The rapid advancement of deep learning algorithms and generative models has enabled the automated production of increasingly striking AI-generated artistic content. Most of this AI-generated art, however, is created by algorithms and computational models, rather than by physical robots.The rapid advancement of deep learning algorithms and generative models has enabled the automated production of increasingly striking AI-generated artistic content. Most of this AI-generated art, however, is created by algorithms and computational models, rather than by physical robots.Robotics[#item_full_content]
The rapid advancement of deep learning algorithms and generative models has enabled the automated production of increasingly striking AI-generated artistic content. Most of this AI-generated art, however, is created by algorithms and computational models, rather than by physical robots.The rapid advancement of deep learning algorithms and generative models has enabled the automated production of increasingly striking AI-generated artistic content. Most of this AI-generated art, however, is created by algorithms and computational models, rather than by physical robots.[#item_full_content]
Deep learning modeling that incorporates physical knowledge is currently a hot topic, and a number of excellent techniques have emerged. The most well-known one is the physics-informed neural networks (PINNs).Deep learning modeling that incorporates physical knowledge is currently a hot topic, and a number of excellent techniques have emerged. The most well-known one is the physics-informed neural networks (PINNs).Machine learning & AI[#item_full_content]
AI holds the potential to revolutionize health care, but it also brings with it a significant challenge: bias. For instance, a dermatologist might use an AI-driven system to help identify suspicious moles. But what if the machine learning model was trained primarily on image data from lighter skin tones, and misses a common form of skin cancer on a darker-skinned patient?AI holds the potential to revolutionize health care, but it also brings with it a significant challenge: bias. For instance, a dermatologist might use an AI-driven system to help identify suspicious moles. But what if the machine learning model was trained primarily on image data from lighter skin tones, and misses a common form of skin cancer on a darker-skinned patient?Machine learning & AI[#item_full_content]
Soft robots inspired by animals can help to tackle real-world problems in efficient and innovative ways. Roboticists have been working to continuously broaden and improve these robots’ capabilities, as this could open new avenues for the automation of tasks in various settings.Soft robots inspired by animals can help to tackle real-world problems in efficient and innovative ways. Roboticists have been working to continuously broaden and improve these robots’ capabilities, as this could open new avenues for the automation of tasks in various settings.[#item_full_content]
Every minute of every day, grid operators monitor the ebb and flow of electricity from generators to substations to homes, businesses, schools, hospitals and more. They make sure that the supply of electricity matches the current demand and often must make snap decisions if there’s a disruption, such as a storm or equipment failure.Every minute of every day, grid operators monitor the ebb and flow of electricity from generators to substations to homes, businesses, schools, hospitals and more. They make sure that the supply of electricity matches the current demand and often must make snap decisions if there’s a disruption, such as a storm or equipment failure.Energy & Green Tech[#item_full_content]
Led by a team from the Institute of Automation, Chinese Academy of Sciences, a new study explores a novel frontier in machine learning. With the rise of large language models, AI is evolving from perceptual intelligence to cognitive intelligence, and human language has become a pivotal component of visual understanding. This study questions whether machines can spontaneously learn a machine language as a visual representation, without relying on human language.Led by a team from the Institute of Automation, Chinese Academy of Sciences, a new study explores a novel frontier in machine learning. With the rise of large language models, AI is evolving from perceptual intelligence to cognitive intelligence, and human language has become a pivotal component of visual understanding. This study questions whether machines can spontaneously learn a machine language as a visual representation, without relying on human language.Machine learning & AI[#item_full_content]
Modern artificial intelligence, such as ChatGPT, is capable of mimicking human behaviors, but the former has more positive outcomes such as cooperation, altruism, trust and reciprocity.Modern artificial intelligence, such as ChatGPT, is capable of mimicking human behaviors, but the former has more positive outcomes such as cooperation, altruism, trust and reciprocity.Machine learning & AI[#item_full_content]
ETRI’s researchers have unveiled a technology that combines generative AI and visual intelligence to create images from text inputs in just 2 seconds, propelling the field of ultra-fast generative visual intelligence.ETRI’s researchers have unveiled a technology that combines generative AI and visual intelligence to create images from text inputs in just 2 seconds, propelling the field of ultra-fast generative visual intelligence.Software[#item_full_content]
The exploration of polarized communities, which consist of two antagonistic subgraphs and include a set of query nodes, is a crucial task in community search on signed networks. Most existing methods either predominantly rely on topological structure while disregarding node attributes or tend to prioritize the global identification of all polarized communities. Thus, they fail to consider two crucial insights.The exploration of polarized communities, which consist of two antagonistic subgraphs and include a set of query nodes, is a crucial task in community search on signed networks. Most existing methods either predominantly rely on topological structure while disregarding node attributes or tend to prioritize the global identification of all polarized communities. Thus, they fail to consider two crucial insights.[#item_full_content]
