Breakthroughs, discoveries, and DIY tips sent every weekday. Our planet runs on pollinators. Without bees, moths, weevils, and more zooming around and spreading plants' reproductive cells, plants and important crops would not grow. Without plants we would not breathe or eat. When these crucial pollinating species visit flowers and other plants, they produce a number of characteristic sounds, such as wing flapping when hovering, landing, and taking off.

In this section, we study how the data augmentation carried out while training on local surface patches improves the robustness of Compass against self-occlusions and missing parts. To this end, we run an ablation experiment adopting the same training pipeline explained in the main paper at Section 3.2, without randomly removing points from the input cloud. As done in the main paper, we trained the model on 3DMatch and test it on 3DMatch, ETH, and Stanford Views. We compare Compass against its ablated version in terms of repeatability of the LRFs. Results for 3DMatch are shown in Table 1: the performance gain achieved by Compass when deploying the proposed data augmentation validates its importance.

Defining and reliably finding a canonical orientation for 3D surfaces is key to many Computer Vision and Robotics applications. This task is commonly addressed by handcrafted algorithms exploiting geometric cues deemed as distinctive and robust by the designer. Yet, one might conjecture that humans learn the notion of the inherent orientation of 3D objects from experience and that machines may do so alike. In this work, we show the feasibility of learning a robust canonical orientation for surfaces represented as point clouds. Based on the observation that the quintessential property of a canonical orientation is equivariance to 3D rotations, we propose to employ Spherical CNNs, a recently introduced machinery that can learn equivariant representations defined on the Special Orthogonal group SO(3). Specifically, spherical correlations compute feature maps whose elements define 3D rotations. Our method learns such feature maps from raw data by a self-supervised training procedure and robustly selects a rotation to transform the input point cloud into a learned canonical orientation. Thereby, we realize the first end-to-end learning approach to define and extract the canonical orientation of 3D shapes, which we aptly dub Compass. Experiments on several public datasets prove its effectiveness at orienting local surface patches as well as whole objects.

Breakthroughs, discoveries, and DIY tips sent every weekday. MotoGP is high-speed, high-tech motorcycle racing. The fastest riders in the world compete on specialized, purpose-built motorcycles from companies like Ducati, Honda, Yamaha on the world stage in this series, which is considered the most prestigious in the game. Riders reach incredible speeds on their machines up to 220 miles per hour, and races can go 350 turns with gravity-defying leaning that scrapes elbows and knees. This Grand Prix is for the toughest of the tough on the moto circuit.

Their findings are the latest in a growing body of research demonstrating LLMs' powers of persuasion. The authors warn they show how AI tools can craft sophisticated, persuasive arguments if they have even minimal information about the humans they're interacting with. The research has been published in the journal Nature Human Behavior. "Policymakers and online platforms should seriously consider the threat of coordinated AI-based disinformation campaigns, as we have clearly reached the technological level where it is possible to create a network of LLM-based automated accounts able to strategically nudge public opinion in one direction," says Riccardo Gallotti, an interdisciplinary physicist at Fondazione Bruno Kessler in Italy, who worked on the project. "These bots could be used to disseminate disinformation, and this kind of diffused influence would be very hard to debunk in real time," he says.

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One of the most important annual events in the field of artificial intelligence kicks off this week in Singapore: the International Conference on Learning Representations. As usual, chip giant Nvidia had a major presence at the conference, presenting over 70 research papers from its team. The papers cover topics ranging from generating music to creating 3D-realistic videos, robot training tasks, and the ability to generate multiple large language models at the push of a button. "People often think of Nvidia as a chip company that makes awesome chips, and of course, we're really proud of that," said Bryan Catanzaro, Nvidia's head of applied deep learning research, in an interview with ZDNET. "But the story that I think matters the most is that in order for us to make those awesome chips, we have to do research like this, because this teaches us how to make all of those systems."

An AI video based on a famous religious artifact has revealed what Christ may have looked like. The Shroud of Turin is an ancient cloth which many Christians believe was used to wrap Jesus' mutilated body after he died on the cross. Photos of the cloth were fed into Midjourney, an AI image generator, which then produced a lifelike image and video of Christ blinking, smiling and praying as he may have once did before the crucifixion around 33AD. The clip was posted on X, where users have called being touted as'the true face of Jesus.' However, others have pointed out that the technology made Jesus appear white when he would have been Middle Eastern with a darker complexion.

-- We propose a decentralized reinforcement learning solution for multi-agent shepherding of non-cohesive targets using policy-gradient methods. This model-free framework effectively solves the shepherding problem without prior dynamics knowledge. Experiments demonstrate our method's effectiveness and scalability with increased target numbers and limited sensing capabilities. The shepherding problem in robotics exemplifies the problem of harnessing complex systems for control [1], [2]. It generally involves a group of actively controlled agents, termed herders, strategically influencing a group of passive agents, referred to as targets.

North Korean leader Kim Jong Un supervises the test of suicide drones with artificial intelligence technology, according to local media, at an unknown location, in this photo released by North Korea's official Korean Central News Agency on March 27, 2025. KIM POWER PLAY: North Korean dictator Kim Jong Un oversaw tests of newly developed AI-powered suicide drones and called for their increased production, North Korean state media said Thursday. A photo taken on October 4, 2023 in Manta, near Turin, shows a smartphone and a laptop displaying the logos of the artificial intelligence OpenAI research company and ChatGPT chatbot. SUZANNE'S TWIN: Suzanne Somers passed away two years ago, but her memory lives on, not only through her Hollywood career and businesses, but artificial intelligence too. Her widower, Alan Hamel, worked with an AI company called Hollo to create a "twin" of his late wife.