A 17-year-old British sailor saved Simon from the Hong Kong docks when he was likely a year old. Breakthroughs, discoveries, and DIY tips sent six days a week. One day in March of 1948, George Hickinbottom, a British sailor, was walking around the docks of Stonecutters Island in Hong Kong. When the 17-year-old spotted a small black-and-white tuxedo cat, barely out of kittenhood, he decided to smuggle the hungry, scrawny animal aboard his ship, the HMS . Hickinbottom didn't get in trouble.

Biscotti once fed Roman navies and Christopher Columbus's expeditions Long before it met espresso, this crunchy pastry kept sailors fed. Roman writer Pliny the Elder was the first writer to mention biscotti in 77 CE. Breakthroughs, discoveries, and DIY tips sent every weekday. Step into a typical Italian restaurant in the U.S. and you'll likely find "biscotti" on the menu. Typically served with a glass of sweet wine or cappuccino, these log-shaped crunchy cookies are a beloved treat that most of us associate with cozy dinners and Little Italy.

The fundamental limitation of the behavioral cloning (BC) approach to imitation learning is that it only teaches an agent what the expert did at states the expert visited. This means that when a BC agent makes a mistake which takes them out of the support of the demonstrations, they often don't know how to recover from it. In this sense, BC is akin to giving the agent the fish -- giving them dense supervision across a narrow set of states -- rather than teaching them to fish: to be able to reason independently about achieving the expert's outcome even when faced with unseen situations at test-time. In response, we explore learning to search (L2S) from expert demonstrations, i.e. learning the components required to, at test time, plan to match expert outcomes, even after making a mistake. These include (1) a world model and (2) a reward model. We carefully ablate the set of algorithmic and design decisions required to combine these and other components for stable and sample/interaction-efficient learning of recovery behavior without additional human corrections. Across a dozen visual manipulation tasks from three benchmarks, our approach SAILOR consistently out-performs state-of-the-art Diffusion Policies trained via BC on the same data. Furthermore, scaling up the amount of demonstrations used for BC by 5-10x still leaves a performance gap. We find that SAILOR can identify nuanced failures and is robust to reward hacking. Our code is available at https://github.com/arnavkj1995/SAILOR .

Adm. Daryl Caudle joins'America's Newsroom' to discuss rising tensions with China's navy, the use of AI in US defense, and a powerful Memorial Day re-enlistment ceremony at Ground Zero. The U.S. Navy has called off a search for a sailor assigned to the USS George Washington amid reports that he possibly went overboard while the ship was sailing north of Australia. The sailor was reported overboard on the aircraft carrier on Monday as the ship was transiting the Timor Sea, the Navy said. US DEFENSE OFFICIAL REACTS TO IRAN'S CLAIMS ABOUT ENCOUNTER WITH WARSHIP This photo shows a general view of U.S. aircraft carrier USS George Washington shortly after berthing at Manila Bay in Manila on July 3. (TED ALJIBE/AFP via Getty Images) The search effort involving the George Washington, its carrier strike group, as well as the Australian Defence (sic) Force and Australian Border Force, concluded at 12:40 p.m. Wednesday. "USS George Washington expresses sincere condolences to those impacted by this loss and is actively engaged with the crew to make services available to tend to their needs during this challenging time," Lt. Cmdr.

It is a game about love and identity, but it also has forklift truck races. It is a game about bloody revenge, but while you're waiting to retaliate, you can buy lottery tickets and visit the arcade. When Bafta recently asked gamers to vote on the most influential game of all time, I'm not sure even the most ardent Sega fans would have gambled on the success of an idiosyncratic Dreamcast adventure from 1999. Yet the results, released on Thursday morning, show Shenmue at No 1, with perhaps more predictable contenders Doom and Super Mario Bros coming in second and third respectively. How has this happened, especially considering the game was considered a financial failure at the time of its release, falling short of recouping its then staggering development costs (a reported 70m, which would now get you about a third of Horizon Forbidden West or Star Wars Outlaws)?

Merging Large Language Models (LLMs) aims to amalgamate multiple homologous LLMs into one with all the capabilities. Ideally, any LLMs sharing the same backbone should be mergeable, irrespective of whether they are Fine-Tuned (FT) with minor parameter changes or Pre-Trained (PT) with substantial parameter shifts. However, existing methods often manually assign the model importance, rendering them feasible only for LLMs with similar parameter alterations, such as multiple FT LLMs. The diverse parameter changed ranges between FT and PT LLMs pose challenges for current solutions in empirically determining the optimal combination. In this paper, we make a pioneering effort to broaden the applicability of merging techniques from FT to PT LLMs. We initially examine the efficacy of current methods in merging FT and PT LLMs, discovering that they struggle to deal with PT LLMs. Subsequently, we introduce an approach based on WeIght DisENtanglement (WIDEN) to effectively extend the merging scope, which first disentangles model weights into magnitude and direction components, and then performs adaptive fusion by considering their respective contributions. In the experiments, we merge Qwen1.5-Chat (an FT LLM with instruction-following skills) with Sailor (a PT LLM with multilingual abilities) across 7B and 14B model scales. Results reveal that: (1) existing solutions usually fail when merging Sailor, either losing both abilities or only retaining instruction-following skills; (2) WIDEN successfully injects the multilingual abilities of Sailor into Qwen1.5-Chat and make it proficient in Southeast Asian languages, achieving enhancements in the fundamental capabilities. In light of previous research, we also merge multiple 13B FT LLMs and observe that WIDEN achieves a balanced amalgamation of instruction following, mathematical reasoning, and code generation skills.

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in representation learning for graphs recently. However, the effectiveness of GNNs, which capitalize on the key operation of message propagation, highly depends on the quality of the topology structure. Most of the graphs in real-world scenarios follow a long-tailed distribution on their node degrees, that is, a vast majority of the nodes in the graph are tail nodes with only a few connected edges. GNNs produce inferior node representations for tail nodes since they lack structural information. In the pursuit of promoting the expressiveness of GNNs for tail nodes, we explore how the deficiency of structural information deteriorates the performance of tail nodes and propose a general Structural Augmentation based taIL nOde Representation learning framework, dubbed as SAILOR, which can jointly learn to augment the graph structure and extract more informative representations for tail nodes. Extensive experiments on public benchmark datasets demonstrate that SAILOR can significantly improve the tail node representations and outperform the state-of-the-art baselines.

Symbolic anchoring is a crucial problem in the field of robotics, as it enables robots to obtain symbolic knowledge from the perceptual information acquired through their sensors. In cognitive-based robots, this process of processing sub-symbolic data from real-world sensors to obtain symbolic knowledge is still an open problem. To address this issue, this paper presents SAILOR, a framework for providing symbolic anchoring in ROS 2 ecosystem. SAILOR aims to maintain the link between symbolic data and perceptual data in real robots over time. It provides a semantic world modeling approach using two deep learning-based sub-symbolic robotic skills: object recognition and matching function. The object recognition skill allows the robot to recognize and identify objects in its environment, while the matching function enables the robot to decide if new perceptual data corresponds to existing symbolic data. This paper provides a description of the framework, the pipeline and development as well as its integration in MERLIN2, a hybrid cognitive architecture fully functional in robots running ROS 2.

While the popular view is that insights are the key benefit of artificial intelligence, in truth AI creates value by improving the quality of decisions. The good news is, the opportunities for it to do that in business are countless. But because decisions in one area of an organization usually have an impact on decisions in other areas, introducing AI often entails redesigning whole systems. In that way, AI is similar to groundbreaking technologies of the past, like electricity, which initially was used only narrowly but ultimately transformed manufacturing. Decisions involve a combination of prediction and judgment, and because AI makes highly accurate predictions, it will shift decision rights to where judgment is still needed, potentially changing who makes decisions and where, when, and how. More-accurate predictions in one part of a value chain will also have ripple effects on other parts. For instance, if a restaurant can reliably forecast the amount of ingredients it needs each week, its orders will fluctuate, making its suppliers’ sales more uncertain. Strong communication is needed to synchronize effort and resources in a system, and modularity will help prevent changes in one area from disrupting others.

Florida-based IC Realtime has been an "industry insider" B2B provider of highly automated HD & 4K video camera systems for security and operations management. While carefully operating during pandemic shutdowns, chip shortages, and supply chain disruptions, the company has also been moving most manufacturing to South Korea and adding Artificial Intelligence (AI) integration to its cameras and recorder products. "South Korea and Japan make our key components now, and we are getting even better performance and reliability," according to company founder Matt Sailor. "Our dealers & integrators value dependable products. Rolling a truck or dispatching a tech when something fails wastes a lot of resources, so reliable operation is job #1 for us."