Why do elephants have such big ears? The multi-use appendages are kind of like their superpower. The African elephant has some of the world's biggest ears, measuring more than six feet long and more than four feet wide. Breakthroughs, discoveries, and DIY tips sent every weekday. While real life elephants can't fly, they certainly have enormous ears.

FID evaluated over 10k samples instead of 50k for efficiency. It is thus important to compare our method's compute requirements to competing methods. BigGAN-deep with the same or lower compute budget. We include communication time across two machines whenever our training batch size doesn't We find that a naive implementation of our models in PyTorch 1.7 is very inefficient, utilizing only Table 7: Throughput of our ImageNet models, measured in Images per V100-sec. In addition, we can train for many fewer iterations while maintaining sample quality superior to BigGAN-deep.

Poachers kill over 20,000 African elephants every year for their ivory. Breakthroughs, discoveries, and DIY tips sent every weekday. The California Department of Fish and Wildlife (CDFW) recently broke up an alleged illegal poaching front in Los Angeles County. According to the department, thousands of elephant ivory pieces along with multiple "large, intricately carved tusks," a sea turtle shell, and at least nine rhinoceros horns were confiscated from an unnamed business. "The global demand for ivory and rhino horn fuels poaching and organized crime," CDFW Deputy Director and Chief of Law Enforcement Nathaniel Arnold said in a statement, adding that these and other operations "send a clear message" to black market vendors.

FID evaluated over 10k samples instead of 50k for efficiency. It is thus important to compare our method's compute requirements to competing methods. BigGAN-deep with the same or lower compute budget. We include communication time across two machines whenever our training batch size doesn't We find that a naive implementation of our models in PyTorch 1.7 is very inefficient, utilizing only Table 7: Throughput of our ImageNet models, measured in Images per V100-sec. In addition, we can train for many fewer iterations while maintaining sample quality superior to BigGAN-deep.

Deep Neural Networks (DNN) have become a central method in ecology. Most current deep learning (DL) applications rely on one of the major deep learning frameworks, in particular Torch or TensorFlow, to build and train DNN. Using these frameworks, however, requires substantially more experience and time than typical regression functions in the R environment. Here, we present 'cito', a user-friendly R package for DL that allows specifying DNNs in the familiar formula syntax used by many R packages. To fit the models, 'cito' uses 'torch', taking advantage of the numerically optimized torch library, including the ability to switch between training models on the CPU or the graphics processing unit (GPU) (which allows to efficiently train large DNN). Moreover, 'cito' includes many user-friendly functions for model plotting and analysis, including optional confidence intervals (CIs) based on bootstraps for predictions and explainable AI (xAI) metrics for effect sizes and variable importance with CIs and p-values. To showcase a typical analysis pipeline using 'cito', including its built-in xAI features to explore the trained DNN, we build a species distribution model of the African elephant. We hope that by providing a user-friendly R framework to specify, deploy and interpret DNN, 'cito' will make this interesting model class more accessible to ecological data analysis. A stable version of 'cito' can be installed from the comprehensive R archive network (CRAN).

Artificial intelligence (AI) and machine learning (ML) present revolutionary opportunities to enhance our understanding of animal behavior and conservation strategies. Using elephants, a crucial species in Africa's protected areas, as our focal point, we delve into the role of AI and ML in their conservation. Given the increasing amounts of data gathered from a variety of sensors like cameras, microphones, geophones, drones, and satellites, the challenge lies in managing and interpreting this vast data. New AI and ML techniques offer solutions to streamline this process, helping us extract vital information that might otherwise be overlooked. This paper focuses on the different AI-driven monitoring methods and their potential for improving elephant conservation. Collaborative efforts between AI experts and ecological researchers are essential in leveraging these innovative technologies for enhanced wildlife conservation, setting a precedent for numerous other species.

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?

On a raw December day, as Christmas music blares over loudspeakers, an African elephant named Asha walks in tight circles in an enclosure at Natural Bridge Zoo, a roadside attraction in Virginia. Her living quarters consist of a barn and three outdoor yards--a fenced patch of grass about 90 by 40 feet, a dirt patch with a few logs scattered about, and a yard where she gives rides to children for $15 and her massive feet have worn a ring into the grass. Her space is barren--no shrubs, trees, or watering holes. Elephants, like humans, are social animals. In the wild, females typically live in herds of eight or more, yet Asha, who's nearly 40 years old, has been confined mostly alone for more than 30 years.

An algorithm designed by a research group from the Universities of Bath, Oxford and Twente may be able to help save African elephants from extinction. Coupled with high-resolution imagery, the algorithm enables a satellite to scan large areas of land in short periods of time and collect 5,000 km2 worth of photos, a good fit for the animals' grassland and forest habitats. The tech development is desperately needed as elephant numbers in Africa are estimated to be at just 415,000. The savanna elephant population has reduced by 60 per cent in the last 50 years and the number of forest elephants have fallen by 86 per cent in the previous three decades. The AI technology carries less risk of double counting, does not endanger humans in the data collection process and is less disturbing for the animals - an improvement on techniques used in the past. Earlier this year Dr Ben Okita, co-chair of the IUCN elephant specialist group, named poaching as one of the biggest threats to African elephants who are targeted by ivory traders.

Vulnerable elephant populations are now being tracked from space using Earth-observation satellites and a type of artificial intelligence (AI) called machine learning. As part of an international project, researchers are using satellite images processed with computer algorithms, which are trained with more than 1,000 images of elephants to help spot the creatures. With machine learning, the algorithms can count elephants even on'complex geographical landscapes', such as those dotted with trees and shrubs. Researchers say this method is a promising new tool for surveying endangered wildlife and can detect animals with the same accuracy as humans. Elephants in woodland as seen from space.