arXiv Computer Science @arxiv_cs@qoto.org

This paper provides an overview of the intricate relationship between social dynamics, technological advancements, and pioneering figures in the fields of cybernetics and artificial intelligence. It explores the impact of collaboration and interpersonal relationships among key scientists, such as McCulloch, Wiener, Pitts, and Rosenblatt, on the development of cybernetics and neural networks. It also discusses the contested attribution of credit for important innovations like the backpropagation algorithm and the potential consequences of unresolved debates within emerging scientific domains. It emphasizes how interpretive flexibility, public perception, and the influence of prominent figures can shape the trajectory of a new field. It highlights the role of funding, media attention, and alliances in determining the success and recognition of various research approaches. Additionally, it points out the missed opportunities for collaboration and integration between symbolic AI and neural network researchers, suggesting that a more unified approach may be possible in today's era without the historical baggage of past debates.

As AI takes on a greater role in the modern world, it is essential to ensure that AI models can overcome decision uncertainty and remain aligned with human morality and interests. This research paper proposes a method for improving the decision-making of language models (LMs) via Automated Parliaments (APs) - constructs made of AI delegates each representing a certain perspective. Delegates themselves consist of three AI models: generators, modifiers, and evaluators. We specify two mechanisms for producing optimal solutions: the Simultaneous Modification mechanism for response creation and an evaluation mechanism for fairly assessing solutions. The overall process begins when each generator creates a response aligned with its delegate's theory. The modifiers alter all other responses to make them more self-aligned. The evaluators collectively assess the best end response. Finally, the modifiers and generators learn from feedback from the evaluators. In our research, we tested the evaluation mechanism, comparing the use of single-value zero-shot prompting and AP few-shot prompting in evaluating morally contentious scenarios. We found that the AP architecture saw a 57.3% reduction in its loss value compared to the baseline. We conclude by discussing some potential applications of APs and specifically their potential impact when implemented as Automated Moral Parliaments.

With constant growth of civilization and modernization of cities all across the world since past few centuries smart traffic management of vehicles is one of the most sorted after problem by research community. It is a challenging problem in computer vision and artificial intelligence domain. Smart traffic management basically involves segmentation of vehicles, estimation of traffic density and tracking of vehicles. The vehicle segmentation from traffic videos helps realization of niche applications such as monitoring of speed and estimation of traffic. When occlusions, background with clutters and traffic with density variations are present, this problem becomes more intractable in nature. Keeping this motivation in this research work, we investigate Faster R-CNN based deep learning method towards segmentation of vehicles. This problem is addressed in four steps viz minimization with adaptive background model, Faster R-CNN based subnet operation, Faster R-CNN initial refinement and result optimization with extended topological active nets. The computational framework uses ideas of adaptive background modeling. It also addresses shadow and illumination related issues. Higher segmentation accuracy is achieved through topological active net deformable models. The topological and extended topological active nets help to achieve stated deformations. Mesh deformation is achieved with minimization of energy. The segmentation accuracy is improved with modified version of extended topological active net. The experimental results demonstrate superiority of this computational framework

We develop an advanced approach for extending Gaussian Differential Privacy (GDP) to general Riemannian manifolds. The concept of GDP stands out as a prominent privacy definition that strongly warrants extension to manifold settings, due to its central limit properties. By harnessing the power of the renowned Bishop-Gromov theorem in geometric analysis, we propose a Riemannian Gaussian distribution that integrates the Riemannian distance, allowing us to achieve GDP in Riemannian manifolds with bounded Ricci curvature. To the best of our knowledge, this work marks the first instance of extending the GDP framework to accommodate general Riemannian manifolds, encompassing curved spaces, and circumventing the reliance on tangent space summaries. We provide a simple algorithm to evaluate the privacy budget $μ$ on any one-dimensional manifold and introduce a versatile Markov Chain Monte Carlo (MCMC)-based algorithm to calculate $μ$ on any Riemannian manifold with constant curvature. Through simulations on one of the most prevalent manifolds in statistics, the unit sphere $S^d$, we demonstrate the superior utility of our Riemannian Gaussian mechanism in comparison to the previously proposed Riemannian Laplace mechanism for implementing GDP.

Cognition is a core part of and a common topic among philosophy of mind, psychology, neuroscience, AI, and cognitive science. Through a mechanistic lens, I propose a framework of defining, modeling, and analyzing cognition mechanisms. Firstly, appropriate terms are introduced and used in explanations related to the framework and within the definition of a mechanism. I implicitly contend that this terminology essentially characterizes a conceptual world required for discussions in this paper. Secondly, a mathematical model of a mechanism based on directed graphs is proposed. Thirdly, the definition of a base necessary for a mechanism to be classified as a cognition mechanism is proposed. I argue that the cognition base has the features of the cognition self of humans. Fourthly, three ways to mechanistically look at mechanisms is defined and specific instances of them are suggested. Fifthly, standards for visualization and presentation of mechanisms, cognition mechanisms, and the instances to mechanistically look at them are suggested and used to analyze cognition mechanisms through appropriate examples. Finally, the features of this paper are discussed and prospects of further development of the proposed framework are briefly expressed.

Despite being (pre)trained on a massive amount of data, state-of-the-art video-language alignment models are not robust to semantically-plausible contrastive changes in the video captions. Our work addresses this by identifying a broad spectrum of contrast misalignments, such as replacing entities, actions, and flipping event order, which alignment models should be robust against. To this end, we introduce the VideoCon, a video-language alignment dataset constructed by a large language model that generates plausible contrast video captions and explanations for differences between original and contrast video captions. Then, a generative video-language model is finetuned with VideoCon to assess video-language entailment and generate explanations. Our VideoCon-based alignment model significantly outperforms current models. It exhibits a 12-point increase in AUC for the video-language alignment task on human-generated contrast captions. Finally, our model sets new state of the art zero-shot performance in temporally-extensive video-language tasks such as text-to-video retrieval (SSv2-Temporal) and video question answering (ATP-Hard). Moreover, our model shows superior performance on novel videos and human-crafted captions and explanations. Our code and data are available at https://github.com/Hritikbansal/videocon.

In recent years, modeling evolving knowledge over temporal knowledge graphs (TKGs) has become a heated topic. Various methods have been proposed to forecast links on TKGs. Most of them are embedding-based, where hidden representations are learned to represent knowledge graph (KG) entities and relations based on the observed graph contexts. Although these methods show strong performance on traditional TKG forecasting (TKGF) benchmarks, they naturally face a strong challenge when they are asked to model the unseen zero-shot relations that has no prior graph context. In this paper, we try to mitigate this problem as follows. We first input the text descriptions of KG relations into large language models (LLMs) for generating relation representations, and then introduce them into embedding-based TKGF methods. LLM-empowered representations can capture the semantic information in the relation descriptions. This makes the relations, whether seen or unseen, with similar semantic meanings stay close in the embedding space, enabling TKGF models to recognize zero-shot relations even without any observed graph context. Experimental results show that our approach helps TKGF models to achieve much better performance in forecasting the facts with previously unseen relations, while still maintaining their ability in link forecasting regarding seen relations.

Recent advancements in Neural Audio Synthesis (NAS) have outpaced the development of standardized evaluation methodologies and tools. To bridge this gap, we introduce AquaTk, an open-source Python library specifically designed to simplify and standardize the evaluation of NAS systems. AquaTk offers a range of audio quality metrics, including a unique Python implementation of the basic PEAQ algorithm, and operates in multiple modes to accommodate various user needs.

The Transformer-based deep networks have increasingly shown significant advantages over CNNs. Some existing work has applied it in the field of wildfire recognition or detection. However, we observed that the vanilla Transformer is not friendly for extracting smoke features. Because low-level information such as color, transparency and texture is very important for smoke recognition, and transformer pays more attention to the semantic relevance between middle- or high-level features, and is not sensitive to the subtle changes of low-level features along the space. To solve this problem, we propose the Cross Contrast Patch Embedding(CCPE) module based on the Swin Transformer, which uses the multi-scales spatial frequency contrast information in both vertical and horizontal directions to improve the discrimination of the network on the underlying details. The fuzzy boundary of smoke makes the positive and negative label assignment for instances in a dilemma, which is another challenge for wildfires detection. To solve this problem, a Separable Negative Sampling Mechanism(SNSM) is proposed. By using two different negative instance sampling strategies on positive images and negative images respectively, the problem of supervision signal confusion caused by label diversity in the process of network training is alleviated. This paper also releases the RealFire Test, the largest real wildfire test set so far, to evaluate the proposed method and promote future research. It contains 50,535 images from 3,649 video clips. The proposed method has been extensively tested and evaluated on RealFire Test dataset, and has a significant performance improvement compared with the baseline detection models.

Large language models (LLMs) have demonstrated remarkable capabilities in solving complex open-domain tasks, guided by comprehensive instructions and demonstrations provided in the form of prompts. However, these prompts can be lengthy, often comprising hundreds of lines and thousands of tokens, and their design often requires considerable human effort. Recent research has explored automatic prompt engineering for short prompts, typically consisting of one or a few sentences. However, the automatic design of long prompts remains a challenging problem due to its immense search space. In this paper, we investigate the performance of greedy algorithms and genetic algorithms for automatic long prompt engineering. We demonstrate that a simple greedy approach with beam search outperforms other methods in terms of search efficiency. Moreover, we introduce two novel techniques that utilize search history to enhance the effectiveness of LLM-based mutation in our search algorithm. Our results show that the proposed automatic long prompt engineering algorithm achieves an average of 9.2% accuracy gain on eight tasks in Big Bench Hard, highlighting the significance of automating prompt designs to fully harness the capabilities of LLMs.

Non-line-of-sight (NLOS) imaging aims to reconstruct partially or completely occluded scenes. Recent approaches have demonstrated high-quality reconstructions of complex scenes with arbitrary reflectance, occlusions, and significant multi-path effects. However, previous works focused on surface scattering only, which reduces the generality in more challenging scenarios such as scenes submerged in scattering media. In this work, we investigate current state-of-the-art NLOS imaging methods based on phasor fields to reconstruct scenes submerged in scattering media. We empirically analyze the capability of phasor fields in reconstructing complex synthetic scenes submerged in thick scattering media. We also apply the method to real scenes, showing that it performs similarly to recent diffuse optical tomography methods.

The widespread integration of autoregressive-large language models (AR-LLMs), such as ChatGPT, across established applications, like search engines, has introduced critical vulnerabilities with uniquely scalable characteristics. In this commentary, we analyse these vulnerabilities, their dependence on natural language as a vector of attack, and their challenges to cybersecurity best practices. We offer recommendations designed to mitigate these challenges.

Autonomous driving is a challenging task that has gained broad attention from both academia and industry. Current solutions using convolutional neural networks require large amounts of computational resources, leading to high power consumption. Spiking neural networks (SNNs) provide an alternative computation model to process information and make decisions. This biologically plausible model has the advantage of low latency and energy efficiency. Recent work using SNNs for autonomous driving mostly focused on simple tasks like lane keeping in simplified simulation environments. This project studies SNNs on photo-realistic driving scenes in the CARLA simulator, which is an important step toward using SNNs on real vehicles. The efficacy and generalizability of the method will be investigated.

'Digital humans' are digital reproductions of humans powered by artificial intelligence (AI) and capable of communicating and forming emotional bonds. The value creation potential of digital humans is overlooked due to the limitations of digital human technologies. This article explores the value creation potential and the value realisation limitations of digital humans. The analysis is based on a review of 62 articles retrieved from the Web of Science database. The analysis suggests that digital humans have the potential to alleviate labour and skill shortages, reduce the natural human element in high-risk tasks, avoid design errors, improve the ergonomics of products and workplaces, and provide guidance and emotional support, all of which will benefit natural humans in the workplace. However, technical limits, evolving understanding of digital humans, the social significance and acceptance of digital humans, ethical considerations, and the adjustment of legal tradition limit the value realisation. This review suggests that digital humans' perceived usefulness and ease of development determine organisations' willingness to utilise this technology. Overcoming the limitations, which still involve engineering challenges and a change in how they are perceived, will positively affect realising the value potential of digital humans in organisations.

Recent decisions by leading AI labs to either open-source their models or to restrict access to their models has sparked debate about whether, and how, increasingly capable AI models should be shared. Open-sourcing in AI typically refers to making model architecture and weights freely and publicly accessible for anyone to modify, study, build on, and use. This offers advantages such as enabling external oversight, accelerating progress, and decentralizing control over AI development and use. However, it also presents a growing potential for misuse and unintended consequences. This paper offers an examination of the risks and benefits of open-sourcing highly capable foundation models. While open-sourcing has historically provided substantial net benefits for most software and AI development processes, we argue that for some highly capable foundation models likely to be developed in the near future, open-sourcing may pose sufficiently extreme risks to outweigh the benefits. In such a case, highly capable foundation models should not be open-sourced, at least not initially. Alternative strategies, including non-open-source model sharing options, are explored. The paper concludes with recommendations for developers, standard-setting bodies, and governments for establishing safe and responsible model sharing practices and preserving open-source benefits where safe.

Intuitively, students who start a homework assignment earlier and spend more time on it should receive better grades on the assignment. However, existing literature on the impact of time spent on homework is not clear-cut and comes mostly from K-12 education. It is not clear that these prior studies can inform coursework in deep learning due to differences in demographics, as well as the computational time needed for assignments to be completed. We study this problem in a post-hoc study of three semesters of a deep learning course at the University of Maryland, Baltimore County (UMBC), and develop a hierarchical Bayesian model to help make principled conclusions about the impact on student success given an approximate measure of the total time spent on the homework, and how early they submitted the assignment. Our results show that both submitting early and spending more time positively relate with final grade. Surprisingly, the value of an additional day of work is apparently equal across students, even when some require less total time to complete an assignment.

As global life expectancy improves, the population of the elderly, persons that are aged 65 years and older, is steadily increasing as well. However, with aging populations a greater prevalence of cognitive impairment has emerged, ranging from mild dementia to severe dementias such as Alzheimer's disease due to genetic and environmental influences, among others. The purpose of this research was to develop a computational algorithm to predict the risk of developing cognitive disorders using a dual machine learning and systems biology approach. The proposed method CAPCODRE (Computational Approach to Predict COgnitive Disorder Risk for the Elderly) utilized air, water, and noise environmental pollution data coupled with a gene-protein interaction network, in addition to cognitive impairment hospitalizations in the United States to create a tailorable, interactive network able to predict risk of dementia and Alzheimer's disease. This network was inputted into a random selection optimization algorithm to select optimal training parameters for training via k-nearest neighbors, random forest regression, and decision trees. CAPCODRE was successfully able to predict and model risk of cognitive health issues through measures of specificity, sensitivity, and accuracy of >85%. The algorithm was integrated into an app for users to receive personalized predictions based on their medical history and geographic location. CAPCODRE can point to the extent of environmental pollution on human health and reveal steps to mitigate risk of severe cognitive impairment. This research also has the potential to address racial disparities in cognitive disorder diagnoses and treatment, promoting more equitable and accessible care.

The adoption of generative AI technologies is swiftly expanding. Services employing both linguistic and mul-timodal models are evolving, offering users increasingly precise responses. Consequently, human reliance on these technologies is expected to grow rapidly. With the premise that people will be impacted by the output of AI, we explored approaches to help AI output produce better results. Initially, we evaluated how contemporary AI services competitively meet user needs, then examined society's depiction as mirrored by Large Language Models (LLMs). We did a query experiment, querying about social conventions in various countries and eliciting a one-word response. We compared the LLMs' value judgments with public data and suggested an model of decision-making in value-conflicting scenarios which could be adopted for future machine value judgments. This paper advocates for a practical approach to using AI as a tool for investigating other remote worlds. This re-search has significance in implicitly rejecting the notion of AI making value judgments and instead arguing a more critical perspective on the environment that defers judgmental capabilities to individuals. We anticipate this study will empower anyone, regardless of their capacity, to receive safe and accurate value judgment-based out-puts effectively.

This study examines the key factors that affect European reactions to artificial intelligence (AI) in the context of both full and flawed democracies in Europe. Analysing a dataset of 4,006 respondents, categorised into full democracies and flawed democracies based on the Democracy Index developed by the Economist Intelligence Unit (EIU), this research identifies crucial factors that shape European attitudes toward AI in these two types of democracies. The analysis reveals noteworthy findings. Firstly, it is observed that flawed democracies tend to exhibit higher levels of trust in government entities compared to their counterparts in full democracies. Additionally, individuals residing in flawed democracies demonstrate a more positive attitude toward AI when compared to respondents from full democracies. However, the study finds no significant difference in AI awareness between the two types of democracies, indicating a similar level of general knowledge about AI technologies among European citizens. Moreover, the study reveals that trust in AI measures, specifically "Trust AI Solution", does not significantly vary between full and flawed democracies. This suggests that despite the differences in democratic quality, both types of democracies have similar levels of confidence in AI solutions.

COVID-19 pandemic has irreversibly changed the attitude towards office presence. While previously remote workers were met with skepticism and distrust, today the same applies to companies prohibiting remote working. Albeit many workspaces are half empty. In this paper, we offer insights into the role of the office, corporate policies and actions regarding remote work in eight companies: Ericsson, Knowit, SpareBank 1 Utvikling, Spotify, Storebrand, Telenor, Company-X, Company-Y, and their sites in Sweden, Norway and the UK. Our findings are twofold. First, we found that companies indeed struggle with office presence and a large share of corporate space (35-67%) is underutilized. Second, we found that the main motivator for office presence is Connection and community, followed by Material offerings, Preference and Duty. Finally, we summarize actionable advice to promote onsite work, which is likely to help many other companies to rejuvenate life in their offices.