Conversational agents (CAs) such as Alexa and Siri are designed to answer questions, offer suggestions -- and even display empathy. However, new research finds they do poorly compared to humans when interpreting and exploring a user's experience.
One group commonly misunderstood by voice technology are individuals who speak African American English, or AAE. Researchers designed an experiment to test how AAE speakers adapt their speech when imagining talking to a voice assistant, compared to talking to a friend, family member, or stranger. The study tested familiar human, unfamiliar human, and voice assistant-directed speech conditions by comparing speech rate and pitch variation. Analysis of the recordings showed that the speakers exhibited two consistent adjustments when they were talking to voice technology compared to talking to another person: a slower rate of speech with less pitch variation.
Computer vision can be a valuable tool for anyone tasked with analyzing hours of footage because it can speed up the process of identifying individuals. For example, law enforcement may use it to perform a search for individuals with a simple query, such as 'Locate anyone wearing a red scarf over the past 48 hours.'
A computer game helped upper secondary school students become better at distinguishing between reliable and misleading news.
A new technique can more effectively perform a safety check on an AI chatbot. Researchers enabled their model to prompt a chatbot to generate toxic responses, which are used to prevent the chatbot from giving hateful or harmful answers when deployed.
Umwelt is a new a system that enables blind and low-vision users to author accessible, interactive charts representing data in three modalities: visualization, textual description, and sonification.