Chinese tech giant Tencent on Thursday claimed its new chatbot had some capabilities on par with top US rival ChatGPT, as the global artificial intelligence race heats up.Chinese tech giant Tencent on Thursday claimed its new chatbot had some capabilities on par with top US rival ChatGPT, as the global artificial intelligence race heats up.Internet[#item_full_content]
Precisive real-time prediction of the movement of nearby vehicles or the future trajectory of pedestrians is essential for safe autonomous driving. A research team led by City University of Hong Kong (CityU) recently developed a novel AI system that improves predictive accuracy amid dense traffic and increases computational efficiency by over 85%, offering great potential for enhancing the safety of autonomous vehicles.Precisive real-time prediction of the movement of nearby vehicles or the future trajectory of pedestrians is essential for safe autonomous driving. A research team led by City University of Hong Kong (CityU) recently developed a novel AI system that improves predictive accuracy amid dense traffic and increases computational efficiency by over 85%, offering great potential for enhancing the safety of autonomous vehicles.Automotive[#item_full_content]
When artificial intelligence robots that have been designed to use algorithms to complete source search tasks, such as search and rescue operations during a fire, encounter a disturbance, they are often unable to complete their task. Proposed solutions have ranged from trying to improve algorithms to introducing additional robots, but these AI-driven robots still encounter fatal problems.When artificial intelligence robots that have been designed to use algorithms to complete source search tasks, such as search and rescue operations during a fire, encounter a disturbance, they are often unable to complete their task. Proposed solutions have ranged from trying to improve algorithms to introducing additional robots, but these AI-driven robots still encounter fatal problems.[#item_full_content]
When artificial intelligence robots that have been designed to use algorithms to complete source search tasks, such as search and rescue operations during a fire, encounter a disturbance, they are often unable to complete their task. Proposed solutions have ranged from trying to improve algorithms to introducing additional robots, but these AI-driven robots still encounter fatal problems.When artificial intelligence robots that have been designed to use algorithms to complete source search tasks, such as search and rescue operations during a fire, encounter a disturbance, they are often unable to complete their task. Proposed solutions have ranged from trying to improve algorithms to introducing additional robots, but these AI-driven robots still encounter fatal problems.Computer Sciences[#item_full_content]
Are democratic societies ready for a future in which AI algorithmically assigns limited supplies of respirators or hospital beds during pandemics? Or one in which AI fuels an arms race between disinformation creation and detection? Or sways court decisions with amicus briefs written to mimic the rhetorical and argumentative styles of Supreme Court justices?Are democratic societies ready for a future in which AI algorithmically assigns limited supplies of respirators or hospital beds during pandemics? Or one in which AI fuels an arms race between disinformation creation and detection? Or sways court decisions with amicus briefs written to mimic the rhetorical and argumentative styles of Supreme Court justices?Consumer & Gadgets[#item_full_content]
Label distribution learning (LDL) is a new learning paradigm to deal with label ambiguity. Compared with traditional supervised learning scenarios, annotation with label distribution is more expensive. Direct use of existing active learning (AL) approaches, which aim to reduce the annotation cost in traditional learning, may lead to the degradation of their performance.Label distribution learning (LDL) is a new learning paradigm to deal with label ambiguity. Compared with traditional supervised learning scenarios, annotation with label distribution is more expensive. Direct use of existing active learning (AL) approaches, which aim to reduce the annotation cost in traditional learning, may lead to the degradation of their performance.[#item_full_content]
Label distribution learning (LDL) is a new learning paradigm to deal with label ambiguity. Compared with traditional supervised learning scenarios, annotation with label distribution is more expensive. Direct use of existing active learning (AL) approaches, which aim to reduce the annotation cost in traditional learning, may lead to the degradation of their performance.Label distribution learning (LDL) is a new learning paradigm to deal with label ambiguity. Compared with traditional supervised learning scenarios, annotation with label distribution is more expensive. Direct use of existing active learning (AL) approaches, which aim to reduce the annotation cost in traditional learning, may lead to the degradation of their performance.Computer Sciences[#item_full_content]
Recently published research assessed human trust when collaborating with eyed and non-eyed robots of the same type. The data suggest that humans might not need human-like machines to trust and work with them. Instead, they even seem to collaborate better with machine-like, eyeless machines.Recently published research assessed human trust when collaborating with eyed and non-eyed robots of the same type. The data suggest that humans might not need human-like machines to trust and work with them. Instead, they even seem to collaborate better with machine-like, eyeless machines.[#item_full_content]
The current crop of AI robots has made giant leaps when it comes to tiny activities.The current crop of AI robots has made giant leaps when it comes to tiny activities.[#item_full_content]
A research team from Skoltech and other institutions have pioneered a new fast way to distinguish weighted goods at a supermarket. Unlike existing systems, the algorithm will make neural network training faster when new types of produce arrive. The paper is published in the IEEE Access journal.A research team from Skoltech and other institutions have pioneered a new fast way to distinguish weighted goods at a supermarket. Unlike existing systems, the algorithm will make neural network training faster when new types of produce arrive. The paper is published in the IEEE Access journal.[#item_full_content]
