Insect venom means nothing to some amphibians. Breakthroughs, discoveries, and DIY tips sent every weekday. In hindsight, the North American " murder hornet " () scare of 2020 was probably a overblown (not to mention culturally problematic). Of course, you still want to avoid the venomous sting from a northern giant hornet, as they're now known. According to entomologist Masato Ono, receiving a dose of the insect's potent, neurotoxic venom felt "like a hot nail being driven into my leg."

Online encyclopedias, such as Wikipedia, have been well-developed and researched in the last two decades. One can find any attributes or other information of a wiki item on a wiki page edited by a community of volunteers. However, the traditional text, images and tables can hardly express some aspects of an wiki item. For example, when we talk about ``Shiba Inu'', one may care more about ``How to feed it'' or ``How to train it not to protect its food''. Currently, short-video platforms have become a hallmark in the online world. Whether you're on TikTok, Instagram, Kuaishou, or YouTube Shorts, short-video apps have changed how we consume and create content today. Except for producing short videos for entertainment, we can find more and more authors sharing insightful knowledge widely across all walks of life. These short videos, which we call knowledge videos, can easily express any aspects (e.g. hair or how-to-feed) consumers want to know about an item (e.g. Shiba Inu), and they can be systematically analyzed and organized like an online encyclopedia. In this paper, we propose Kuaipedia, a large-scale multi-modal encyclopedia consisting of items, aspects, and short videos lined to them, which was extracted from billions of videos of Kuaishou (Kwai), a well-known short-video platform in China. We first collected items from multiple sources and mined user-centered aspects from millions of users' queries to build an item-aspect tree. Then we propose a new task called ``multi-modal item-aspect linking'' as an expansion of ``entity linking'' to link short videos into item-aspect pairs and build the whole short-video encyclopedia. Intrinsic evaluations show that our encyclopedia is of large scale and highly accurate. We also conduct sufficient extrinsic experiments to show how Kuaipedia can help fundamental applications such as entity typing and entity linking.

He says songwriters face "a battle" to defend work against the growing use of artificial intelligence.

Time will tell if AI decides to wipe out humanity in a terminator-style total war. In the meantime, MailOnline Travel decided to harness its power for good – and ask it for advice about surviving a multitude of perils, from a plane crash to a volcanic eruption, and from attacks by bees and crocodiles to a sinking ship. Mostly useful, though some may find the suggestion to'get away from the bees' stating the very obvious. Google Bard's plane crash advice includes avoiding sitting in the first few rows, as'these rows are more likely to be damaged in a crash' They are trained to handle emergency situations and will know what to do. This will help to keep you in your seat during the crash. This position will help to protect your head and neck in the event of a crash. To assume the brace position, place your feet flat on the floor, lean forward, and place your head down on your knees. It is important to stay calm in an emergency situation.

Time series data are ubiquitous in real-world applications. However, one of the most common problems is that the time series data could have missing values by the inherent nature of the data collection process. So imputing missing values from multivariate (correlated) time series data is imperative to improve a prediction performance while making an accurate data-driven decision. Conventional works for imputation simply delete missing values or fill them based on mean/zero. Although recent works based on deep neural networks have shown remarkable results, they still have a limitation to capture the complex generation process of the multivariate time series. In this paper, we propose a novel imputation method for multivariate time series data, called STING (Self-attention based Time-series Imputation Networks using GAN). We take advantage of generative adversarial networks and bidirectional recurrent neural networks to learn latent representations of the time series. In addition, we introduce a novel attention mechanism to capture the weighted correlations of the whole sequence and avoid potential bias brought by unrelated ones. Experimental results on three real-world datasets demonstrate that STING outperforms the existing state-of-the-art methods in terms of imputation accuracy as well as downstream tasks with the imputed values therein.

The jellyfish sighting app, MedusApp, recently incorporated artificial intelligence (AI) to automatically recognize different species of jellyfish. Until now, this app only required users to select the species of jellyfish from a catalog provided; now the user can upload photos and have the species automatically identified before uploading them to the app for publication. MedusApp, which is freely available in Spanish and English for both Android and iPhone, has been developed by researchers from the University of Alicante (UA) and two computer scientists from the Polytechnic University of Valencia (UPV), in collaboration with the CIBER of Diseases (CIBERES) and the Immunoallergy Laboratory of the Fundación Jiménez Díaz Health Research Institute (IIS-FJD). Since its launch in 2018, the platform has amassed more than 100,000 downloads and 6,000 jellyfish sightings. "Thanks to the collaboration of citizens and their sightings, we have been able to train the AI software with several thousand real photos to generate a mathematical model with a total of 25 species, that will ultimately help the app automatically recognize the most common jellyfish," a novelty update that the programmers from the UPV Eduardo Blasco and Ramón Palacios have highlighted.

Social insect colonies routinely face large vertebrate predators, against which they need to mount a collective defense. To do so, honeybees use an alarm pheromone that recruits nearby bees into mass stinging of the perceived threat. This alarm pheromone is carried directly on the stinger, hence its concentration builds up during the course of the attack. Here, we investigate how individual bees react to different alarm pheromone concentrations, and how this evolved response-pattern leads to better coordination at the group level. We first present an individual dose-response curve to the alarm pheromone, obtained experimentally. Second, we apply Projective Simulation to model each bee as an artificial learning agent that relies on the pheromone concentration to decide whether to sting or not. If the emergent collective performance benefits the colony, the individual reactions that led to it are enhanced via reinforcement learning, thus emulating natural selection. Predators are modeled in a realistic way so that the effect of factors such as their resistance, their killing rate or their frequency of attacks can be studied. We are able to reproduce the experimentally measured response-pattern of real bees, and to identify the main selection pressures that shaped it. Finally, we apply the model to a case study: by tuning the parameters to represent the environmental conditions of European or African bees, we can predict the difference in aggressiveness observed between these two subspecies.

You might know wasps for their ability to brainwash cockroaches or inflict one of the most painful stings on Earth--one so powerful that the actual scientific advice to victims is to just lie down and scream until it passes. Lesser-known is the wasp's superlative ability to carry loads that are unexpectedly heavy given the creature's size. Small drones, or "micro air vehicles," are only able to lift the equivalent of their own weight. If we want flying robots that can move massive objects without requiring them to be the size of pterodactyls, engineers will need to come up with new ways of lifting stuff. So drone designers are looking to wasps for help, and developing creative ways to use the environment itself as a secret weapon in robotics.

It seems like a perfect opportunity for technology to step in and solve problems in the natural world – using tiny helicopter drones to pollinate crops as the number of bees plummets. But amid all the buzz, could this plan for'robot bees' have a sting in the tail? One scientist has suggested the robobees could be taken over by hackers – and turned into killing machines. The robots are under development in both the US and Japan, and it is hoped they could be ready for use within a decade. Under the plans, the drones would wear fuzzy'jackets' that pollen would then stick to, allowing them to pollinate flowers.

When Amazon introduced Alexa in 2014, it quickly discovered that users wanted more than traffic reports and Taylor Swift songs. According to the company, more than 50% of interactions with Alexa are "nonutilitarian and entertainment related," a category that includes professions of love for the female-voiced AI assistant, admissions of loneliness and sadness and requests for a joke. Amazon has sold more than 15 million Echo devices and now owns 75% of the smart-speaker market, according to estimates by Consumer Intelligence Research Partners, which puts this company on the front lines of what might be called early-stage AI therapy, in which a device is asked to respond to extremely personal questions and requests by its users. And while experts say that technology companies likely don't have a legal responsibility when it comes to potential user harm, many see an ethical obligation to consider how to help. Amazon is training Alexa to respond to sensitive questions and statements.