Artificial Intelligence (AI) plays a significant role in enhancing the capabilities of defense systems, revolutionizing strategic decision-making, and shaping the future landscape of military operations. Neuro-Symbolic AI is an emerging approach that leverages and augments the strengths of neural networks and symbolic reasoning. These systems have the potential to be more impactful and flexible than traditional AI systems, making them well-suited for military applications. This paper comprehensively explores the diverse dimensions and capabilities of Neuro-Symbolic AI, aiming to shed light on its potential applications in military contexts. We investigate its capacity to improve decision-making, automate complex intelligence analysis, and strengthen autonomous systems. We further explore its potential to solve complex tasks in various domains, in addition to its applications in military contexts. Through this exploration, we address ethical, strategic, and technical considerations crucial to the development and deployment of Neuro-Symbolic AI in military and civilian applications. Contributing to the growing body of research, this study represents a comprehensive exploration of the extensive possibilities offered by Neuro-Symbolic AI.

Local authorities and news outlets in northern Iraq's semi-autonomous Kurdish region have said that several people were killed in a Turkish drone strike on Friday, including two journalists. In an initial statement on Friday, the regional authorities said that a car belonging to the Kurdistan Workers' Party (PKK) was struck near the city of Sulaymaniyah, killing a senior PKK official, his guard and his driver. However, a later statement by the Kurdistan regional government's Deputy Prime Minister Qubad Talabani said that the attack targeted a group of journalists, two of whom were killed. "They were two women journalists, not members of an armed force to be a threat to the security and stability of any country or region," Talabani said in a statement. Reporters Without Borders (RSF), a press advocacy organisation, also released a statement denouncing the deaths of the two journalists, identified as 27-year-old Hero Baha'uddin and 40-year-old Golestan Tara from Sterk TV.

Rep. Alexandria Ocasio-Cortez, D-N.Y., emphasized the possibility that Vice President Kamala Harris will be the "first woman President of the United States" during a late-night appearance with Stephen Colbert. Rep. Alexandria Ocasio-Cortez, D-N.Y., predicted that Vice President Kamala Harris will be the "first woman President of the United States" during a late-night appearance on Thursday, conspicuously leaning into identity politics. Following the end of the Democratic National Convention, the progressive lawmaker went on CBS' "The Late Show with Stephen Colbert" and played up how remarkable it is that Harris could become president. We will have the first woman President of the United States in November," Ocasio-Cortez predicted to raucous applause. Rep. Alexandria Ocasio-Cortez, D-N.Y., predicts that Vice President Kamala Harris will be the first woman president. Playing up the drama in her declaration, the congresswoman talked about how she grew up only seeing depictions of female leadership in episodes of "Star Trek: Voyager" that she watched with her dad as a kid. But now that Harris has been nominated, that fantasy is one step closer to reality. "My dad felt it very important for me to watch this because he wanted me to see an example of a woman in leadership, and when I was a kid the only example of that was in science fiction." She continued, "And today represents a day where it has become our reality." The late-night show audience went wild at the prospect. She also had a welcome interviewer in Colbert, a rabid Democratic supporter who even moderated a fundraiser for President Biden before he was forced off the 2024 ticket. Colbert then asked the lawmaker about her statements earlier this year warning that the Democratic Party will not unite behind Harris if President Biden steps aside. Reading her quote, he said, "'If you think that there is a consensus among the people who want Joe Biden to leave that they will support Vice President Harris, you will be mistaken." The host then asked her, "Have you ever been happier to be wrong?" She replied, repeating the word, "Ecstatic" several times. CNN'S DANA BASH ARGUES DNC APPEALS TO MEN WHO ARE NOT SO'TESTOSTERONE-LADEN' Colbert continued, "You didn't think this would necessarily happen?" to which she said, "No, and I think it is important that this was not predestined, this was not predetermined." "Vice President Harris earned this nomination through her grit, her politics, through every bit of hard work.

From Julia Fox to Kamala Harris, many celebrities are embracing the'Brat Summer' trend. Now, a rather unexpected figure has hopped on Charli XCX's bandwagon - Boston Dynamics' humanoid robot, Atlas. Boston Dynamics has posted a new video showing the bot performing press-ups to Charli XCX's song, 360. The tech giant jokingly captioned the video'BRATlas', in reference to Charli XCX's album. 'Get that full burpee going, Atlas slackin!' one user joked, while another added: 'The robot is better at doing push-ups than me.'

Deep learning techniques have revolutionized image classification by mimicking human cognition and automating complex decision-making processes. However, the deployment of AI systems in the wild, especially in high-security domains such as defence, is curbed by the lack of explainability of the model. To this end, eXplainable AI (XAI) is an emerging area of research that is intended to explore the unexplained hidden black box nature of deep neural networks. This paper explores the application of the eXplainable Artificial Intelligence (XAI) tool to interpret the underwater image classification results, one of the first works in the domain to the best of our knowledge. Our study delves into the realm of SONAR image classification using a custom dataset derived from diverse sources, including the Seabed Objects KLSG dataset, the camera SONAR dataset, the mine SONAR images dataset, and the SCTD dataset. An extensive analysis of transfer learning techniques for image classification using benchmark Convolutional Neural Network (CNN) architectures such as VGG16, ResNet50, InceptionV3, DenseNet121, etc. is carried out. On top of this classification model, a post-hoc XAI technique, viz. Local Interpretable Model-Agnostic Explanations (LIME) are incorporated to provide transparent justifications for the model's decisions by perturbing input data locally to see how predictions change. Furthermore, Submodular Picks LIME (SP-LIME) a version of LIME particular to images, that perturbs the image based on the submodular picks is also extensively studied. To this end, two submodular optimization algorithms i.e. Quickshift and Simple Linear Iterative Clustering (SLIC) are leveraged towards submodular picks. The extensive analysis of XAI techniques highlights interpretability of the results in a more human-compliant way, thus boosting our confidence and reliability.

Assessing an AI system's behavior-particularly in Explainable AI Systems-is sometimes done empirically, by measuring people's abilities to predict the agent's next move-but how to perform such measurements? In empirical studies with humans, an obvious approach is to frame the task as binary (i.e., prediction is either right or wrong), but this does not scale. As output spaces increase, so do floor effects, because the ratio of right answers to wrong answers quickly becomes very small. The crux of the problem is that the binary framing is failing to capture the nuances of the different degrees of "wrongness." To address this, we begin by proposing three mathematical bases upon which to measure "partial wrongness." We then uses these bases to perform two analyses on sequential decision-making domains: the first is an in-lab study with 86 participants on a size-36 action space; the second is a re-analysis of a prior study on a size-4 action space. Other researchers adopting our operationalization of the prediction task and analysis methodology will improve the rigor of user studies conducted with that task, which is particularly important when the domain features a large output space.

Synthetic data generation is a promising technique to facilitate the use of sensitive data while mitigating the risk of privacy breaches. However, for synthetic data to be useful in downstream analysis tasks, it needs to be of sufficient quality. Various methods have been proposed to measure the utility of synthetic data, but their results are often incomplete or even misleading. In this paper, we propose using density ratio estimation to improve quality evaluation for synthetic data, and thereby the quality of synthesized datasets. We show how this framework relates to and builds on existing measures, yielding global and local utility measures that are informative and easy to interpret. We develop an estimator which requires little to no manual tuning due to automatic selection of a nonparametric density ratio model. Through simulations, we find that density ratio estimation yields more accurate estimates of global utility than established procedures. A real-world data application demonstrates how the density ratio can guide refinements of synthesis models and can be used to improve downstream analyses. We conclude that density ratio estimation is a valuable tool in synthetic data generation workflows and provide these methods in the accessible open source R-package densityratio.

The emergence of Generative Artificial Intelligence (AI) and Large Language Models (LLMs) has marked a new era of Natural Language Processing (NLP), introducing unprecedented capabilities that are revolutionizing various domains. This paper explores the current state of these cutting-edge technologies, demonstrating their remarkable advancements and wide-ranging applications. Our paper contributes to providing a holistic perspective on the technical foundations, practical applications, and emerging challenges within the evolving landscape of Generative AI and LLMs. We believe that understanding the generative capabilities of AI systems and the specific context of LLMs is crucial for researchers, practitioners, and policymakers to collaboratively shape the responsible and ethical integration of these technologies into various domains. Furthermore, we identify and address main research gaps, providing valuable insights to guide future research endeavors within the AI research community.

The quadratic computational complexity in the self-attention mechanism of popular transformer architectures poses significant challenges for training and inference, particularly in terms of efficiency and memory requirements. Towards addressing these challenges, this paper introduces a novel fast computation method for gradient calculation in multi-layer transformer models. Our approach enables the computation of gradients for the entire multi-layer transformer model in almost linear time $n^{1+o(1)}$, where $n$ is the input sequence length. This breakthrough significantly reduces the computational bottleneck associated with the traditional quadratic time complexity. Our theory holds for any loss function and maintains a bounded approximation error across the entire model. Furthermore, our analysis can hold when the multi-layer transformer model contains many practical sub-modules, such as residual connection, casual mask, and multi-head attention. By improving the efficiency of gradient computation in large language models, we hope that our work will facilitate the more effective training and deployment of long-context language models based on our theoretical results.

Providing security for information is highly critical in the current era with devices enabled with smart technology, where assuming a day without the internet is highly impossible. Fast internet at a cheaper price, not only made communication easy for legitimate users but also for cybercriminals to induce attacks in various dimensions to breach privacy and security. Cybercriminals gain illegal access and breach the privacy of users to harm them in multiple ways. Malware is one such tool used by hackers to execute their malicious intent. Development in AI technology is utilized by malware developers to cause social harm. In this work, we intend to show how Artificial Intelligence and Machine learning can be used to detect and mitigate these cyber-attacks induced by malware in specific obfuscated malware. We conducted experiments with memory feature engineering on memory analysis of malware samples. Binary classification can identify whether a given sample is malware or not, but identifying the type of malware will only guide what next step to be taken for that malware, to stop it from proceeding with its further action. Hence, we propose a multi-class classification model to detect the three types of obfuscated malware with an accuracy of 89.07% using the Classic Random Forest algorithm. To the best of our knowledge, there is very little amount of work done in classifying multiple obfuscated malware by a single model. We also compared our model with a few state-of-the-art models and found it comparatively better.