Articles tagged with Ai Common Sense Knowledge
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A recent study found that Google is more current but biased by advertisers, while ChatGPT is more objective but outdated.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 However, inconsistencies and outdated information were detected in some responses.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.