requiring only publicly available data to generate a synthetic population of agents compatible with commonly used agent-based modeling software such as MATSim. The synthetic population was found to be accurate in terms of distance distribution, mode choice, and destination choice for a variety of population sizes.

Apple is stepping up its plans to enter the car market and aims to launch a self-driving electric vehicle in 2025, according to a report. The tech company's much-rumoured automotive project has bolstered its ambitions under new leadership and is pushing for a fully self-driving vehicle with no steering wheel or pedals, said Bloomberg. The car's interior would be designed for hands-off driving, with one possible design featuring passengers sitting around a U-shaped seating formation. Apple's below-the-radar car venture – known as Project Titan – was dealt an apparent blow in September when the executive in charge of its development, Doug Field, defected to Ford. But the iPhone maker appears undaunted by the challenge of entering the competitive electric vehicle market despite a number of senior leadership changes at Titan this year, Field's the most significant among them.

Machine learning (ML) is how a system learns and adapts its processes from the patterns found in large amounts of data. When we think of machine learning, some prominent examples come to mind. For instance, the way product recommendations on Amazon are eerily similar to Google searches you've done. The scope of machine learning extends far beyond what we know of and see in our daily lives. Since machine learning is a relatively new field, the limits of its application are constantly pushed outward.

The fronds of the coconut tree swayed gently in the cooling breeze blowing from the sea. The turquoise blue waters of the shallow lagoon shimmered in the evening sunlight. Children played on the white sand. The whitewashed clinic building was set among a grove of coconut and mango trees. The clinic had a doctor's room with an old-fashioned high back revolving chair and a room where most procedures could be done.