AI decision-making is now common in self-driving cars, patient diagnosis and legal consultation, and it needs to be safe and trustworthy. Researchers have been trying to demystify complex AI models by developing interpretable and transparent models, collectively known as explainable AI methods or explainable AI (XAI) methods. A research team offered their insight specifically into audio XAI models in a review article published in Intelligent Computing.AI decision-making is now common in self-driving cars, patient diagnosis and legal consultation, and it needs to be safe and trustworthy. Researchers have been trying to demystify complex AI models by developing interpretable and transparent models, collectively known as explainable AI methods or explainable AI (XAI) methods. A research team offered their insight specifically into audio XAI models in a review article published in Intelligent Computing.[#item_full_content]
In 2009, an Air France jet crashed into the ocean, leaving no survivors. The plane’s autopilot system shut down and the pilots, having become reliant on their computerized assistant, were unable to correct the situation manually.In 2009, an Air France jet crashed into the ocean, leaving no survivors. The plane’s autopilot system shut down and the pilots, having become reliant on their computerized assistant, were unable to correct the situation manually.[#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]
Imagine doubling the processing power of your smartphone, tablet, personal computer, or server using the existing hardware already in these devices.Imagine doubling the processing power of your smartphone, tablet, personal computer, or server using the existing hardware already in these devices.[#item_full_content]
Recently, professors Risheng Liu from Dalian University of Technology and Zhouchen Lin from Peking University collaborated on an opinion article published in the National Science Review (NSR). Their article delves deeply into AutoML from the perspective of bilevel optimization, achieving unified modeling of various AutoML tasks while exploring challenges and opportunities. This article will be included in the NSR’s special topic on “Automating Machine Learning.”Recently, professors Risheng Liu from Dalian University of Technology and Zhouchen Lin from Peking University collaborated on an opinion article published in the National Science Review (NSR). Their article delves deeply into AutoML from the perspective of bilevel optimization, achieving unified modeling of various AutoML tasks while exploring challenges and opportunities. This article will be included in the NSR’s special topic on “Automating Machine Learning.”[#item_full_content]
Though artificial intelligence decreases human error in experimentation, human experts outperform AI when identifying causation or working with small data sets.Though artificial intelligence decreases human error in experimentation, human experts outperform AI when identifying causation or working with small data sets.[#item_full_content]
In an era where artificial intelligence (AI) is transforming industries from health care to finance, understanding how these digital brains learn is more crucial than ever. Now, two researchers from EPFL, Antonia Sclocchi and Matthieu Wyart, have shed light on this process, focusing on a popular method known as Stochastic Gradient Descent (SGD).In an era where artificial intelligence (AI) is transforming industries from health care to finance, understanding how these digital brains learn is more crucial than ever. Now, two researchers from EPFL, Antonia Sclocchi and Matthieu Wyart, have shed light on this process, focusing on a popular method known as Stochastic Gradient Descent (SGD).[#item_full_content]
Pollsters trying to predict presidential election results and physicists searching for distant exoplanets have at least one thing in common: They often use a tried-and-true scientific technique called Bayesian inference.Pollsters trying to predict presidential election results and physicists searching for distant exoplanets have at least one thing in common: They often use a tried-and-true scientific technique called Bayesian inference.[#item_full_content]
Researchers at the University of Trento, Italy, have developed a novel approach for prime factorization via quantum annealing, leveraging a compact modular encoding paradigm and enabling the factorization of large numbers using D-Wave quantum devices.Researchers at the University of Trento, Italy, have developed a novel approach for prime factorization via quantum annealing, leveraging a compact modular encoding paradigm and enabling the factorization of large numbers using D-Wave quantum devices.[#item_full_content]
About a quarter of Americans get their news on YouTube. With its billions of users and hours upon hours of content, YouTube is one the largest online media platforms in the world.About a quarter of Americans get their news on YouTube. With its billions of users and hours upon hours of content, YouTube is one the largest online media platforms in the world.[#item_full_content]
