Darius Rucker jokes that when he wakes up, he doesn't want to have a robot standing over him. Darius Rucker is not worried by the emergence of artificial intelligence and its potential threat to songwriters, explaining that he'll continue to write music for himself. However, the singer-songwriter does find the technology to be off-putting in a larger context. "It's scary," Rucker told Fox News Digital at CMA Fest. "I don't want to wake up one day and have a robot standing over me. It's scary, but technology can be that way. "If people were to use it and everything for songwriting… technology is just way in front of me.

We introduce and define the novel problem of multi-distribution information retrieval (IR) where given a query, systems need to retrieve passages from within multiple collections, each drawn from a different distribution. Some of these collections and distributions might not be available at training time. To evaluate methods for multi-distribution retrieval, we design three benchmarks for this task from existing single-distribution datasets, namely, a dataset based on question answering and two based on entity matching. We propose simple methods for this task which allocate the fixed retrieval budget (top-k passages) strategically across domains to prevent the known domains from consuming most of the budget. We show that our methods lead to an average of 3.8+ and up to 8.0 points improvements in Recall@100 across the datasets and that improvements are consistent when fine-tuning different base retrieval models. Our benchmarks are made publicly available.

Music similarity is an essential aspect of music retrieval, recommendation systems, and music analysis. Moreover, similarity is of vital interest for music experts, as it allows studying analogies and influences among composers and historical periods. Current approaches to musical similarity rely mainly on symbolic content, which can be expensive to produce and is not always readily available. Conversely, approaches using audio signals typically fail to provide any insight about the reasons behind the observed similarity. This research addresses the limitations of current approaches by focusing on the study of musical similarity using both symbolic and audio content. The aim of this research is to develop a fully explainable and interpretable system that can provide end-users with more control and understanding of music similarity and classification systems.

The recently proposed segment anything model (SAM) has made a significant influence in many computer vision tasks. It is becoming a foundation step for many high-level tasks, like image segmentation, image caption, and image editing. However, its huge computation costs prevent it from wider applications in industry scenarios. The computation mainly comes from the Transformer architecture at high-resolution inputs. In this paper, we propose a speed-up alternative method for this fundamental task with comparable performance. By reformulating the task as segments-generation and prompting, we find that a regular CNN detector with an instance segmentation branch can also accomplish this task well. Specifically, we convert this task to the well-studied instance segmentation task and directly train the existing instance segmentation method using only 1/50 of the SA-1B dataset published by SAM authors. With our method, we achieve a comparable performance with the SAM method at 50 times higher run-time speed. We give sufficient experimental results to demonstrate its effectiveness. The codes and demos will be released at https://github.com/CASIA-IVA-Lab/FastSAM.

The rapid spread of fake news is a serious problem calling for AI solutions. We employ a deep learning based automated detector through a three level hierarchical attention network (3HAN) for fast, accurate detection of fake news. 3HAN has three levels, one each for words, sentences, and the headline, and constructs a news vector: an effective representation of an input news article, by processing an article in an hierarchical bottom-up manner. The headline is known to be a distinguishing feature of fake news, and furthermore, relatively few words and sentences in an article are more important than the rest. 3HAN gives a differential importance to parts of an article, on account of its three layers of attention. By experiments on a large real-world data set, we observe the effectiveness of 3HAN with an accuracy of 96.77%. Unlike some other deep learning models, 3HAN provides an understandable output through the attention weights given to different parts of an article, which can be visualized through a heatmap to enable further manual fact checking.

It looks like Fake Drake won't be taking home a Grammy. Recording Academy CEO Harvey Mason Jr. said this week that although the organization will consider music with limited AI-generated voices or instrumentation for award recognition, it will only honor songs written and performed "mostly by a human." "At this point, we are going to allow AI music and content to be submitted, but the Grammys will only be allowed to go to human creators who have contributed creatively in the appropriate categories," Mason said in an interview with Grammy.com. "If there's an AI voice singing the song or AI instrumentation, we'll consider it. But in a songwriting-based category, it has to have been written mostly by a human. Same goes for performance categories – only a human performer can be considered for a Grammy. If AI did the songwriting or created the music, that's a different consideration. But the Grammy will go to human creators at this point."

Some years ago, the satellite radio and pharmaceutical entrepreneur Martine Rothblatt decided that she wanted a semblance of her wife to last forever. So she commissioned Hanson Robotics to create a robot that looked exactly like the head and shoulders of her wife, Bina. The human Bina uploaded many of her memories and autobiographical material into a computer connected to the robot, which Rothblatt named BINA 48. Other information about the world was also uploaded. Like ChatGPT, BINA 48 has a large database (although not as extensive) and a search engine.

New York City grocers are expressing outrage over a push by city council members to ban facial recognition technology stores rely on to deter shoplifting due to concerns of racial discrimination. Ferreira Foodtown CEO Jason Ferreira joined "Fox & Friends" Tuesday to call out the suggestion as thefts continue to rock businesses in the Big Apple. Ferreira, who has been in business for over 45 years, said the shoplifting has never been worse. "It's not only people that are doing it professionally. We have people that are doing it just because they can get away with it. And the gamut runs from children to people that are older."

When wired heard that Christopher Nolan and his producer--and wife--Emma Thomas were coming out with a biopic of J. Robert Oppenheimer, we were perplexed. It is hard for WIRED to resist a Nolan–Thomas film. Nolan has a real love of science, just like us. Add to that, the duo like to bend their audience's minds. WIRED parlance is more often about looking ahead. So we kinda thought maybe we weren't the magazine to dive into this one.

People in Texas sounded off on AI job displacement, with half of the people who spoke to Fox News convinced that the tech will rob them of work. Artificial intelligence algorithms can help predict a person's political ideology based on their facial characteristics, a study conducted in Denmark found. The tech found right-wing politicians were more likely to have happy facial expressions in photos while people pictured with neutral facial expressions were more likely to identify as left-wing, the study said. The study, "Using deep learning to predict ideology from facial photographs: expressions, beauty, and extra-facial information," found that AI can predict a person's political ideology with 61% accuracy when analyzing a photo of a person. Deep learning, a method in AI where computer scientists teach computers to learn and process information similar to humans, can be used to make predictions about people based on photographs alone, the researchers explained in their paper, which was published in Scientific Reports.