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]
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.Computer Sciences[#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]
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.Computer Sciences[#item_full_content]
The Dungeons & Dragons role-playing game franchise says it won’t allow artists to use artificial intelligence technology to draw its cast of sorcerers, druids and other characters and scenery.The Dungeons & Dragons role-playing game franchise says it won’t allow artists to use artificial intelligence technology to draw its cast of sorcerers, druids and other characters and scenery.Machine learning & AI[#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]
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.Computer Sciences[#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]
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.Computer Sciences[#item_full_content]
As state lawmakers rush to get a handle on fast-evolving artificial intelligence technology, they’re often focusing first on their own state governments before imposing restrictions on the private sector.As state lawmakers rush to get a handle on fast-evolving artificial intelligence technology, they’re often focusing first on their own state governments before imposing restrictions on the private sector.Machine learning & AI[#item_full_content]
