486 research outputs found
Multidisciplinary perspectives on Artificial Intelligence and the law
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
Unifying context with labeled property graph: A pipeline-based system for comprehensive text representation in NLP
Extracting valuable insights from vast amounts of unstructured digital text presents significant challenges across diverse domains. This research addresses this challenge by proposing a novel pipeline-based system that generates domain-agnostic and task-agnostic text representations. The proposed approach leverages labeled property graphs (LPG) to encode contextual information, facilitating the integration of diverse linguistic elements into a unified representation. The proposed system enables efficient graph-based querying and manipulation by addressing the crucial aspect of comprehensive context modeling and fine-grained semantics. The effectiveness of the proposed system is demonstrated through the implementation of NLP components that operate on LPG-based representations. Additionally, the proposed approach introduces specialized patterns and algorithms to enhance specific NLP tasks, including nominal mention detection, named entity disambiguation, event enrichments, event participant detection, and temporal link detection. The evaluation of the proposed approach, using the MEANTIME corpus comprising manually annotated documents, provides encouraging results and valuable insights into the system\u27s strengths. The proposed pipeline-based framework serves as a solid foundation for future research, aiming to refine and optimize LPG-based graph structures to generate comprehensive and semantically rich text representations, addressing the challenges associated with efficient information extraction and analysis in NLP
Low- and high-resource opinion summarization
Customer reviews play a vital role in the online purchasing decisions we make. The reviews
express user opinions that are useful for setting realistic expectations and uncovering important
details about products. However, some products receive hundreds or even thousands of
reviews, making them time-consuming to read. Moreover, many reviews contain uninformative
content, such as irrelevant personal experiences. Automatic summarization offers an
alternative – short text summaries capturing the essential information expressed in reviews.
Automatically produced summaries can reflect overall or particular opinions and be tailored to
user preferences. Besides being presented on major e-commerce platforms, home assistants
can also vocalize them. This approach can improve user satisfaction by assisting in making
faster and better decisions.
Modern summarization approaches are based on neural networks, often requiring thousands of
annotated samples for training. However, human-written summaries for products are expensive
to produce because annotators need to read many reviews. This has led to annotated data
scarcity where only a few datasets are available. Data scarcity is the central theme of our
works, and we propose a number of approaches to alleviate the problem. The thesis consists
of two parts where we discuss low- and high-resource data settings.
In the first part, we propose self-supervised learning methods applied to customer reviews
and few-shot methods for learning from small annotated datasets. Customer reviews without
summaries are available in large quantities, contain a breadth of in-domain specifics, and
provide a powerful training signal. We show that reviews can be used for learning summarizers
via a self-supervised objective. Further, we address two main challenges associated with
learning from small annotated datasets. First, large models rapidly overfit on small datasets
leading to poor generalization. Second, it is not possible to learn a wide range of in-domain
specifics (e.g., product aspects and usage) from a handful of gold samples. This leads to
subtle semantic mistakes in generated summaries, such as ‘great dead on arrival battery.’ We
address the first challenge by explicitly modeling summary properties (e.g., content coverage
and sentiment alignment). Furthermore, we leverage small modules – adapters – that are
more robust to overfitting. As we show, despite their size, these modules can be used to
store in-domain knowledge to reduce semantic mistakes. Lastly, we propose a simple method
for learning personalized summarizers based on aspects, such as ‘price,’ ‘battery life,’ and
‘resolution.’ This task is harder to learn, and we present a few-shot method for training a
query-based summarizer on small annotated datasets.
In the second part, we focus on the high-resource setting and present a large dataset with
summaries collected from various online resources. The dataset has more than 33,000 humanwritten
summaries, where each is linked up to thousands of reviews. This, however, makes it
challenging to apply an ‘expensive’ deep encoder due to memory and computational costs. To
address this problem, we propose selecting small subsets of informative reviews. Only these
subsets are encoded by the deep encoder and subsequently summarized. We show that the
selector and summarizer can be trained end-to-end via amortized inference and policy gradient
methods
Unveiling the frontiers of deep learning: innovations shaping diverse domains
Deep learning (DL) enables the development of computer models that are
capable of learning, visualizing, optimizing, refining, and predicting data. In
recent years, DL has been applied in a range of fields, including audio-visual
data processing, agriculture, transportation prediction, natural language,
biomedicine, disaster management, bioinformatics, drug design, genomics, face
recognition, and ecology. To explore the current state of deep learning, it is
necessary to investigate the latest developments and applications of deep
learning in these disciplines. However, the literature is lacking in exploring
the applications of deep learning in all potential sectors. This paper thus
extensively investigates the potential applications of deep learning across all
major fields of study as well as the associated benefits and challenges. As
evidenced in the literature, DL exhibits accuracy in prediction and analysis,
makes it a powerful computational tool, and has the ability to articulate
itself and optimize, making it effective in processing data with no prior
training. Given its independence from training data, deep learning necessitates
massive amounts of data for effective analysis and processing, much like data
volume. To handle the challenge of compiling huge amounts of medical,
scientific, healthcare, and environmental data for use in deep learning, gated
architectures like LSTMs and GRUs can be utilized. For multimodal learning,
shared neurons in the neural network for all activities and specialized neurons
for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table
Video Summarization Using Unsupervised Deep Learning
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
Towards Argument-Aware Abstractive Summarization of Long Legal Opinions with Summary Reranking
We propose a simple approach for the abstractive summarization of long legal
opinions that considers the argument structure of the document. Legal opinions
often contain complex and nuanced argumentation, making it challenging to
generate a concise summary that accurately captures the main points of the
legal opinion. Our approach involves using argument role information to
generate multiple candidate summaries, then reranking these candidates based on
alignment with the document's argument structure. We demonstrate the
effectiveness of our approach on a dataset of long legal opinions and show that
it outperforms several strong baselines
Data extraction methods for systematic review (semi)automation: Update of a living systematic review [version 2; peer review: 3 approved]
Background: The reliable and usable (semi)automation of data extraction can support the field of systematic review by reducing the workload required to gather information about the conduct and results of the included studies. This living systematic review examines published approaches for data extraction from reports of clinical studies.
Methods: We systematically and continually search PubMed, ACL Anthology, arXiv, OpenAlex via EPPI-Reviewer, and the dblp computer science bibliography. Full text screening and data extraction are conducted within an open-source living systematic review application created for the purpose of this review. This living review update includes publications up to December 2022 and OpenAlex content up to March 2023.
Results: 76 publications are included in this review. Of these, 64 (84%) of the publications addressed extraction of data from abstracts, while 19 (25%) used full texts. A total of 71 (93%) publications developed classifiers for randomised controlled trials. Over 30 entities were extracted, with PICOs (population, intervention, comparator, outcome) being the most frequently extracted. Data are available from 25 (33%), and code from 30 (39%) publications. Six (8%) implemented publicly available tools
Conclusions: This living systematic review presents an overview of (semi)automated data-extraction literature of interest to different types of literature review. We identified a broad evidence base of publications describing data extraction for interventional reviews and a small number of publications extracting epidemiological or diagnostic accuracy data. Between review updates, trends for sharing data and code increased strongly: in the base-review, data and code were available for 13 and 19% respectively, these numbers increased to 78 and 87% within the 23 new publications. Compared with the base-review, we observed another research trend, away from straightforward data extraction and towards additionally extracting relations between entities or automatic text summarisation. With this living review we aim to review the literature continually
Multimodal Foundation Models for Zero-shot Animal Species Recognition in Camera Trap Images
Due to deteriorating environmental conditions and increasing human activity,
conservation efforts directed towards wildlife is crucial. Motion-activated
camera traps constitute an efficient tool for tracking and monitoring wildlife
populations across the globe. Supervised learning techniques have been
successfully deployed to analyze such imagery, however training such techniques
requires annotations from experts. Reducing the reliance on costly labelled
data therefore has immense potential in developing large-scale wildlife
tracking solutions with markedly less human labor. In this work we propose
WildMatch, a novel zero-shot species classification framework that leverages
multimodal foundation models. In particular, we instruction tune
vision-language models to generate detailed visual descriptions of camera trap
images using similar terminology to experts. Then, we match the generated
caption to an external knowledge base of descriptions in order to determine the
species in a zero-shot manner. We investigate techniques to build instruction
tuning datasets for detailed animal description generation and propose a novel
knowledge augmentation technique to enhance caption quality. We demonstrate the
performance of WildMatch on a new camera trap dataset collected in the
Magdalena Medio region of Colombia.Comment: 18 pages, 9 figure
AI Hype: Public Relations and AI's doomsday machine
This chapter broadens current professional debates by highlighting a different but vital relationship between the PR profession and AI, one in which PR professionals – acting as AI cheerleaders – are deeply implicated in generating AI hype. My discussion explores recent market studies research on disruption and hype cycles, before delving into the latest, somewhat disturbing phase in AI’s hype cycle, in which end-of-the-world scenarios are invoked to stimulate a climate of fear around AI. The chapter concludes by exploring some ethical concerns with promoting AI and automation as humanity’s inevitable future
- …