The birds can make good pets, but only if taken care of properly. Breakthroughs, discoveries, and DIY tips sent six days a week. Rescuers in North Carolina recently saved over 500 pigeons from a home in Greensboro. Guildford County Animal Services and two other bird rescues based in Charlotte initially believed that the call was for about 300 birds . Instead, they found about 550 pigeons inside of a shed behind the home, hidden from the street.

A 2024 study found that cats learn to associate images with words faster than human babies. Breakthroughs, discoveries, and DIY tips sent every weekday. "My cat always watches my phone as I text or read a book," someone wrote on Reddit . "Even right now she is on my shoulder, intently watching what I am typing on this post. Can she read or is she just interested in what I am doing?"

From sales to compliance, AI agents promise to augment workforce capabilities, streamline workflows, and enhance productivity. Amid the turbulence of the wider global economy in recent years, the pharmaceuticals industry is weathering its own storms. Simultaneously, the cost of bringing new drugs to market is climbing. In clinics and health-care facilities, norms and expectations are evolving, too. Patients and health-care providers are seeking more personalized services, leading to greater demand for precision drugs and targeted therapies. The need for personalization extends to sales and marketing operations too as pharma companies are increasingly needing to compete for the attention of health-care professionals (HCPs).

AI toys are all the rage in China--and now they're appearing on shelves in the US too Competition is heating up, with Mattel and OpenAI expected to launch a product for kids this year. Kids have always played with and talked to stuffed animals. But now their toys can talk back, thanks to a wave of companies that are fitting children's playthings with chatbots and voice assistants. It's a trend that has particularly taken off in China: A recent report by the Shenzhen Toy Industry Association and JD.com predicts that the sector will surpass ¥100 billion ($14 billion) by 2030, growing faster than almost any other branch of consumer AI. According to the Chinese corporation registration database Qichamao, there are over 1,500 AI toy companies operating in China as of October 2025. One of the latest entrants to the market is a toy called BubblePal, a device the size of a Ping-Pong ball that clips onto a child's favorite stuffed animal and makes it "talk."

Why do collectives outperform individuals when solving some problems? Fundamentally, collectives have greater computational resources with more sensory information, more memory, more processing capacity, and more ways to act. While greater resources present opportunities, there are also challenges in coordination and cooperation inherent in collectives with distributed, modular structures. Despite these challenges, we show how collective resource advantages lead directly to well-known forms of collective intelligence including the wisdom of the crowd, collective sensing, division of labour, and cultural learning. Our framework also generates testable predictions about collective capabilities in distributed reasoning and context-dependent behavioural switching. Through case studies of animal navigation and decision-making, we demonstrate how collectives leverage their computational resources to solve problems not only more effectively than individuals, but by using qualitatively different problem-solving strategies.

People looking for precursors to artificial intelligence often point to science fiction by authors like Isaac Asimov or thought experiments like the Turing test. But an equally important, if surprising and less appreciated, forerunner is American psychologist B.F. Skinner's research with pigeons in the middle of the 20th century. Skinner believed that association--learning, through trial and error, to link an action with a punishment or reward--was the building block of every behavior, not just in pigeons but in all living organisms, including human beings. His "behaviorist" theories fell out of favor with psychologists and animal researchers in the 1960s but were taken up by computer scientists who eventually provided the foundation for many of the artificial-intelligence tools from leading firms like Google and OpenAI. This story is from our forthcoming print issue, which is all about security.

People looking for precursors to artificial intelligence often point to science fiction by authors like Isaac Asimov or thought experiments like the Turing test. But an equally important, if surprising and less appreciated, forerunner is Skinner's research with pigeons in the middle of the 20th century. Skinner believed that association--learning, through trial and error, to link an action with a punishment or reward--was the building block of every behavior, not just in pigeons but in all living organisms, including human beings. His "behaviorist" theories fell out of favor with psychologists and animal researchers in the 1960s but were taken up by computer scientists who eventually provided the foundation for many of the artificial-intelligence tools from leading firms like Google and OpenAI. These companies' programs are increasingly incorporating a kind of machine learning whose core concept--reinforcement--is taken directly from Skinner's school of psychology and whose main architects, the computer scientists Richard Sutton and Andrew Barto, won the 2024 Turing Award, an honor widely considered to be the Nobel Prize of computer science.

The extraction-shooter genre is getting a little more crowded and a lot more stylish with the announcement of Mycopunk, a four-player, first-person romp from indie studio Pigeons at Play and publisher Devolver Digital. Mycopunk is coming to Steam in early access this year. Mycopunk stars four eccentric robots who've been hired by an intergalactic megacorporation to exterminate an invasive, violent fungus that's taken root on a valuable planet. Each robot has a specific class and moveset, but players can use any weapon or loadout with any character -- and that's a huge benefit, because there are a ton of wacky guns, upgrades and ammo options in this game. For example, there are bouncing shotgun pellets, bullets that hover in place and then dive down when you press the trigger again, and a rocket launcher move that also makes you fly.

Personalized content filtering, such as recommender systems, has become a critical infrastructure to alleviate information overload. However, these systems merely filter existing content and are constrained by its limited diversity, making it difficult to meet users' varied content needs. To address this limitation, personalized content generation has emerged as a promising direction with broad applications. Nevertheless, most existing research focuses on personalized text generation, with relatively little attention given to personalized image generation. The limited work in personalized image generation faces challenges in accurately capturing users' visual preferences and needs from noisy user-interacted images and complex multimodal instructions. Worse still, there is a lack of supervised data for training personalized image generation models. To overcome the challenges, we propose a Personalized Image Generation Framework named Pigeon, which adopts exceptional large multimodal models with three dedicated modules to capture users' visual preferences and needs from noisy user history and multimodal instructions. To alleviate the data scarcity, we introduce a two-stage preference alignment scheme, comprising masked preference reconstruction and pairwise preference alignment, to align Pigeon with the personalized image generation task. We apply Pigeon to personalized sticker and movie poster generation, where extensive quantitative results and human evaluation highlight its superiority over various generative baselines.

Vision-Language Models (VLMs) have recently demonstrated remarkable capabilities in comprehending complex visual content. However, the mechanisms underlying how VLMs process visual information remain largely unexplored. In this paper, we conduct a thorough empirical analysis, focusing on attention modules across layers. We reveal several key insights about how these models process visual data: (i) the internal representation of the query tokens (e.g., representations of "describe the image"), is utilized by VLMs to store global image information; we demonstrate that these models generate surprisingly descriptive responses solely from these tokens, without direct access to image tokens. (ii) Cross-modal information flow is predominantly influenced by the middle layers (approximately 25% of all layers), while early and late layers contribute only marginally.(iii) Fine-grained visual attributes and object details are directly extracted from image tokens in a spatially localized manner, i.e., the generated tokens associated with a specific object or attribute attend strongly to their corresponding regions in the image. We propose novel quantitative evaluation to validate our observations, leveraging real-world complex visual scenes. Finally, we demonstrate the potential of our findings in facilitating efficient visual processing in state-of-the-art VLMs.