CDI also offers a plausible approach for automatically making sense of competing arguments in a way that accords with the features enumerated here. This paper is part of an argument that it is now feasible to computationally instantiate a reasonable approximation of a coherence theory of truth [64]: the recent benchmark [12] provides additional quantitative evidence in this direction. By "hard-coding" acceptance of conclusively established propositions, this theory can furthermore be anchored in a correspondence theory of truth [65]. In other words, coherence computations can be required to incorporate privileged information that also coheres with observed reality. While it is easy to imagine attempts to try the same thing with privileged information that does not cohere with observed reality, lies cannot persist when they can easily be unraveled. Even with flawless technology (which this will not be), obstacles will be manifold. For example, in a pluralistic society, legal coherence may actually require sacrificing fairness in some ways [66]. Ultimately, people must decide matters for themselves. It is only reasonable to hope that technology can serve as a reliable tool to help people make their decisions more coherent.

--We propose a low-rank adaptation method for training privacy-preserving vision transformer (ViT) models that efficiently freezes pre-trained ViT model weights. In the proposed method, trainable rank decomposition matrices are injected into each layer of the ViT architecture, and moreover, the patch embedding layer is not frozen, unlike in the case of the conventional low-rank adaptation methods. The proposed method allows us not only to reduce the number of trainable parameters but to also maintain almost the same accuracy as that of full-time tuning. The importance of vision transformer (ViT) based-models [1] has been increasing in recent years. ViT -based models can be applied to vision-language tasks [2] in addition to image classification, object detection [3], and semantic segmentation tasks [4].

Large language models (LLMs) have shown great success in various Natural Language Processing (NLP) tasks, whist they still need updates after deployment to fix errors or keep pace with the changing knowledge in the world. Researchers formulate such problem as Model Editing and have developed various editors focusing on different axes of editing properties. However, current editors can hardly support all properties and rely on heavy computational resources. In this paper, we propose a plug-in Model Editing method based on neuron-indexed dynamic LoRA (MELO), which alters the behavior of language models by dynamically activating certain LoRA blocks according to the index built in an inner vector database. Our method satisfies various editing properties with high efficiency and can be easily integrated into multiple LLM backbones. Experimental results show that our proposed MELO achieves state-of-the-art editing performance on three sequential editing tasks (document classification, question answering and hallucination correction), while requires the least trainable parameters and computational cost.

Sequential recommenders have made great strides in capturing a user's preferences. Nevertheless, the cold-start recommendation remains a fundamental challenge as they typically involve limited user-item interactions for personalization. Recently, gradient-based meta-learning approaches have emerged in the sequential recommendation field due to their fast adaptation and easy-to-integrate abilities. The meta-learning algorithms formulate the cold-start recommendation as a few-shot learning problem, where each user is represented as a task to be adapted. While meta-learning algorithms generally assume that task-wise samples are evenly distributed over classes or values, user-item interactions in real-world applications do not conform to such a distribution (e.g., watching favorite videos multiple times, leaving only positive ratings without any negative ones). Consequently, imbalanced user feedback, which accounts for the majority of task training data, may dominate the user adaptation process and prevent meta-learning algorithms from learning meaningful meta-knowledge for personalized recommendations. To alleviate this limitation, we propose a novel sequential recommendation framework based on gradient-based meta-learning that captures the imbalanced rating distribution of each user and computes adaptive loss for user-specific learning. Our work is the first to tackle the impact of imbalanced ratings in cold-start sequential recommendation scenarios. Through extensive experiments conducted on real-world datasets, we demonstrate the effectiveness of our framework.

Just two months after its launch, ChatGPT is already estimated to have reached over 100 million users, making it the fastest-growing consumer application ever. Models of this sort, which can generate realistic, human-like text responses, will have a significant impact on our lives. Our speaker, Prof. Gerard de Melo will discuss how a few simple technical ideas enabled such models to become so powerful, & how these led to both strengths & weaknesses. He will then discuss a series of ongoing developments that are likely to shape the next generation of large AI models, e.g. These new developments, however, will not only lead to new applications but also to new risks.

The Army of the future will involve humans and autonomous machines working together to accomplish the mission. According to Army researchers, this vision will only succeed if artificial intelligence is perceived to be ethical. Researchers, based at the U.S. Army Combat Capabilities Development Command, now known as DEVCOM, Army Research Laboratory, Northeastern University and the University of Southern California, expanded existing research to cover moral dilemmas and decision making that has not been pursued elsewhere. This research, featured in Frontiers in Robotics and AI, tackles the fundamental challenge of developing ethical artificial intelligence, which, according to the researchers, is still mostly understudied. "Autonomous machines, such as automated vehicles and robots, are poised to become pervasive in the Army," said DEVCOM ARL researcher Dr. Celso de Melo, who is located at the laboratory's ARL West regional site in Playa Vista, California.

The Army's plans for robotic wingmen in vehicle formations, a drone on every soldier and robotic mules carrying gear all aim to take the load off the fighter. But how will the two communicate, robot and human? Voice commands like automated assistants on smartphones are great, but not when the threat of incoming fire means the robot battle buddy needs to decipher a range of priorities that humans might take for granted. The next test will come in late 2021 and involve a company-sized maneuver at Fort Hood, Texas. Think more C3PO or R2D2 in the "Star Wars" movies than Hal in "2001: A Space Odyssey" --or better yet, a friendly cyborg from "Terminator" might be the best way to see your robot combatant squad mate of the distant future.

Dr. Celso de Melo, computer scientist with the U.S. Army Combat Capabilities Development Command's Army Research Laboratory at CCDC ARL West in Playa Vista, California, in collaboration with Dr. Kazunori Teradafrom Gifu University in Japan, recently published a paper in Scientific Reports where they show that emotion expressions can shape cooperation. Autonomous machines that act on people's behalf are poised to become pervasive in society, de Melo said; however, for these machines to succeed and be adopted, it is essential that people are able to trust and cooperate with them. "Human cooperation is paradoxical," de Melo said. "An individual is better off being a free rider, while everyone else cooperates; however, if everyone thought like that, cooperation would never happen. This research aims to understand the mechanisms that promote cooperation with a particular focus on the influence of strategy and signaling."

This undated photo provided by researchers in January 2019 shows the OroBOT, based on an Orobates Pabsti fossil. Scientists have used a nearly 300-million-year old skeleton and preserved ancient footprints to create the moving robot model of prehistoric life. Scientists now have an idea of how an ancient creature likely moved thanks to a nearly 300-million-year-old fossilized skeleton and a set of well-preserved footprints. Studying the ancient fossil and a set of fossilized tracks from a plant-eating creature known as the Orabates pabsti, evolutionary biologist John Nyakatura at Humboldt University in Berlin and robotics expert Kamilo Melo at the Swiss Federal Institute of Technology in Lausanne recently created a life-size replica of the animal, which existed before the dinosaurs, scientists say. "We carefully modeled each and every bone," Nyakatura told The Associated Press of the replica.

WASHINGTON - How did the earliest land animals move? Scientists have used a nearly 300-million-year-old fossil skeleton and preserved ancient footprints to create a moving robot model of prehistoric life. Evolutionary biologist John Nyakatura at Humboldt University in Berlin has spent years studying a 290-million-year-old fossil dug up in central Germany's Bromacker quarry in 2000. The four-legged plant-eater lived before the dinosaurs and fascinates scientists "because of its position on the tree of life," said Nyakatura. Researchers believe the creature is a "stem amniote" -- an early land-dwelling animal that later evolved into modern mammals, birds and reptiles.