Articles tagged with Symbolic Ai
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.