New research published in Telematics and Informatics provides evidence that the fulfillment of basic psychological needs through technology use is linked to changes in attitudes towards artificial intelligence over time. The findings indicate that self-determination, particularly feelings of competence and relatedness, plays a crucial role in shaping both negative and positive attitudes towards this emerging technology.

“We live in a world where artificial intelligence (AI) is becoming more common and accessible than ever. People’s attitudes towards AI will most certainly have a huge effect on how fast and widely AI can spread in society and how the development of AI will turn out,” said study author Jenna Bergdahl, a researcher at the Emerging Technologies Lab at Tampere University.

“As a researcher, I work in the Emerging Technologies Lab at Tampere University, where we are particularly interested in the new technological forms of life that constantly challenge and transform human and post-human living. Two projects from the Emerging Technologies Lab, called UrbanAI and Self & Technology, are focusing especially on artificial intelligence in society and conducting cross-national social psychological research on human-technology interaction.”

A new soft robotic gripper is not only 3D printed in one print, it also doesn’t need any electronics to work. The device was developed by a team of roboticists at the University of California San Diego, in collaboration with researchers at the BASF corporation, who detailed their work in Science Robotics.

The researchers wanted to design a soft gripper that would be ready to use right as it comes off the 3D printer, equipped with built in gravity and touch sensors. As a result, the gripper can pick up, hold, and release objects. No such gripper existed before this work.

“We designed functions so that a series of valves would allow the gripper to both grip on contact and release at the right time,” said Yichen Zhai, a postdoctoral researcher in the Bioinspired Robotics and Design Lab at the University of California San Diego and the leading author of the paper. “It’s the first time such a gripper can both grip and release. All you have to do is turn the gripper horizontally. This triggers a change in the airflow in the valves, making the two fingers of the gripper release.”

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Robotic Transformer 2 (RT-2) is a novel vision-language-action (VLA) model that learns from both web and robotics data, and translates this knowledge into generalised instructions for robotic control.

High-capacity vision-language models (VLMs) are trained on web-scale datasets, making these systems remarkably good at recognising visual or language patterns and operating across different languages. But for robots to achieve a similar level of competency, they would need to collect robot data, first-hand, across every object, environment, task, and situation.

In our paper, we introduce Robotic Transformer 2 (RT-2), a novel vision-language-action (VLA) model that learns from both web and robotics data, and translates this knowledge into generalised instructions for robotic control, while retaining web-scale capabilities.