Examples the team gives include choosing an object to use as a hammer when there’s no hammer available (the robot chooses a rock) and picking the best drink for a tired person (the robot chooses an energy drink).

“RT-2 shows improved generalization capabilities and semantic and visual understanding beyond the robotic data it was exposed to,” the researchers wrote in a Google blog post. “This includes interpreting new commands and responding to user commands by performing rudimentary reasoning, such as reasoning about object categories or high-level descriptions.”

The dream of general-purpose robots that can help humans with whatever may come up—whether in a home, a commercial setting, or an industrial setting—won’t be achievable until robots can learn on the go. What seems like the most basic instinct to us is, for robots, a complex combination of understanding context, being able to reason through it, and taking actions to solve problems that weren’t anticipated to pop up. Programming them to react appropriately to a variety of unplanned scenarios is impossible, so they need to be able to generalize and learn from experience, just like humans do.

Digital sociology explores the interplay between digital technology, social behaviors, and societal structures. As an emerging discipline, it delves into how online interactions, social media platforms, and digital tools shape and reflect cultural dynamics, personal identities, and group norms. Digital sociologists analyze trends in cyber communities, the implications of digital data collection, and the broader impact of the internet on social evolution.

In this age of rapid technological advancement — where we find ourselves in the midst of a Digital Revolution and most recently, in an AI Revolution, the fast-spinning changes in society require analysis and understanding. Digital sociology provides critical insights into the multifaceted relationship between technology and the fabric of society.

Experts warn that AI-generated content may pose a threat to the AI technology that produced it.

In a recent paper on how generative AI tools like ChatGPT are trained, a team of AI researchers from schools like the University of Oxford and the University of Cambridge found that the large language models behind the technology may potentially be trained on other AI-generated content as it continues to spread in droves across the internet — a phenomenon they coined as “model collapse.” In turn, the researchers claim that generative AI tools may respond to user queries with lower-quality outputs, as their models become more widely trained on “synthetic data” instead of the human-made content that make their responses unique.

Other AI researchers have coined their own terms to describe the training method. In a paper released in July, researchers from Stanford and Rice universities called this phenomenon the “Model Autography Disorder,” in which the “self-consuming” loop of AI training itself on content generated by other AI could result in generative AI tools “doomed” to have their “quality” and “diversity” of images and text generated falter. Jathan Sadowski, a senior fellow at the Emerging Technologies Research Lab in Australia who researches AI, called this phenomenon “Habsburg AI,” arguing that AI systems heavily trained on outputs of other generative AI tools can create “inbred mutant” responses that contain “exaggerated, grotesque features.”