An AI-infused future has tremendous promise in virtually all aspects of our lives from medicine to education, industry to finance.An AI-infused future has tremendous promise in virtually all aspects of our lives from medicine to education, industry to finance.[#item_full_content]

Researchers from the UCLA Samueli School of Engineering have unveiled an artificial intelligence-based model for computational imaging and microscopy without training with experimental objects or real data.Researchers from the UCLA Samueli School of Engineering have unveiled an artificial intelligence-based model for computational imaging and microscopy without training with experimental objects or real data.[#item_full_content]

Seeking to reduce the computing power needed for the widely used dynamic mode decomposition algorithm, a team of researchers in China led by Guo-Ping Guo developed a quantum-classical hybrid algorithm. They tested their algorithm in three application scenarios: data denoising, scene background extraction, and fluid dynamics analysis. They determined that it can operate with only a small number of samples and has a quantum advantage in the analysis of high-dimensional time series.Seeking to reduce the computing power needed for the widely used dynamic mode decomposition algorithm, a team of researchers in China led by Guo-Ping Guo developed a quantum-classical hybrid algorithm. They tested their algorithm in three application scenarios: data denoising, scene background extraction, and fluid dynamics analysis. They determined that it can operate with only a small number of samples and has a quantum advantage in the analysis of high-dimensional time series.[#item_full_content]

In the rapidly evolving landscape of business and technology, optimizing computational efficiency is key to breaking new ground. At the International Conference for Machine Learning held July 23–29 in Honolulu, researchers presented a paper exploring whether an algorithm called Ford-Fulkerson—which computes the maximum flow in a network—can work faster by using machine learning.In the rapidly evolving landscape of business and technology, optimizing computational efficiency is key to breaking new ground. At the International Conference for Machine Learning held July 23–29 in Honolulu, researchers presented a paper exploring whether an algorithm called Ford-Fulkerson—which computes the maximum flow in a network—can work faster by using machine learning.[#item_full_content]

Since the advent of OpenAI’s ChatGPT, large language models (LLMs) have become significantly popular. These models, trained on vast amounts of data, can answer written user queries in strikingly human-like ways, rapidly generating definitions to specific terms, text summaries, context-specific suggestions, diet plans, and much more.Since the advent of OpenAI’s ChatGPT, large language models (LLMs) have become significantly popular. These models, trained on vast amounts of data, can answer written user queries in strikingly human-like ways, rapidly generating definitions to specific terms, text summaries, context-specific suggestions, diet plans, and much more.[#item_full_content]

ChatGPT and Bard may well be key players in the digital revolution currently underway in computing, coding, medicine, education, industry and finance, but they also are capable of easily being tricked into providing subversive data.ChatGPT and Bard may well be key players in the digital revolution currently underway in computing, coding, medicine, education, industry and finance, but they also are capable of easily being tricked into providing subversive data.[#item_full_content]

Prof. Liu Yang from the University of Chinese Academy of Sciences (UCAS), in collaboration with her colleagues from Renmin University of China and Massachusetts Institute of Technology, has proposed a novel network, namely, the physics-encoded recurrent convolutional neural network (PeRCNN), for modeling and discovery of nonlinear spatio-temporal dynamical systems based on sparse and noisy data.Prof. Liu Yang from the University of Chinese Academy of Sciences (UCAS), in collaboration with her colleagues from Renmin University of China and Massachusetts Institute of Technology, has proposed a novel network, namely, the physics-encoded recurrent convolutional neural network (PeRCNN), for modeling and discovery of nonlinear spatio-temporal dynamical systems based on sparse and noisy data.[#item_full_content]

Mamtaj Akter, a Vanderbilt computer science graduate student in the lab of Pamela Wisniewski, Flowers Family Fellow in Engineering and associate professor of computer science, has co-authored a study evaluating how technology can help people manage mobile privacy and security as a community.Mamtaj Akter, a Vanderbilt computer science graduate student in the lab of Pamela Wisniewski, Flowers Family Fellow in Engineering and associate professor of computer science, has co-authored a study evaluating how technology can help people manage mobile privacy and security as a community.[#item_full_content]

Simon Fraser University computing science assistant professor Jason Peng is leading a research team that is raising motion simulation technology to the next level—and using the game of tennis to showcase just how real virtual athletes’ moves can be.Simon Fraser University computing science assistant professor Jason Peng is leading a research team that is raising motion simulation technology to the next level—and using the game of tennis to showcase just how real virtual athletes’ moves can be.[#item_full_content]

New research from UCL has found that humans were only able to detect artificially generated speech 73% of the time, with the same accuracy in both English and Mandarin.New research from UCL has found that humans were only able to detect artificially generated speech 73% of the time, with the same accuracy in both English and Mandarin.[#item_full_content]