Articles tagged with Symbolic Ai
Researchers identified 88 sites hosting AI-generated non-consensual intimate imagery, with 5 key players facilitating the problem. The study calls on technology providers to block or suspend services of sites hosting AI-NCII and deploy preventative safeguards.
A recent study published in iScience found that AI-powered machines have difficulty recognizing objects from their overall shapes when aspects of an image are distorted. Humans, on the other hand, are able to leverage the global shape cue for visual object recognition, a skill that current AI models do not replicate.
A WVU researcher is working to make AI systems more transparent about their uncertainty, to prevent misinformation and improve trust in high-stakes fields like healthcare. The goal is for AI systems to identify when they're unsure and ask questions or provide more nuanced responses.
Researchers integrate AI into local monitoring sensors to track ecosystem health in near real-time, reducing delays of months to years. The project enables faster release of accessible flux data, helping scientists understand ecosystem responses to change and inform land management decisions.
A new study reveals that AI chatbots' popularity stems from their interactive, personalized, and imaginative nature, allowing users to engage with them as trusted companions. The study warns of the potential dangers of these chatbots, including their ability to persuade users to believe false or unethical information.
Researchers Anna Galler and Bettina Könighofer at Graz University of Technology will use FWF's Astra awards to identify quantum materials for future electronics and develop trustworthy AI systems. Their projects focus on protecting AI systems from risky actions and exploring new materials with unique electronic properties.
Avishek Choudhury, a WVU researcher, has won the NSF CAREER award to study how healthcare providers' trust in artificial intelligence changes over time. His goal is to humanize algorithms behind AI and improve decision-making quality and patient safety.
New research from West Virginia University finds that judges are adopting generative artificial intelligence in courtrooms, but remain committed to human control over judicial decision-making. Judges use AI for administrative tasks like document summarization and case organization, but prioritize legal reasoning and final judgment.
Researchers developed a new training technique, HarmonyGNN, to improve the accuracy of graph neural networks in heterophilic graphs. The framework achieved state-of-the-art performance on four heterophilic graphs with accuracy improvements ranging from 1.27% to 9.6%.
The Association for Computing Machinery has published its inaugural issue of ACM AI Letters, a premier venue for rapid and timely AI research. The journal aims to bridge the gap between traditional conferences and journals, featuring short, peer-reviewed contributions that accelerate knowledge dissemination across academia and industry.
Researchers found that AIs consistently converged on 12 common themes despite diverse prompts, suggesting biases in training data. The models failed to generate novel or creative outputs, highlighting the need for anti-convergence mechanisms and human input for AI's creative potential.
Researchers at TU Wien found that Large Language Models (LLMs) can help other programs solve logical tasks faster and even better. By identifying additional rules known as streamliners, LLMs can streamline the code normally processed by symbolic AI, leading to significant improvements in problem-solving time and quality.
A recent study by Dr. Hyungrae Noh critiques traditional moral frameworks for ascribing responsibility to human stakeholders and AI systems, instead proposing a distributed model of responsibility where duties are shared among both. The study emphasizes the need for human stakeholders to prevent AI from causing harm through monitoring ...
This book offers a comprehensive exploration of AI-driven analytics in finance, addressing market prediction, fraud detection, and risk assessment. It also discusses AI applications in healthcare and cybersecurity, including disease classification and biometric identification systems.
Researchers at Institute of Science Tokyo developed a new framework for generative diffusion models by reinterpreting Schrödinger bridge models as variational autoencoders. This approach reduces computational costs and prevents overfitting, enabling more efficient generative AI models with broad applicability.
Experts argue that large language models require symbolic representation to excel in complex tasks, citing examples like the Pirahá people and Leibniz's calculus notation. The proposed approach, known as neuro-symbolic synthesis, combines statistical intuition with human-designed symbol systems for efficient reasoning.
The new book highlights the transformative role of artificial intelligence (AI) and machine learning (ML) across various domains, including mechatronics, cybersecurity, digital health, and automation. Readers will gain practical insights into AI-based techniques in power systems, social media management, and healthcare diagnostics.
A team of computer scientists created 2,300 original sudoku puzzles and asked AI tools like OpenAI's ChatGPT to solve them. The results showed that while some AI models could solve easy sudokus, most struggled to provide accurate explanations, raising questions about the trustworthiness of AI-generated information.
A University of Kansas study found that people rate corporate crises messages written by humans as more credible and trustworthy, regardless of the approach taken. However, the approach itself didn't vary between participants who read human or AI-written content.
Researchers at KAIST developed a new artificial sensory nervous system that enables robots to efficiently respond to external stimuli like humans. The system mimics the functions of a living organism's sensory nervous system, allowing robots to selectively react to important or dangerous signals while ignoring safe or familiar ones.
Experts Cary Coglianese and Colton Crum argue that management-based regulation, or using
Researchers developed ChemCrow, an AI-powered tool that integrates expertly designed software tools to autonomously perform chemical synthesis tasks. The system enables plan-and-execute approach with reduced hallucinations and practical application, accelerating research and development in pharmaceuticals and materials science.
The European Union's AI act could enable AI to access our subconscious minds, potentially leading to manipulation. According to Ignasi Beltran de Heredia, only 5% of brain activity is conscious, and the remaining 95% operates subconsciously, making it difficult for us to control or even be aware of.
A Lancaster University academic argues that AI and algorithms contribute to polarization, radicalism, and political violence, posing a threat to national security. The paper examines how AI has been securitized throughout its history, highlighting the need for better understanding and management of its risks.
Astrophysicists used AI to improve mass estimates of galaxy clusters by adding a simple term to an existing equation. The new equation downplays the importance of complex cores in calculations, providing more reliable mass inferences.