Imagine an artificial intelligence (AI) model that can watch and understand moving images with the subtlety of a human brain. Now, scientists at Scripps Research have made this a reality by creating MovieNet: an innovative AI that processes videos much like how our brains interpret real-life scenes as they unfold over time.

Chatbots can wear a lot of proverbial hats: dictionary, therapist, poet, all-knowing friend. The artificial intelligence models that power these systems appear exceptionally skilled and efficient at providing answers, clarifying concepts, and distilling information. But to establish trustworthiness of content generated by such models, how can we really know if a particular statement is factual, a hallucination, or just a plain misunderstanding?

To help humans to complete everyday manual tasks, robots should be able to reliably manipulate everyday objects that vary in shape, texture and size. Many conventional approaches to enable robotic manipulation of various objects rely on extensive training and precise programming, also delineating the properties of objects that the robots will be manipulating.

Businesses are already being radically transformed by artificial intelligence (AI). Tools now exist that offer instantaneous, high-quality results in improving certain operations without the burden of high costs or delays. In fact, generative AI could completely upend the traditional ways that we measure success in business.

A team led by Prof. Dong Erbao from the University of Science and Technology of China (USTC) of the Chinese Academy of Sciences (CAS), collaborating with Prof. Yu Xinge from the City University of Hong Kong, developed a novel tactile perception method based on flexible grating structural color. The work was published in National Science Review.

Modeling how cars deform in a crash, how spacecraft respond to extreme environments, or how bridges resist stress could be made thousands of times faster thanks to new artificial intelligence that enables personal computers to solve massive math problems that generally require supercomputers.

In recent years, engineers have been trying to create hardware systems that better support the high computational demands of machine learning algorithms. These include systems that can perform multiple functions, acting as sensors, memories and computer processors all at once.

Researchers at Stanford Engineering have developed an AI-trained model to accurately recreate the hand movements of elite-level pianists and the physical stresses they endure while playing.

A team of roboticists at the Beijing Institute of Technology, working with a pair of colleagues from the Technical University of Munich, has created a new kind of rat robot—one that was designed to interact in social ways with real rats.

For all the talk about artificial intelligence upending the world, its economic effects remain uncertain. There is massive investment in AI but little clarity about what it will produce.