Articles tagged with Ai Common Sense Knowledge
The JGU Center for Lifelong Learning is developing personalized AI-based learning experiences for adult learners, aiming to improve motivation and learning outcomes. The project will also discuss the challenges of AI technologies in teaching modern foreign languages.
The TRAILS AI Institute has awarded eight seed grants totaling $1.5 million to advance AI design, development and governance. The funded projects include developing AI chatbots for smoking cessation and designing animal-like robots for autism support.
A new AI system, Coscientist, has demonstrated its ability to autonomously learn about Nobel Prize-winning chemical reactions and design successful laboratory procedures. The system achieved this in just a few minutes, outperforming human chemists in some cases.
A new study investigates how people perceive and claim authorship of artificially generated texts, revealing that perceived ownership does not always align with declared authorship. Researchers found that participants who wrote the text themselves felt a stronger sense of ownership, while those who relied on AI ghostwriters did not.
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 paper by Anthony Chemero explains how AI thinking differs from human thinking, highlighting the limitations of large language models trained on biased data. Despite generating impressive text, these models can make up facts and produce biased outputs due to their lack of embodiment and understanding of context.
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.
A study of 33 physicians across 17 specialties found that chatbots provided largely accurate information to diverse medical queries. However, the chatbots had important limitations that require further research and model development.
Biased AI can limit climate predictions and misguide governments due to missing information from under-represented communities. Human-in-the-loop design can fill these 'data holes' by offering a sense check on used data and context.
A recent study found that Google is more current but biased by advertisers, while ChatGPT is more objective but outdated. The researchers suggest combining both strengths to build a better system.
Researchers developed a tool called SKILL that enables AI agents to learn 102 distinct tasks by sharing knowledge in parallel, reducing the time needed to master new skills. The technology has potential applications in medicine, education, and other fields where vast knowledge is required.
Researchers have developed an AI algorithm that accurately estimates coastal fish stocks, providing critical data for sustainable management. The tool has the potential to save millions of dollars in annual research costs and bridge the global data and sustainability divide.
A team of researchers developed an unsupervised entity alignment framework to improve knowledge graph search, avoiding human labor. The framework outperformed most competitors on precision and recall, scoring higher overall across multiple datasets.
A new neural network, CD-GAN, uses common sense knowledge to enhance text descriptions and generate images of birds at three resolution levels. The system achieved competitive scores against other image generation methods, producing vivid and natural-looking images.
A new study by the University of Maryland School of Medicine found that ChatGPT provides correct information on breast cancer screening for most questions. However, inconsistencies and outdated information were detected in some responses. The researchers recommend relying on doctors for advice due to the limitations of ChatGPT.
Researchers seek to develop algorithms providing meaningful explanations for AI decision-making, enabling higher human trust and adoption in fields like science. The project focuses on symbolic reasoning and estimating explanation accuracy, addressing the need for transparent AI systems.
The NERVE Center has developed test methods and metrics for various robots, identifying limitations to improve systems. The center's success grew its research capabilities through partnerships with NIST and the U.S. Army.
Researchers at North Carolina State University developed a blueprint for incorporating ethical guidelines into AI decision-making programs. The new mathematical formula, based on the Agent, Deed, and Consequence (ADC) Model, considers intent, character, and consequences of actions to make more informed decisions.
A study by NTU Singapore found that people have lower trust in health interventions suggested by AI alone, compared to those they perceive as based on human expert opinion. Emphasizing the involvement of a human health expert can improve acceptance and effectiveness of AI-suggested preventive care measures.
Researchers at USC's Information Sciences Institute developed a method to train AI to understand analogies in Aesop's fables, enabling it to make creative connections between familiar and novel situations. The study found that humans approach analogical reasoning subjectively and interpretively, influencing the outcome.