718 research outputs found

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    AI: Limits and Prospects of Artificial Intelligence

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    The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence

    Video Summarization Using Unsupervised Deep Learning

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    In this thesis, we address the task of video summarization using unsupervised deep-learning architectures. Video summarization aims to generate a short summary by selecting the most informative and important frames (key-frames) or fragments (key-fragments) of the full-length video, and presenting them in temporally-ordered fashion. Our objective is to overcome observed weaknesses of existing video summarization approaches that utilize RNNs for modeling the temporal dependence of frames, related to: i) the small influence of the estimated frame-level importance scores in the created video summary, ii) the insufficiency of RNNs to model long-range frames' dependence, and iii) the small amount of parallelizable operations during the training of RNNs. To address the first weakness, we propose a new unsupervised network architecture, called AC-SUM-GAN, which formulates the selection of important video fragments as a sequence generation task and learns this task by embedding an Actor-Critic model in a Generative Adversarial Network. The feedback of a trainable Discriminator is used as a reward by the Actor-Critic model in order to explore a space of actions and learn a value function (Critic) and a policy (Actor) for video fragment selection. To tackle the remaining weaknesses, we investigate the use of attention mechanisms for video summarization and propose a new supervised network architecture, called PGL-SUM, that combines global and local multi-head attention mechanisms which take into account the temporal position of the video frames, in order to discover different modelings of the frames' dependencies at different levels of granularity. Based on the acquired experience, we then propose a new unsupervised network architecture, called CA-SUM, which estimates the frames' importance using a novel concentrated attention mechanism that focuses on non-overlapping blocks in the main diagonal of the attention matrix and takes into account the attentive uniqueness and diversity of the associated frames of the video. All the proposed architectures have been extensively evaluated on the most commonly-used benchmark datasets, demonstrating their competitiveness against other approaches and documenting the contribution of our proposals on advancing the current state-of-the-art on video summarization. Finally, we make a first attempt on producing explanations for the video summarization results. Inspired by relevant works in the Natural Language Processing domain, we propose an attention-based method for explainable video summarization and we evaluate the performance of various explanation signals using our CA-SUM architecture and two benchmark datasets for video summarization. The experimental results indicate the advanced performance of explanation signals formed using the inherent attention weights, and demonstrate the ability of the proposed method to explain the video summarization results using clues about the focus of the attention mechanism

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Advances in automatic terminology processing: methodology and applications in focus

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    A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.The information and knowledge era, in which we are living, creates challenges in many fields, and terminology is not an exception. The challenges include an exponential growth in the number of specialised documents that are available, in which terms are presented, and the number of newly introduced concepts and terms, which are already beyond our (manual) capacity. A promising solution to this ‘information overload’ would be to employ automatic or semi-automatic procedures to enable individuals and/or small groups to efficiently build high quality terminologies from their own resources which closely reflect their individual objectives and viewpoints. Automatic terminology processing (ATP) techniques have already proved to be quite reliable, and can save human time in terminology processing. However, they are not without weaknesses, one of which is that these techniques often consider terms to be independent lexical units satisfying some criteria, when terms are, in fact, integral parts of a coherent system (a terminology). This observation is supported by the discussion of the notion of terms and terminology and the review of existing approaches in ATP presented in this thesis. In order to overcome the aforementioned weakness, we propose a novel methodology in ATP which is able to extract a terminology as a whole. The proposed methodology is based on knowledge patterns automatically extracted from glossaries, which we considered to be valuable, but overlooked resources. These automatically identified knowledge patterns are used to extract terms, their relations and descriptions from corpora. The extracted information can facilitate the construction of a terminology as a coherent system. The study also aims to discuss applications of ATP, and describes an experiment in which ATP is integrated into a new NLP application: multiplechoice test item generation. The successful integration of the system shows that ATP is a viable technology, and should be exploited more by other NLP applications

    Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

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    The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop

    A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4

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    Large language models (LLMs) are a special class of pretrained language models obtained by scaling model size, pretraining corpus and computation. LLMs, because of their large size and pretraining on large volumes of text data, exhibit special abilities which allow them to achieve remarkable performances without any task-specific training in many of the natural language processing tasks. The era of LLMs started with OpenAI GPT-3 model, and the popularity of LLMs is increasing exponentially after the introduction of models like ChatGPT and GPT4. We refer to GPT-3 and its successor OpenAI models, including ChatGPT and GPT4, as GPT-3 family large language models (GLLMs). With the ever-rising popularity of GLLMs, especially in the research community, there is a strong need for a comprehensive survey which summarizes the recent research progress in multiple dimensions and can guide the research community with insightful future research directions. We start the survey paper with foundation concepts like transformers, transfer learning, self-supervised learning, pretrained language models and large language models. We then present a brief overview of GLLMs and discuss the performances of GLLMs in various downstream tasks, specific domains and multiple languages. We also discuss the data labelling and data augmentation abilities of GLLMs, the robustness of GLLMs, the effectiveness of GLLMs as evaluators, and finally, conclude with multiple insightful future research directions. To summarize, this comprehensive survey paper will serve as a good resource for both academic and industry people to stay updated with the latest research related to GPT-3 family large language models.Comment: Preprint under review, 58 page

    Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

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    This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C1011198) , (Institute for Information & communications Technology Planning & Evaluation) (IITP) grant funded by the Korea government (MSIT) under the ICT Creative Consilience Program (IITP-2021-2020-0-01821) , and AI Platform to Fully Adapt and Reflect Privacy-Policy Changes (No. 2022-0-00688).Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to comprehend and trust due to their black-box nature. Usually, it is essential to understand the reasoning behind an AI mode ľs decision-making. Thus, the need for eXplainable AI (XAI) methods for improving trust in AI models has arisen. XAI has become a popular research subject within the AI field in recent years. Existing survey papers have tackled the concepts of XAI, its general terms, and post-hoc explainability methods but there have not been any reviews that have looked at the assessment methods, available tools, XAI datasets, and other related aspects. Therefore, in this comprehensive study, we provide readers with an overview of the current research and trends in this rapidly emerging area with a case study example. The study starts by explaining the background of XAI, common definitions, and summarizing recently proposed techniques in XAI for supervised machine learning. The review divides XAI techniques into four axes using a hierarchical categorization system: (i) data explainability, (ii) model explainability, (iii) post-hoc explainability, and (iv) assessment of explanations. We also introduce available evaluation metrics as well as open-source packages and datasets with future research directions. Then, the significance of explainability in terms of legal demands, user viewpoints, and application orientation is outlined, termed as XAI concerns. This paper advocates for tailoring explanation content to specific user types. An examination of XAI techniques and evaluation was conducted by looking at 410 critical articles, published between January 2016 and October 2022, in reputed journals and using a wide range of research databases as a source of information. The article is aimed at XAI researchers who are interested in making their AI models more trustworthy, as well as towards researchers from other disciplines who are looking for effective XAI methods to complete tasks with confidence while communicating meaning from data.National Research Foundation of Korea Ministry of Science, ICT & Future Planning, Republic of Korea Ministry of Science & ICT (MSIT), Republic of Korea 2021R1A2C1011198Institute for Information amp; communications Technology Planning amp; Evaluation) (IITP) - Korea government (MSIT) under the ICT Creative Consilience Program IITP-2021-2020-0-01821AI Platform to Fully Adapt and Reflect Privacy-Policy Changes2022-0-0068

    Proceedings of the 8th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2023)

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    This volume gathers the papers presented at the Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, during 21–22 September 2023
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