AI is used in social media to analyze user behavior, personalize content recommendations, and detect trends or sentiment. It employs machine learning algorithms and natural language processing to understand and categorize user-generated content, optimize ad targeting, and enhance user experiences on social media platforms.
This article discusses bioRxiv's collaboration with ScienceCast, an AI startup, to use large language models for multi-level summaries of scientific preprints. While aiming to enhance accessibility, the pilot reveals challenges in accurately summarizing complex technical content, with scientists noting inaccuracies. The future outlook suggests potential benefits as AI capabilities advance, but concerns around precision and the need for a balance between automation and human oversight persist.
Researchers propose essential prerequisites for improving the robustness evaluation of large language models (LLMs) and highlight the growing threat of embedding space attacks. This study emphasizes the need for clear threat models, meaningful benchmarks, and a comprehensive understanding of potential vulnerabilities to ensure LLMs can withstand adversarial challenges in open-source models.
This paper presents MULTITuDE, a benchmark dataset designed for multilingual machine-generated text (MGT) detection. The study evaluates various detection methods across 11 languages, demonstrating that fine-tuning detectors with multilingual language models is an effective approach, and the linguistic similarity between languages plays a significant role in the generalization of detectors.
This review explores the landscape of social robotics research, addressing knowledge gaps and implications for business and management. It highlights the need for more studies on social robotic interactions in organizations, trust in human-robot relationships, and the impact of virtual social robots in the metaverse, emphasizing the importance of balancing technology integration with societal well-being.
Researchers present the "SCALE" framework, which evaluates the impact of AI on the mortgage market, with a focus on promoting homeownership inclusivity for marginalized communities. The framework encompasses societal values, contextual integrity, accuracy, legality, and expanded opportunity, aiming to address concerns about bias and discrimination in AI applications within the mortgage industry while advancing fair lending practices and social equity in homeownership.
In a groundbreaking study, AI-driven data analysis accurately predicts Greco-Roman wrestlers' competitive success, with just an 11% error rate. This research has the potential to revolutionize athlete selection and training in various sports, offering valuable insights for coaches and athletes alike.
Researchers have introduced a groundbreaking approach to AI learning in social environments, where agents actively interact with humans. By combining reinforcement learning with social norms, the study demonstrated a 112% improvement in recognizing new information, highlighting the potential of socially situated AI in open social settings and human-AI interactions.
This study delves into the transformative potential of data science in African healthcare and research, emphasizing the critical role of ethical governance. It highlights ongoing initiatives, investments, and challenges while stressing the need for collaboration and investment in ethical oversight to drive impactful research in the continent.
A recent study delves into the automated classification of short texts from social media, crucial for social science research. The research compares lexicon-based and supervised machine learning approaches, highlighting the significance of traditional ML algorithms in short text classification and their efficiency compared to deep neural architectures, especially in cases with limited data resources.
Researchers have developed a robust web-based malware detection system that utilizes deep learning, specifically a 1D-CNN architecture, to classify malware within portable executable (PE) files. This innovative approach not only showcases impressive accuracy but also bridges the gap between advanced malware detection technology and user accessibility through a user-friendly web interface.
Researchers delve into the intricacies of user intent modeling in conversational recommender systems, revealing symbiotic relationships between models and features. Through systematic literature reviews and real-world case studies, they present a structured decision model that emphasizes practical adaptability and promotes collaboration, equipping academics and practitioners to innovate in the realm of AI-driven conversations.
Researchers propose a hybrid model that integrates sentiment analysis using Word2vec and Long Short-Term Memory (LSTM) for accurate exchange rate trend prediction. By incorporating emotional weights from Weibo data and historical exchange rate information, combined with CNN-LSTM architecture, the model demonstrates enhanced prediction accuracy compared to traditional methods.
Researchers unveil the MedMine initiative, a pioneering effort that harnesses the power of pre-trained language models like Med7 and Clinical-XLM-RoBERTa for medication mining in clinical texts. By systematically evaluating these models and addressing challenges, the initiative lays the groundwork for a transformative shift in healthcare practices, promising accurate medication extraction, improved patient care, and advancements in medical research.
Recent advancements in Natural Language Processing (NLP) have revolutionized various fields, yet concerns about embedded biases have raised ethical and fairness issues. To combat this challenge, a team of researchers presents Nbias, an innovative framework introduced in an arXiv* article. Nbias detects and mitigates biases in textual data, addressing explicit and implicit biases that can perpetuate stereotypes and inequalities.
This comprehensive review explores the integration of machine learning (ML) techniques in forest fire science. The study highlights the significance of early fire prediction and detection for effective fire management. It discusses various ML methods applied in forest fire detection, prediction, fire mapping, and data evaluation. The review identifies challenges and research priorities while emphasizing the potential benefits of ML in improving forest fire resilience and enabling more efficient data analysis and modeling.
"Lero's new public survey aims to uncover citizens' hopes and fears about AI and software, focusing on issues of responsibility and ethics. This project seeks to address the underrepresentation of public sentiment in ongoing debates, amidst rising concerns over biases in AI and potential human rights abuses."
Technology experts convened at Oak Ridge National Laboratory's Department of Energy for the Trillion-Pixel GeoAI Challenge workshop to discuss the future of geospatial systems. The event emphasized advancements in artificial intelligence, cloud infrastructure, high-performance computing, and remote sensing, highlighting their potential in addressing national and human security concerns like disaster response and land-use planning.
Researchers propose a visual analytics pipeline that leverages citizen volunteered geographic information (VGI) from social media to enhance impact-based weather warning systems. By combining text and image analysis, machine learning, and interactive visualization, they aim to detect and explore extreme weather events with greater accuracy and provide valuable localized information for disaster management and resilience planning.
Researchers introduce a speech emotion recognition (SER) system that accurately predicts a speaker's emotional state using audio signals. By employing convolutional neural networks (CNN) and Mel-frequency cepstral coefficients (MFCC) for feature extraction, the proposed system outperforms existing approaches, showcasing its potential in various applications such as human-computer interaction and emotion-aware technologies.
Large-scale multi-label dataset, Incidents1M, is introduced for incident detection and image-filtering experiments on social media. It enables timely understanding of natural disaster progression and aftermath using automated methods.
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