Christopher Nolan's'The Odyssey' uncorks a Trojan Horse popcorn bucket that stores the goods in its crotch New trailer released for upcoming post-apocalyptic thriller'The Dog Stars' with Jacob Elordi'House of the Dragon' Season 3 premiere runtime and details revealed for hit HBO series You're not getting away with watering your grass with your'crank' out on Sheriff Grady Judd's watch Taylor Sheridan's hit CIA/military series'Lioness' gets official season release date on Paramount+ It wasn't on his shopping list, but a man managed to accidentally shoot himself in the groin at Walmart anyway Smokin' Charley Hull gets off to a fast start at U.S. Women's Open before fading, Stephen A weight loss & MEAT Waka Flocka Flame picks Trump over Kamala Harris, compares choice to Kobe Bryant vs. LeBron James Model Penny Lane's work on the SI Swimsuit runway last weekend still has the internet talking Velma from'Scooby-Doo' cranks up the heat, Nick Saban dragged on Capitol Hill & the great car dealership scam Steve Doocy explores Bentonville, Arkansas, the'Mountain Bike Capital of the World' Steve Doocy traces Walmart's origins in Arkansas Pompeo warns Iranian regime will'not go away' after US helicopter downed House approves resolution to limit Trump's war powers Trump's reveals new details on Iran drone attack downing US Apache helicopter The'Nicotine' singer's comments in Lexington drew massive support from fans praising her boldness Ella Langley discusses releasing a series of singles and building momentum toward her sophomore album during coverage tied to the 2026 iHeartRadio Music Awards held March 26, 2026, at the Dolby Theatre in Los Angeles. Country music sensation Ella Langley continues to prove she's one of the best people in entertainment. Langley has been on an unstoppable role lately, and she's built herself a massive following in the genre she's dominating. It's not just the music fans love. It's the fact that the Nicotine singer comes off as incredibly authentic, and is very open about who she is as a person.

Fox News' Stephanie Bennett reports the latest on the unidentified drones from London. The Pentagon unveiled a new counter-drone strategy after a spate of incursions near U.S. bases prompted concerns over a lack of an action plan for the increasing threat of unmanned aerial vehicles. Though much of the strategy remains classified, Defense Secretary Lloyd Austin will implement a new counter-drone office within the Pentagon – Joint Counter-Small UAS Office – and a new Warfighter Senior Integration Group, according to a new memo. The Pentagon will also begin work on a second Replicator initiative, but it will be up to the incoming Trump administration to decide whether to fund this plan. The first Replicator initiative worked to field inexpensive, dispensable drones to thwart drone attacks by adversarial groups across the Middle East and elsewhere.

A retired, senior Pentagon official has confirmed that UFO'mother ships' were spotted'releasing swarms of smaller craft' -- adding further mystery to the still-unexplained intrusions over multiple US military bases. His statements come amid the release of 50 pages of Air Force records related to provocative'drone' incursions, that one general calls'Close Encounters at Langley.' For at least 17 nights last December, swarms of noisy, small UFOs were seen at dusk'moving at rapid speeds' and displaying'flashing red, green, and white lights' penetrating the highly restricted airspace above Langley Air Force Base in Virginia. Senior ex-Pentagon security official Chris Mellon told DailyMail.com'Two of the notable aspects,' he said, 'are the fact our drone signal-jamming devices have proven ineffective and these craft are making no effort to remain concealed.'

The literature on concept formation has demonstrated that humans are capable of learning concepts incrementally, with a variety of attribute types, and in both supervised and unsupervised settings. Many models of concept formation focus on a subset of these characteristics, but none account for all of them. In this paper, we present TRESTLE, an incremental account of probabilistic concept formation in structured domains that unifies prior concept learning models. TRESTLE works by creating a hierarchical categorization tree that can be used to predict missing attribute values and cluster sets of examples into conceptually meaningful groups. It updates its knowledge by partially matching novel structures and sorting them into its categorization tree. Finally, the system supports mixed-data representations, including nominal, numeric, relational, and component attributes. We evaluate TRESTLE's performance on a supervised learning task and an unsupervised clustering task. For both tasks, we compare it to a nonincremental model and to human participants. We find that this new categorization model is competitive with the nonincremental approach and more closely approximates human behavior on both tasks. These results serve as an initial demonstration of TRESTLE's capabilities and show that, by taking key characteristics of human learning into account, it can better model behavior than approaches that ignore them.

Cobweb, a human-like category learning system, differs from most cognitive science models in incrementally constructing hierarchically organized tree-like structures guided by the category utility measure. Prior studies have shown that Cobweb can capture psychological effects such as basic-level, typicality, and fan effects. However, a broader evaluation of Cobweb as a model of human categorization remains lacking. The current study addresses this gap. It establishes Cobweb's alignment with classical human category learning effects. It also explores Cobweb's flexibility to exhibit both exemplar- and prototype-like learning within a single framework. These findings set the stage for further research on Cobweb as a robust model of human category learning.

This work networks can exceed human capabilities in certain tasks aims to combine computer vision principles with this prior such as object detection and classification (He, Zhang, Ren, & approach and explore the idea of incorporating new visual information Sun, 2016). However, such networks cannot handle continual incrementally without erasing previously learned learning of new tasks without forgetting previously learned data. Our results demonstrate that Cobweb/4V does not exhibit data. Catastrophic forgetting is a fundamental challenge for catastrophic forgetting, only limited interference effects artificial neural networks (McCloskey & Cohen, 1989). This when compared to neural networks. We find that Cobweb/4V phenomenon happens when the network is trained on multiple is competitive with neural network approaches while having tasks sequentially, and to meet the objective of the new minimal forgetting effects. It is also more data efficient task, it changes the weights learned to perform the previous and achieves asymptotic performance with fewer examples.

The paper surveys automated scientific discovery, from equation discovery and symbolic regression to autonomous discovery systems and agents. It discusses the individual approaches from a "big picture" perspective and in context, but also discusses open issues and recent topics like the various roles of deep neural networks in this area, aiding in the discovery of human-interpretable knowledge. Further, we will present closed-loop scientific discovery systems, starting with the pioneering work on the Adam system up to current efforts in fields from material science to astronomy. Finally, we will elaborate on autonomy from a machine learning perspective, but also in analogy to the autonomy levels in autonomous driving. The maximal level, level five, is defined to require no human intervention at all in the production of scientific knowledge. Achieving this is one step towards solving the Nobel Turing Grand Challenge to develop AI Scientists: AI systems capable of making Nobel-quality scientific discoveries highly autonomously at a level comparable, and possibly superior, to the best human scientists by 2050.

A soft plastic'robot' that can take on different shapes is being developed in a NASA laboratory to be used one day for exploring the moon and beyond. It has been made using 3D printing technology with a mould and liquid plastic that cools to form the final structure. It has been designed with internal air pockets that can be inflated and deflated to create different shapes, sizes and strengths out of silicon. Their plasticity means they are more resistance to hard hits and can take on multiple functions, even joining together to form temporary mega structures. A soft plastic'robot' (pictured) that can take on different shapes is being developed in a NASA laboratory to be used one day for exploring the moon and beyond Two interns, Chuck Sullivan and Jack Fitzpatrick,working in NASA's Langley's Makerspace Lab, have created a small plastic device that could be the prototype of a future'collapsible' space robot.

Department of Computer Science Rutgers Universaty New Brunswick, New Jersey 08903 Abstract In this article we discuss a method for learning useful conditions on the application of operators during heuristic search Since learning is not attempted until a complete solution path has been found for a problem, credit for correct moves and blame for incorrect moves is easily assigned We review four learning systems that have incorporated similar techniques to learn in the domains of algebra, symbolic integration, and puzzle-solving We conclude that the basic approach of learning from solution paths can be applied t,o any situation in which problems can be solved by sequential search Finally, we examine some potential difficulties that may arise in more complex domains, and suggest some possible extensions for dealing with them. PEOPLE LEARN FROM EXPERIENCE, and for the past 25 years, Artificial Intelligence researchers have been attempting to replicate this process. In t,his article we focus on learning in domains where search is involved. Furthermore, we will restrict our attention t,o cases in which the legal operators for a task are known, and the learning task is to determine the conditions under which those operators can be usefully applied. Once such a set of heuristically useful conditions has been discovered, search will be directed down profitable We would like to thank Jaime Carbonell and Hans Berliner for helpful comments on an earlier version of this article.

If the world is ever going to enjoy the upsides of a sky filled with drones, the unmanned aircraft must be able to behave at least as well as human pilots. They must know how to react to other aircraft coming right for them, how to manage sudden weather changes, and what to do when their vehicle goes haywire. That's why researchers at NASA's Langley Research Center in Virginia have developed a system that can help with one slice of drone troubleshooting: enabling small UAVs to determine on their own when they're not working properly, and then find a safe place to land. Safe2Ditch, invented by Langley's Trish and Lou Glaab, is designed for fully autonomous aircraft without human pilots at the controls. It uses software algorithms to detect battery or motor problems, control-surface or structural failures, or even shifting cargo that can disrupt the aircraft's balance.