But have you ever wondered what a fight between a hippopotamus and a great white shark might look like? Now, scientists have set the record straight, after using artificial intelligence (AI) to simulate battles between the most terrifying animals on Earth. Somewhat surprisingly, the simulations suggest that a hippo would beat a great white shark - and could even take down a polar bear. However, the ultimate champion of the animal kingdom is the African Elephant, according to researchers from Animal Matchup. In honour of World Animal Day, experts from Animal Match set out to settle the debate - which animal is the strongest?

Transformer-based language models (LMs) are known to capture factual knowledge in their parameters. While previous work looked into where factual associations are stored, only little is known about how they are retrieved internally during inference. We investigate this question through the lens of information flow. Given a subject-relation query, we study how the model aggregates information about the subject and relation to predict the correct attribute. With interventions on attention edges, we first identify two critical points where information propagates to the prediction: one from the relation positions followed by another from the subject positions. Next, by analyzing the information at these points, we unveil a three-step internal mechanism for attribute extraction. First, the representation at the last-subject position goes through an enrichment process, driven by the early MLP sublayers, to encode many subject-related attributes. Second, information from the relation propagates to the prediction. Third, the prediction representation "queries" the enriched subject to extract the attribute. Perhaps surprisingly, this extraction is typically done via attention heads, which often encode subject-attribute mappings in their parameters. Overall, our findings introduce a comprehensive view of how factual associations are stored and extracted internally in LMs, facilitating future research on knowledge localization and editing.

Large language models (LLMs) can achieve highly effective performance on various reasoning tasks by incorporating step-by-step chain-of-thought (CoT) prompting as demonstrations. However, the reasoning chains of demonstrations generated by LLMs are prone to errors, which can subsequently lead to incorrect reasoning during inference. Furthermore, inappropriate exemplars (overly simplistic or complex), can affect overall performance among varying levels of difficulty. We introduce Iter-CoT (Iterative bootstrapping in Chain-of-Thoughts Prompting), an iterative bootstrapping approach for selecting exemplars and generating reasoning chains. By utilizing iterative bootstrapping, our approach enables LLMs to autonomously rectify errors, resulting in more precise and comprehensive reasoning chains. Simultaneously, our approach selects challenging yet answerable questions accompanied by reasoning chains as exemplars with a moderate level of difficulty, which enhances the LLMs' generalizability across varying levels of difficulty. Experimental results indicate that Iter-CoT exhibits superiority, achieving competitive performance across three distinct reasoning tasks on ten datasets.

The news: OpenAI has launched two new ways to interact with its flagship large language model in a major update. You can have a spoken conversation with the chatbot as if you were making a call, and it's also able to answer questions about images. How it works: The ability to talk to ChatGPT draws on two separate models. Whisper, OpenAI's existing speech-to-text model, converts what you say into text, which is then fed to the chatbot. And a new text-to-speech model converts ChatGPT's responses into spoken words.

Planet Earth is about to receive a special delivery -- the biggest sample yet from an asteroid. A United States space agency (NASA) spacecraft will fly by Earth on Sunday and drop off what is expected to be at least a cupful of rubble it grabbed from the asteroid Bennu, closing out a seven-year quest. The sample capsule will parachute into the Utah desert as its mothership, the OSIRIS-REx spacecraft, zooms off for an encounter with another asteroid. Scientists anticipate getting about 250g (0.5lb) of pebbles and dust, much more than the teaspoon or so brought back by Japan from two other asteroids. No other country has fetched pieces of asteroids, preserved time capsules from the dawn of our solar system that can help explain how Earth -- and life -- came to be.

In 1996 Nasa and the White House made the explosive announcement that the rock contained traces of Martian bugs. The meteorite, catalogued as Allen Hills (ALH) 84001, crashed onto the frozen wastes of Antarctica 13,000 years ago and was recovered in 1984. Photographs were released showing elongated segmented objects that appeared strikingly lifelike.

Even if much has been written about ingredients that trigger laughter, researchers are still far from having completely understood their interplay in the cognitive process that leads a listener to guffaw at a pun or a joke. They are even farther from a detailed analysis and modeling of the mechanisms that are at work in this process. However, in recent articles Dupin de Saint-Cyr and Prade (2020, 2022) took a first step in this direction by laying bare that a belief revision mechanism was solicited in the reception of a narrative joke. Namely the punchline, which triggers a revision, is both surprising and explains perfectly what was reported in the beginning of the joke. A similar idea has been more informally proposed in Ritchie (2002). It is quite clear that this is insufficient for characterizing a narrative joke.

Datasets from airborne sensors like AVIRIS [2], ROSIS [3], and HYDICE [4] contain Hyperspectral Imaging, employed in satellites for space remote labeled images with diverse land cover categories like urban sensing, like HYPSO-1, faces constraints due to few and agricultural areas. Additionally, other popular datasets labeled data sets, affecting the training of AI models demanding such as the Kennedy Space Center and Jasper Ridge have limited these ground-truth annotations. In this work, we water coverage due to the capture over small geographic introduce The HYPSO-1 Sea-Land-Cloud-Labeled Dataset, extents using airborne platforms. Despite the widespread use an open dataset with 200 diverse hyperspectral images from of these datasets in HSI classification, each set consists of a the HYPSO-1 mission, available in both raw and calibrated single labeled image, inadequate for training emerging dataintensive forms for scientific research in Earth observation.

NASA's Curiosity rover has spent 11 years searching far and wide for signs of life on Mars. And now it has emerged that the car-sized robot may have found something. In 2021, it detected an unusual array of polygon-shaped cracks within the soil that scientists now believe is evidence that the Red Planet once had Earth-like conditions that could have allowed microorganisms to survive 3.6 billion years ago. The mysterious mud cracks on the bed of an ancient lake hint that wet and dry cycles comparable to the seasons we experience on our planet today may have existed on Mars. Such cycles are vital for encouraging the formation of carbon-based'polymers' - known as the building blocks of organic compounds and even DNA.

Extreme weather events pose significant challenges, thereby demanding techniques for accurate analysis and precise forecasting to mitigate its impact. In recent years, deep learning techniques have emerged as a promising approach for weather forecasting and understanding the dynamics of extreme weather events. This review aims to provide a comprehensive overview of the state-of-the-art deep learning in the field. We explore the utilization of deep learning architectures, across various aspects of weather prediction such as thunderstorm, lightning, precipitation, drought, heatwave, cold waves and tropical cyclones. We highlight the potential of deep learning, such as its ability to capture complex patterns and non-linear relationships. Additionally, we discuss the limitations of current approaches and highlight future directions for advancements in the field of meteorology. The insights gained from this systematic review are crucial for the scientific community to make informed decisions and mitigate the impacts of extreme weather events.