All Work Published on Sciences (Social, Health, Biological, Physical)
Five teams will use the funding to advance their work in biology, generative AI and creativity, policing, and more.
Five teams will use the funding to advance their work in biology, generative AI and creativity, policing, and more.
Contextualizing Meaningful Social Interactions and Psychological Well-Being in Everyday Life
Using three large-scale longitudinal datasets collected from a cohort of university students over the span of 3 years (total N = 2,896 participants; ecological momentary assessments = 129,414), we found that engagement in meaningful social interactions with peers was associated with lower momentary loneliness and greater affective well-being. We also examined the role of four contextual factors (interaction partners, communication channels, places, and co-occurring activities) in explaining the relationships between meaningful social interactions and momentary well-being. Across samples, we found (a) participants reported experiencing greater loneliness and lower affective well-being after engaging in meaningful social interaction via computer-mediated channels (and via direct messaging in particular), compared to face-to-face, and (b) participants reported experiencing lower affective well-being after engaging in meaningful social interactions while dining and studying or working, compared to while resting. Taken together, our findings provide insight into the relationships between meaningful social interactions, momentary well-being, and contextual factors.
Using three large-scale longitudinal datasets collected from a cohort of university students over the span of 3 years (total N = 2,896 participants; ecological momentary assessments = 129,414), we found that engagement in meaningful social interactions with peers was associated with lower momentary loneliness and greater affective well-being. We also examined the role of four contextual factors (interaction partners, communication channels, places, and co-occurring activities) in explaining the relationships between meaningful social interactions and momentary well-being. Across samples, we found (a) participants reported experiencing greater loneliness and lower affective well-being after engaging in meaningful social interaction via computer-mediated channels (and via direct messaging in particular), compared to face-to-face, and (b) participants reported experiencing lower affective well-being after engaging in meaningful social interactions while dining and studying or working, compared to while resting. Taken together, our findings provide insight into the relationships between meaningful social interactions, momentary well-being, and contextual factors.
"Steampunk" Self-Learning Mechanical Circuits That Adapt to Their Environments
Researchers at Stanford have invented a new type of self-powered mechanical circuits that learn. It could lead to new purely mechanical machines that understand and adapt to the changing world around them.
Researchers at Stanford have invented a new type of self-powered mechanical circuits that learn. It could lead to new purely mechanical machines that understand and adapt to the changing world around them.
How Culture Shapes What People Want From AI
There is an urgent need to incorporate the perspectives of culturally diverse groups into AI developments. We present a novel conceptual framework for research that aims to expand, reimagine, and reground mainstream visions of AI using independent and interdependent cultural models of the self and the environment. Two survey studies support this framework and provide preliminary evidence that people apply their cultural models when imagining their ideal AI. Compared with European American respondents, Chinese respondents viewed it as less important to control AI and more important to connect with AI, and were more likely to prefer AI with capacities to influence. Reflecting both cultural models, findings from African American respondents resembled both European American and Chinese respondents. We discuss study limitations and future directions and highlight the need to develop culturally responsive and relevant AI to serve a broader segment of the world population.
There is an urgent need to incorporate the perspectives of culturally diverse groups into AI developments. We present a novel conceptual framework for research that aims to expand, reimagine, and reground mainstream visions of AI using independent and interdependent cultural models of the self and the environment. Two survey studies support this framework and provide preliminary evidence that people apply their cultural models when imagining their ideal AI. Compared with European American respondents, Chinese respondents viewed it as less important to control AI and more important to connect with AI, and were more likely to prefer AI with capacities to influence. Reflecting both cultural models, findings from African American respondents resembled both European American and Chinese respondents. We discuss study limitations and future directions and highlight the need to develop culturally responsive and relevant AI to serve a broader segment of the world population.
