A recent line of work in mechanistic interpretability has focused on reverse-engineering the computation performed by neural networks trained on the binary operation of finite groups. We investigate the internals of one-hidden-layer neural networks trained on this task, revealing previously unidentified structure and producing a more complete description of such models that unifies the explanations of previous works. Notably, these models approximate equivariance in each input argument. We verify that our explanation applies to a large fraction of networks trained on this task by translating it into a compact proof of model performance, a quantitative evaluation of model understanding. In particular, our explanation yields a guarantee of model accuracy that runs in 30% the time of brute force and gives a >=95% accuracy bound for 45% of the models we trained. We were unable to obtain nontrivial non-vacuous accuracy bounds using only explanations from previous works.

The Israeli military "repeatedly" fired at UNIFIL headquarters and positions in southern Lebanon, injuring two members of the peacekeeping force, the United Nations says, as Israel presses on with its assault on Hezbollah. UNIFIL – the UN Interim Force in Lebanon – said on Thursday that two of its peacekeepers were injured after an Israeli tank "fired its weapon" at a guard tower at the group's headquarters, located in the border area town of Naqoura. The attack on the tower had caused the two peacekeepers to fall. "The injuries are fortunately, this time, not serious, but they remain in hospital," said UNIFIL in a statement. The Israeli soldiers also fired on a UN position – named "1-31"- in the village of Labbouneh, "hitting the entrance to the bunker where peacekeepers were sheltering, and damaging vehicles and a communications system", it said. The peacekeeping force reported that it had observed an Israeli military drone flying inside the UN position up to the bunker entrance.

In lifelong learning systems based on artificial neural networks, one of the biggest obstacles is the inability to retain old knowledge as new information is encountered. This phenomenon is known as catastrophic forgetting. In this paper, we propose a new kind of connectionist architecture, the Sequential Neural Coding Network, that is robust to forgetting when learning from streams of data points and, unlike networks of today, does not learn via the popular back-propagation of errors. Grounded in the neurocognitive theory of predictive coding, our model adapts its synapses in a biologically-plausible fashion while another neural system learns to direct and control this cortex-like structure, mimicking some of the task-executive control functionality of the basal ganglia. In our experiments, we demonstrate that our self-organizing system experiences significantly less forgetting compared to standard neural models, outperforming a swath of previously proposed methods, including rehearsal/data buffer-based methods, on both standard (SplitMNIST, Split Fashion MNIST, etc.) and custom benchmarks even though it is trained in a stream-like fashion.

The claims could have been taken word-for-word from any one of numerous US far-right websites in recent months. "Reports are surfacing suggesting that [lawmakers] may have been involved in transporting large numbers of refugees and immigration applicants to polling stations to secure votes for individual candidates," the author of the article claimed. This wasn't a conspiracist asserting that Honduran migrants are being imported into the US to replace swing-state Republican voters, though; the claim came from a website called The Irish Channel. A new report published on Tuesday by researchers at the Institute for Strategic Dialogue outlines how the website has used generative AI to create articles that have been "heavily influenced by similar election denial efforts in the US." The anti-immigrant narrative, based on made-up quotes and fabricated academics, is just one of the conspiracies imported wholesale into Ireland from the US in recent months.

The research was partially supported by the Paul and Marcia Wythes Center on Contemporary China and Office of Population Research at Princeton University. We are grateful to Wen Liu, Gou Wu, and Dean Minello for their excellent research assistance. The ideas expressed herein are those of the authors. Abstract Liking it or not, ready or not, we are likely to enter a new phase of human history in which Artificial Intelligence (AI) will dominate economic production and social life - the AI Revolution. Before the actual arrival of the AI Revolution, it is time for us to speculate on how AI will impact the social world. In this article, we focus on the social impact of generative LLMbased AI (GELLMAI), discussing societal factors that contribute to its technological development and its potential roles in enhancing both between-country and within-country social inequality. There are good indications that the US and China will lead the field and will be the main competitors for domination of AI in the world. We conjecture the AI Revolution will likely give rise to a post-knowledge society in which knowledge per se will become less important than in today's world. Instead, individual relationships and social identity will become more important. With the advent of Generative Large Language Model (LLM)-based Artificial Intelligence (AI) tools such as ChatGPT from OpenAI and Bard from Google, it is natural to wonder about the social impact of this technology. In the remainder of this paper, we will refer to generative LLMbased AI simply as GELLMAI. The main objective of this paper is to explore, tentatively, the social impact of GELLMAI. While the question about the social impact of GELLMAI is undoubtedly important, any answers must be tentative and speculative at this point. We are still in the early stages of GELLMAI and may need to wait years, perhaps even decades, to fully understand its social implications. However, drawing from our experiences with past technologies in history, our current understanding of GELLMAI, empirical knowledge about the social world, and sociological reasoning, we can engage in preliminary and speculative discussions. We offer our account below. We believe that the social impact of GELLMAI is enormous, with the potential to revolutionize not only the production of goods and services but also to fundamentally alter the organization of human societies and the nature of daily life.

Deep learning has proven to be a highly effective tool for a wide range of applications, significantly when leveraging the power of multi-loss functions to optimize performance on multiple criteria simultaneously. However, optimal selection and weighting loss functions in deep learning tasks can significantly influence model performance, yet manual tuning of these functions is often inefficient and inflexible. We propose a novel framework called dynamic memory fusion for adaptive multi-loss function penalizing in real-time to address this. This framework leverages historical loss values data to dynamically adjust the weighting of multiple loss functions throughout the training process. Additionally, this framework integrates an auxiliary loss function to enhance model performance in the early stages. To further research horizons, we introduce the class-balanced dice loss function, designed to address class imbalance by prioritizing underrepresented classes. Experiments on breast ultrasound datasets demonstrate that the framework improves segmentation performance across various metrics. These results demonstrate the effectiveness of our proposed framework in ensuring that the model dynamically adjusts its focus to prioritize the most relevant criteria, leading to improved performance in evolving environments. The source code for our proposed methodology is publicly available on GitHub.

Images are capable of conveying emotions, but emotional experience is highly subjective. Advances in artificial intelligence have enabled the generation of images based on emotional descriptions. However, the level of agreement between the generative images and human emotional responses has not yet been evaluated. To address this, 20 artistic landscapes were generated using StyleGAN2-ADA. Four variants evoking positive emotions (contentment, amusement) and negative emotions (fear, sadness) were created for each image, resulting in 80 pictures. An online questionnaire was designed using this material, in which 61 observers classified the generated images. Statistical analyses were performed on the collected data to determine the level of agreement among participants, between the observer's responses, and the AI-generated emotions. A generally good level of agreement was found, with better results for negative emotions. However, the study confirms the subjectivity inherent in emotional evaluation.

Some automatic systems have ASR-based model has been fine-tuned for automated speech shown robust performance and stability by learning from expert disorder quality assessment tasks, yielding impressive results decisions [6, 7]. and setting a new baseline for Head and Neck Cancer speech contexts. This demonstrates that the ASR dimension from In 2024, Nguyen et al. [8] introduced a system that Wav2Vec2 closely aligns with assessment dimensions. Despite leverages the Automatic Speech Recognition (ASR) based its effectiveness, this system remains a black box with Wav2Vec2 model [9], known for its strong capability in no clear interpretation of the connection between the model learning speech representations. This approach compared ASR dimension and clinical assessments. This paper presents self-supervised learning (SSL) and the ASR dimension for the first analysis of this baseline model for speech quality assessment, speech quality assessment. It is shown that the fine-tuning focusing on intelligibility and severity tasks.

Maternal mortality remains a significant global public health challenge. One promising approach to reducing maternal deaths occurring during facility-based childbirth is through early warning systems, which require the consistent monitoring of mothers' vital signs after giving birth. Wireless vital sign monitoring devices offer a labor-efficient solution for continuous monitoring, but their scarcity raises the critical question of how to allocate them most effectively. We devise an allocation algorithm for this problem by modeling it as a variant of the popular Restless Multi-Armed Bandit (RMAB) paradigm. In doing so, we identify and address novel, previously unstudied constraints unique to this domain, which render previous approaches for RMABs unsuitable and significantly increase the complexity of the learning and planning problem. To overcome these challenges, we adopt the popular Proximal Policy Optimization (PPO) algorithm from reinforcement learning to learn an allocation policy by training a policy and value function network. We demonstrate in simulations that our approach outperforms the best heuristic baseline by up to a factor of $4$.

In recent times, a considerable number of research studies have been carried out to address the issue of Missing Value Imputation (MVI). MVI aims to provide a primary solution for datasets that have one or more missing attribute values. The advancements in Artificial Intelligence (AI) drive the development of new and improved machine learning (ML) algorithms and methods. The advancements in ML have opened up significant opportunities for effectively imputing these missing values. The main objective of this article is to conduct a comprehensive and rigorous review, as well as analysis, of the state-of-the-art ML applications in MVI methods. This analysis seeks to enhance researchers' understanding of the subject and facilitate the development of robust and impactful interventions in data preprocessing for Data Analytics. The review is performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) technique. More than 100 articles published between 2014 and 2023 are critically reviewed, considering the methods and findings. Furthermore, the latest literature is examined to scrutinize the trends in MVI methods and their evaluation. The accomplishments and limitations of the existing literature are discussed in detail. The survey concludes by identifying the current gaps in research and providing suggestions for future research directions and emerging trends in related fields of interest.