We present Segment Anything Model (SAM) 3, a unified model that detects, segments, and tracks objects in images and videos based on concept prompts, which we define as either short noun phrases (e.g., "yellow school bus"), image exemplars, or a combination of both. Promptable Concept Segmentation (PCS) takes such prompts and returns segmentation masks and unique identities for all matching object instances. To advance PCS, we build a scalable data engine that produces a high-quality dataset with 4M unique concept labels, including hard negatives, across images and videos. Our model consists of an image-level detector and a memory-based video tracker that share a single backbone. Recognition and localization are decoupled with a presence head, which boosts detection accuracy. SAM 3 doubles the accuracy of existing systems in both image and video PCS, and improves previous SAM capabilities on visual segmentation tasks. We open source SAM 3 along with our new Segment Anything with Concepts (SA-Co) benchmark for promptable concept segmentation.

Although Pilat works with the robots daily, she told The Guardian that she still doesn't fully understand them, so she works with Boston Dynamics engineers to shape the robot dogs' personalities. Together, Pilat and the engineers use AI, software, and machine learning to train the robots and even use Basia as a surrogate pet, frequently taking it for walks around New York. Basia is the'serious one,' Pilat told The Guardian, adding that the robot dog will paint about one canvas every three days, while Vanya is the'mother of the group' and paces around the studio. Meanwhile, Bunny's vanity often wins out, and it will continuously pose in front of a wall designed for selfies.

Did you know Neural is taking the stage on Sept 30 and Oct 1? Together with an amazing line-up of experts, we will explore the future of AI during TNW Conference 2021. Samson and Delilah is among the most famous works by Peter Paul Rubens, one of the most influential artists of the 17th century. The painting depicts an Old Testament story in which the warrior Samson is betrayed by his lover Delilah. When London's National Gallery bought the masterpiece in 1980, it became the third most expensive artwork ever purchased at auction. But the buyers may now be searching for their receipt.

The National Gallery has always given pride of place to Peter Paul Rubens's Samson and Delilah, listing it among the "highlights" of its collection, since it purchased the picture at Christie's in 1980 for a then record price. It depicts the Old Testament hero in the lap of the lover who betrayed him, having beguiled him into revealing that his God-given strength lay in his uncut hair. As Samson sleeps, Delilah's accomplice cuts his locks, rendering him powerless, with soldiers ready at the door to capture him. Critics have long suggested that the painting is not really by Rubens. And now a series of scientific tests employing groundbreaking AI technology have concluded that the 17th-century Flemish master could never have painted it.

Researchers have enlisted an AI to help them uncover hidden drawings on the canvas of one of Leonardo Da Vinci most famous paintings. The project was a collaboration between the National Gallery's Dr. Catherine Higgitt and a team from the Imperial College of London, led by Pier Luigi Dragotti. Higgitt and her team at the Gallery had discovered faint sketch marks on the canvas of da Vinci's'Virgin on the Rocks,' which he had originally been commissioned to create in 1483 for a chapel in Milan. Researchers in London discovered a hidden drawing on the canvas of one of Leonard da Vinci's most famous paintings, 'Virgin on the Rocks' (pictured above) The sketchings appeared to hint at an early version of the image that differed from the finished version, which depicts the Madonna with an infant Jesus and an infant John the Baptist in a cavern. The sketches showed wings, which suggested that da Vinci might have originally planned for an angel to be in the painting, as well as a different position for the Madonna.

Putting exhibition text into your phone is nothing new. Big institutions, from the National Gallery to the Netherlands' Rijksmuseum, have had dedicated apps for years, combining audio tours with factoids on collections, artists' lives and specific compositions. The Museo Nacional del Prado's app, for example, goes so far as to let viewers zoom into 14 masterpieces in Ultra HD, and see X-rays that reveal sketches beneath famous works of art. What arguably holds these apps back, however, is the fact they're tied to specific collections in specific buildings. New app on the block Smartify wants to address that, by creating a single platform for gallery-goers to scan artworks to access information and expert commentary across a range of different galleries and museums.