A digital twin is a virtual representation of a real system – a building, the power grid, a city, even a human being – that mimics the characteristics of the system. A digital twin is more than just a computer model, however. It receives data from sensors in the real system to constantly parallel the system’s state.

A digital twin helps people analyze and predict a system’s behavior under different conditions. The systems being twinned are typically very complex and require significant effort to model and track.

Digital twins are useful in a wide variety of domains, including supply chains, health care, buildings, bridges, self-driving cars and retail customer personas to improve efficiency and reliability. For example, a warehouse operator can optimize a warehouse’s performance by exploring the response of its digital twin to various material handling policies and equipment without incurring the cost of making actual changes.

Even a wildfire can be represented by a digital twin. Government agencies can predict the spread of the fire and its impact under different conditions such as wind velocity, humidity and proximity to habitats, and use this information to guide evacuations.

Read the full article at The Conversation.

“A box, bicycle, or traffic cone may be all that is necessary to scare a driverless vehicle into coming to a dangerous stop in the middle of the street or on a freeway off-ramp, creating a hazard for other motorists and pedestrians,” says Qi Alfred Chen, professor of computer science at the University of California, Irvine, and coauthor of a new paper on the findings.

Chen recently presented the paper at the Network and Distributed System Security Symposium in San Diego.

Read the full story on Futurity.

When a driverless car is in motion, one faulty decision by its collision-avoidance system can lead to disaster, but researchers at the University of California, Irvine have identified another possible risk: Autonomous vehicles can be tricked into an abrupt halt or other undesired driving behavior by the placement of an ordinary object on the side of the road.

“A box, bicycle or traffic cone may be all that is necessary to scare a driverless vehicle into coming to a dangerous stop in the middle of the street or on a freeway off-ramp, creating a hazard for other motorists and pedestrians,” said Qi Alfred Chen, UCI professor of computer science and co-author of a paper on the subject presented recently at the Network and Distributed System Security Symposium in San Diego.

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Given the many unknowns about large language models and potential for powerful but flawed chatbots to cause trouble, Google should consider inviting outsiders to do more than just try limited demos of LaMDA, says Sameer Singh, a fellow at Allen Institute for AI and [ICS associate] professor at University of California, Irvine. “There has to be more conversation about how they’re making this safe and testing so outsiders can contribute to those efforts,” he says.

Read the full story on Wired.