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.

With nearly 400 breeds spanning almost every corner of the planet, dogs have long followed man on his travels. In a bid to piece together the complex evolution of dogs, researchers have looked at the genetic sequences of 161 modern breeds. The resulting map unearths new evidence that dogs travelled with humans across the Bering land bridge 15,000 years ago, and will likely help researchers identify disease-causing genes in both dogs and humans. In a bid to piece together the complex evolution of dogs, researchers have looked at the genetic sequences of 161 modern breeds. Researchers from the National Institutes of Health (NIH) in Maryland say their findings highlight how the oldest dog breeds evolved, or were bred to fill certain roles.