People can develop emotional closeness to artificial intelligence (AI)—under certain conditions, even more so than to other people. This is shown by a new study conducted by a research team led by Prof. Dr. Markus Heinrichs and Dr. Tobias Kleinert from the Department of Psychology at the University of Freiburg and Prof. Dr. Bastian Schiller from Heidelberg University’s Institute of Psychology. Participants felt a sense of closeness especially when they did not know that they were communicating with AI. The results have been published in Communications Psychology.People can develop emotional closeness to artificial intelligence (AI)—under certain conditions, even more so than to other people. This is shown by a new study conducted by a research team led by Prof. Dr. Markus Heinrichs and Dr. Tobias Kleinert from the Department of Psychology at the University of Freiburg and Prof. Dr. Bastian Schiller from Heidelberg University’s Institute of Psychology. Participants felt a sense of closeness especially when they did not know that they were communicating with AI. The results have been published in Communications Psychology.Consumer & Gadgets[#item_full_content]

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective at capturing relationships between nodes and edges in data, but often overlook higher-order, complex connections. To address this challenge, a research team at The Hong Kong Polytechnic University (PolyU) has developed a new heterogeneous graph attention network, revolutionizing the modeling of complex relationships in graph-structured data. This innovation is poised to break through AI application limitations in fields such as neuroscience, logistics, computer vision and biology.As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective at capturing relationships between nodes and edges in data, but often overlook higher-order, complex connections. To address this challenge, a research team at The Hong Kong Polytechnic University (PolyU) has developed a new heterogeneous graph attention network, revolutionizing the modeling of complex relationships in graph-structured data. This innovation is poised to break through AI application limitations in fields such as neuroscience, logistics, computer vision and biology.Machine learning & AI[#item_full_content]

Penn Engineers have developed a novel design for solar-powered data centers that will orbit Earth and could realistically scale to meet the growing demand for AI computing while reducing the environmental impact of data centers.Penn Engineers have developed a novel design for solar-powered data centers that will orbit Earth and could realistically scale to meet the growing demand for AI computing while reducing the environmental impact of data centers.Energy & Green Tech[#item_full_content]

EU lawmakers on Wednesday demanded artificial intelligence providers pay for their use of copyrighted European content as they called for expanded rules to apply to generative AI.EU lawmakers on Wednesday demanded artificial intelligence providers pay for their use of copyrighted European content as they called for expanded rules to apply to generative AI.Machine learning & AI[#item_full_content]

Large language models (LLMs), artificial intelligence (AI) systems that can process and generate texts in various languages, are now widely used by people worldwide. These models have proved to be effective in rapidly sourcing information, answering questions, creating written content for specific applications and writing computer code.Large language models (LLMs), artificial intelligence (AI) systems that can process and generate texts in various languages, are now widely used by people worldwide. These models have proved to be effective in rapidly sourcing information, answering questions, creating written content for specific applications and writing computer code.Computer Sciences[#item_full_content]

When Daniel Graham, an associate professor in the University of Virginia School of Data Science, talks about the future of intelligent systems, he does not begin with the usual vocabulary of cybersecurity or threat mitigation. Instead, he focuses on quality assurance and on how to build digital and physical systems we can trust.When Daniel Graham, an associate professor in the University of Virginia School of Data Science, talks about the future of intelligent systems, he does not begin with the usual vocabulary of cybersecurity or threat mitigation. Instead, he focuses on quality assurance and on how to build digital and physical systems we can trust.Security[#item_full_content]

How inconvenient would it be if you had to manually transfer every contact and photo from scratch every time you switched to a new smartphone? Current artificial intelligence (AI) models face a similar predicament. Whenever a superior new AI model—such as a new version of ChatGPT—emerges, it has to be retrained with massive amounts of data and at a high cost to acquire specialized knowledge in specific fields. Now a Korean research team has developed a “knowledge transplantation” technology between AI models that can resolve this inefficiency.How inconvenient would it be if you had to manually transfer every contact and photo from scratch every time you switched to a new smartphone? Current artificial intelligence (AI) models face a similar predicament. Whenever a superior new AI model—such as a new version of ChatGPT—emerges, it has to be retrained with massive amounts of data and at a high cost to acquire specialized knowledge in specific fields. Now a Korean research team has developed a “knowledge transplantation” technology between AI models that can resolve this inefficiency.Computer Sciences[#item_full_content]