Poodle crossbreeds have grown overwhelmingly popular, sparking controversy in dog parks and kennel clubs alike. The features of doodles such as Peaches (above), a goldendoodle, have become the canine equivalent of Instagram face. Meet the Breeds, the American Kennel Club's annual showcase of purebred dogs, took place over two eye-wateringly cold days in early February at the Javits Center, in Manhattan. About a hundred and fifty of the two hundred and five varieties recognized as official breeds by the A.K.C., the long-standing authority in the U.S. dog world, were in attendance for the public to ogle, fondle, and coo "So cute!" to, including the basset fauve de Bretagne, a hunting hound from France that's one of three newly recognized breeds recently allowed into the purebred pantheon. Some of the dogs had competed in the Westminster Kennel Club Dog Show earlier in the week, and past champions had their ribbons on display. In spite of the frigid weather, pavilions hosting the more popular breeds--the pug, the Doberman pinscher, the Great Dane, the St. Bernard--were packed. Lesser-known varieties, such as the saluki, the Löwchen, and the Lapponian herder, drew sparser crowds. There were exhibition spaces for each breed, and on the back walls were three adjectives supposedly describing that particular type of dog's temperament. There is, in fact, no evidence that temperament is consistent within a breed, but the idea is deeply rooted in dogdom. I stopped to caress the velvety ear leather of a pharaoh hound ("Friendly, Smart, Noble"), a sprinting breed once used to hunt rabbits in Malta; accept kisses from a Portuguese water dog, bred to assist with retrieving tackle ("Affectionate, Adventurous, Athletic"); and have my photograph taken with a Leonberger, a German breed from the town of Leonberg, in southwest Germany ("Friendly, Gentle, Playful"). No one was supposed to be openly selling dogs, but, if you asked, the breeders would share their information. Excluding what are known as companion dogs, like the Leonberger, most of the animals at the show were designed for a purpose that is no longer required of them. In Great Britain, foxhounds are legally barred from chasing foxes. Consider the fate of the otterhound, an ancient variety with a noble heritage which was once used in the U.K. to hunt river otters, which were prized for their thick fur and disliked by wealthy landowners because they ate fish in their stocked ponds.

Science Biology Golden retrievers and humans share'striking' genetic similarities The same genes influence intelligence, anxiety, and depression in both species. Breakthroughs, discoveries, and DIY tips sent every weekday. You're likely not reading too much into your dog's mood: according to researchers at the University of Cambridge, certain genes influencing golden retriever behavior are also traceable to human emotions including intelligence, depression, and anxiety. "The findings are really striking," Eleanor Raffan, a neuroscience researcher and coauthor of a study published in the, said in a statement . "They provide strong evidence that humans and golden retrievers have shared genetic roots for their behavior."

In computer vision, explainable AI (xAI) methods seek to mitigate the 'black-box' problem by making the decision-making process of deep learning models more interpretable and transparent. Traditional xAI methods concentrate on visualizing input features that influence model predictions, providing insights primarily suited for experts. In this work, we present an interaction-based xAI method that enhances user comprehension of image classification models through their interaction. Thus, we developed a web-based prototype allowing users to modify images via painting and erasing, thereby observing changes in classification results. Our approach enables users to discern critical features influencing the model's decision-making process, aligning their mental models with the model's logic. Experiments conducted with five images demonstrate the potential of the method to reveal feature importance through user interaction. Our work contributes a novel perspective to xAI by centering on end-user engagement and understanding, paving the way for more intuitive and accessible explainability in AI systems.

In the Bling Zoo, a tiger wears a giant gold medallion, a monkey sports a bejeweled crown, and a turtle munches on a bowl of diamonds. Unfortunately, this fantastical destination does not exist. 'Bling Zoo' was just one of a series of videos Sora created Thursday when CEO Sam Altman asked his followers on X (formerly Twitter) to submit commands that were generated into movies. The results were so ultra-realistic, they led one observer to comment: 'This one convinced me the future is here and it's going to be OK.' One user requested that Sora create, 'An instructional cooking session for homemade gnocchi hosted by a grandmother social media influencer set in a rustic Tuscan country kitchen with cinematic lighting' This prompt led to the most realistic video containing a human that Altman posted on Thursday.

You don't have to pick up a paintbrush to create a museum-worthy painting. Thanks to an AI tool called DALL-E, all you have to do is type in the picture you want to make. Now that it's finally available to the public, we'll explain how to use DALL-E to generate AI art for free. This versatile tool is excellent for novice artists and experts alike. For example, it can help you develop ideas for paintings -- and you can then tweak the images you generate, so they look perfect.

We propose an enhancement technique of the Class Activation Mapping methods like Grad-CAM or Excitation Backpropagation, which presents visual explanations of decisions from CNN-based models. Our idea, called Gradual Extrapolation, can supplement any method that generates a heatmap picture by sharpening the output. Instead of producing a coarse localization map highlighting the important predictive regions in the image, our method outputs the specific shape that most contributes to the model output. Thus, it improves the accuracy of saliency maps. Effect has been achieved by gradual propagation of the crude map obtained in deep layer through all preceding layers with respect to their activations. In validation tests conducted on a selected set of images, the proposed method significantly improved the localization detection of the neural networks' attention. Furthermore, the proposed method is applicable to any deep neural network model.

If you dip even a toe into the realm of artificial intelligence, you'll come across artificial neural networks. Artificial neural networks are the systems that power artificial intelligence. It's a type of computer that doesn't just read code that it already understands. Neural networks process vast amounts of information to help create an understanding of what's already right in front of you. People think the key to understanding neural networks is calculus, but this system of computing has roots in biology.

The debate over whether dogs or cats are the smartest pet has raged for decades, if not centuries. But in a twist that is sure to ruffle the fur of cat-lovers, new research shows that dogs are more intelligent than their feline foes after all. Experts showed that dogs have more than twice as many brain cells in a region linked with thinking, planning and other complex behaviours. The researchers say the number of neurons in an animal's cerebral cortex is a hallmark of intelligence. The cortex is the largest layer of the brain and is associated with thinking, planning and other complex behaviours.

You can't throw a stone these days without hitting an article about "big data." There's endless advice on how you can be more "data-driven" (we prefer "data-informed"); how data can drive you and your business to infinite success; how X or Y analytics product will make your data dreams reality. People talk a lot about how "big" data can be, but much less about how "big" the problem data itself actually presents; that's a problem we've only barely begun to tackle. I don't mean the day-to-day hard things we deal with -- the janitorial slog of cleaning data, formulating SQL, building charts, getting your data infra right -- I mean the larger picture. How do we go about the business of translating the bits we collect at (often) massive scale into actual human knowledge.

Machine Learning, Artificial Intelligence and Natural Language Processing (NLP) are transforming the technological landscape in a wide range of applications. Three primary uses are predictive analytics, deductive reasoning and natural language understanding. Interfaces for domains such as search and geolocation are increasingly natural-language-like instead of using rigid menu-driven, or programming-language-like interfaces. The task of understanding the user's intention requires complex systems based on machine learning, training data, NLP algorithms modeling theoretical linguistics, or a combination of these techniques. Secondly, machine learning allows us to predict user intention based off of previous user data and tendencies.