Artificial intelligence could have a more significant impact on Britain than the Industrial Revolution, the deputy prime minister has said, but warned it could be used by hackers to access sensitive information from the government. Oliver Dowden said AI could speed up productivity and perform boring aspects of jobs. "This is a total revolution that is coming," Dowden told the Times. "It's going to totally transform almost all elements of life over the coming years, and indeed, even months, in some cases. "It is much faster than other revolutions that we've seen and much more extensive, whether that's the invention of the internal combustion engine or the Industrial Revolution." Dowden said AI would allow for faster future decision-making by governments. Asylum claim applications processed by the Home Office are already using AI, and it could even be used in reducing paperwork that goes into ministerial red boxes. "The thing that AI right now does really well, it takes massive amounts of information from datasets in different places and enables you to get to a point where you can make decisions," he said. "Ministers are never going to outsource to AI the making of decisions." But he warned AI could be harnessed by terrorists to expand knowledge on dangerous material or conduct widespread hacking operations in the wake of such attacks against the Electoral Commission and the Police Service of Northern Ireland. The details of more than 10,000 officers and staff at the Police Service of Northern Ireland were published online for a number of hours on Tuesday, after an "industrial-scale breach of data". Dowden said: "You can shortcut hacking by AI.

'The Big Weekend Show' panelists discuss Elon Musk offering to pay users' legal bills if they are'unfairly treated' by employers for likes or posts on X, the platform formerly known as Twitter. That's what George Orwell would say if he could visit our world, 75 years after he wrote his final novel, "1984." Orwell sought to demonstrate the dangers not just of totalitarianism but of a world where words lose their meaning. Many of the terms he coined for the novel have since entered common discourse -- "thought police," "Big Brother," "doublethink," and the "memory hole," to name a few. And of course the adjective "Orwellian" comes to us because of this book.

A House Democrat is warning artificial intelligence could become a tool of "digital colonialism" if the U.S. doesn't take steps to work with Western Hemisphere nations to create AI systems that reflect diversity and inclusion. Rep. Adriano Espaillat, D-N.Y., proposed a resolution during the August break that says the U.S. must champion a "regional" AI strategy that includes Western Hemisphere nations as this new technology is developed. "United States-led investments in the development of AI in the Western Hemisphere would promote the inclusion and representation of underserved populations in the global development and deployment of AI technologies, ensuring that no individual country dominates AI but rather collaborative developments in the Western Hemisphere," his resolution asserted. WHAT IS ARTIFICIAL INTELLIGENCE (AI)? Rep Adriano Espaillat, D-N.Y., is calling on the U.S. to work closely with Western nations as it develops artificial intelligence systems and guidelines.

Russia's defence ministry said its forces destroyed a wave of 20 Ukrainian drones over the Russian-annexed Crimean Peninsula. There were no casualties and no damage as a result of the attempted attack early on Saturday morning, the defence ministry said on the Telegram messaging app. Fourteen drones were destroyed by air defence systems and six were suppressed by electronic warfare, the ministry said. It was not immediately clear what was the target of the reported attacks on the peninsula. Sergei Kryuchkov, an adviser to the Russia-installed governor of Crimea, said earlier that air defence systems were engaged in repelling air attacks in different parts of the peninsula.

It is common practice to apply padding prior to convolution operations to preserve the resolution of feature-maps in Convolutional Neural Networks (CNN). While many alternatives exist, this is often achieved by adding a border of zeros around the inputs. In this work, we show that adversarial attacks often result in perturbation anomalies at the image boundaries, which are the areas where padding is used. Consequently, we aim to provide an analysis of the interplay between padding and adversarial attacks and seek an answer to the question of how different padding modes (or their absence) affect adversarial robustness in various scenarios.

Autonomous navigation in offroad environments has been extensively studied in the robotics field. However, navigation in covert situations where an autonomous vehicle needs to remain hidden from outside observers remains an underexplored area. In this paper, we propose a novel Deep Reinforcement Learning (DRL) based algorithm, called CoverNav, for identifying covert and navigable trajectories with minimal cost in offroad terrains and jungle environments in the presence of observers. CoverNav focuses on unmanned ground vehicles seeking shelters and taking covers while safely navigating to a predefined destination. Our proposed DRL method computes a local cost map that helps distinguish which path will grant the maximal covertness while maintaining a low cost trajectory using an elevation map generated from 3D point cloud data, the robot's pose, and directed goal information. CoverNav helps robot agents to learn the low elevation terrain using a reward function while penalizing it proportionately when it experiences high elevation. If an observer is spotted, CoverNav enables the robot to select natural obstacles (e.g., rocks, houses, disabled vehicles, trees, etc.) and use them as shelters to hide behind. We evaluate CoverNav using the Unity simulation environment and show that it guarantees dynamically feasible velocities in the terrain when fed with an elevation map generated by another DRL based navigation algorithm. Additionally, we evaluate CoverNav's effectiveness in achieving a maximum goal distance of 12 meters and its success rate in different elevation scenarios with and without cover objects. We observe competitive performance comparable to state of the art (SOTA) methods without compromising accuracy.

Hyper-relational knowledge graphs (HKGs) extend standard knowledge graphs by associating attribute-value qualifiers to triples, which effectively represent additional fine-grained information about its associated triple. Hyper-relational knowledge graph completion (HKGC) aims at inferring unknown triples while considering its qualifiers. Most existing approaches to HKGC exploit a global-level graph structure to encode hyper-relational knowledge into the graph convolution message passing process. However, the addition of multi-hop information might bring noise into the triple prediction process. To address this problem, we propose HyperFormer, a model that considers local-level sequential information, which encodes the content of the entities, relations and qualifiers of a triple. More precisely, HyperFormer is composed of three different modules: an entity neighbor aggregator module allowing to integrate the information of the neighbors of an entity to capture different perspectives of it; a relation qualifier aggregator module to integrate hyper-relational knowledge into the corresponding relation to refine the representation of relational content; a convolution-based bidirectional interaction module based on a convolutional operation, capturing pairwise bidirectional interactions of entity-relation, entity-qualifier, and relation-qualifier. realize the depth perception of the content related to the current statement. Furthermore, we introduce a Mixture-of-Experts strategy into the feed-forward layers of HyperFormer to strengthen its representation capabilities while reducing the amount of model parameters and computation. Extensive experiments on three well-known datasets with four different conditions demonstrate HyperFormer's effectiveness. Datasets and code are available at https://github.com/zhiweihu1103/HKGC-HyperFormer.

A millennium of endeavors to fully recognize and foresee the evolution of dynamic environments has produced many mathematical models for forecasting, and information-gathering techniques, but also exceptionally complicated computational systems. Predefined complicated realities called hyperchaotic frameworks [1] demonstrate unpredictable sequences of behavior over time and sometimes defy standards. These events' temporal and spatial relationships can be compared to physiological kinetics [2]. Several complicated frameworks are currently developed to comprehend spontaneous incidents, their erratic conduct, and how changing the circumstances of actual events may result in an unanticipated shift in the result. Over the duration of the past couple of eons, the objective of being able to understand and anticipate unpredictable actions has been accomplished with the aid of innovations in technology [3] and fundamental principles [4].

Deep neural networks (DNNs) have gained prominence in various applications, such as classification, recognition, and prediction, prompting increased scrutiny of their properties. A fundamental attribute of traditional DNNs is their vulnerability to modifications in input data, which has resulted in the investigation of adversarial attacks. These attacks manipulate the data in order to mislead a DNN. This study aims to challenge the efficacy and generalization of contemporary defense mechanisms against adversarial attacks. Specifically, we explore the hypothesis proposed by Ilyas et. al, which posits that DNN image features can be either robust or non-robust, with adversarial attacks targeting the latter. This hypothesis suggests that training a DNN on a dataset consisting solely of robust features should produce a model resistant to adversarial attacks. However, our experiments demonstrate that this is not universally true. To gain further insights into our findings, we analyze the impact of adversarial attack norms on DNN representations, focusing on samples subjected to $L_2$ and $L_{\infty}$ norm attacks. Further, we employ canonical correlation analysis, visualize the representations, and calculate the mean distance between these representations and various DNN decision boundaries. Our results reveal a significant difference between $L_2$ and $L_{\infty}$ norms, which could provide insights into the potential dangers posed by $L_{\infty}$ norm attacks, previously underestimated by the research community.

Recently developed large language models have achieved remarkable success in generating fluent and coherent text. However, these models often tend to 'hallucinate' which critically hampers their reliability. In this work, we address this crucial problem and propose an approach that actively detects and mitigates hallucinations during the generation process. Specifically, we first identify the candidates of potential hallucination leveraging the model's logit output values, check their correctness through a validation procedure, mitigate the detected hallucinations, and then continue with the generation process. Through extensive experiments with GPT-3.5 (text-davinci-003) on the 'article generation task', we first demonstrate the individual efficacy of our detection and mitigation techniques. Specifically, the detection technique achieves a recall of ~88% and the mitigation technique successfully mitigates 57.6% of the correctly detected hallucinations. Importantly, our mitigation technique does not introduce new hallucinations even in the case of incorrectly detected hallucinations, i.e., false positives. Then, we show that the proposed active detection and mitigation approach successfully reduces the hallucinations of the GPT-3.5 model from 47.5% to 14.5% on average. We further demonstrate the effectiveness and wide applicability of our approach through additional studies including performance on different types of questions (multi-hop and false premise questions) and with another LLM from a different model family (Vicuna). In summary, our work contributes to improving the reliability and trustworthiness of large language models, a crucial step en route to enabling their widespread adoption in real-world applications.