Spacetime is a conceptual model that fuses the three dimensions of space (length, width, and breadth) with the fourth dimension of time. By doing so, a four-dimensional geometric object is created. Researchers have recently used a similar way of thinking to study AI environments, leading to a unique reframing of AI problems in geometric terms.Spacetime is a conceptual model that fuses the three dimensions of space (length, width, and breadth) with the fourth dimension of time. By doing so, a four-dimensional geometric object is created. Researchers have recently used a similar way of thinking to study AI environments, leading to a unique reframing of AI problems in geometric terms.[#item_full_content]
Airport operations are affected by the unpredictable nature of flight times, departure delays, weather conditions, and other factors.Airport operations are affected by the unpredictable nature of flight times, departure delays, weather conditions, and other factors.[#item_full_content]
Imagine a world where your smartphone can detect your mood just by the way you type a message or the tone of your voice. Picture a car that adjusts its music playlist based on your stress levels during rush hour traffic. These scenarios are not just futuristic fantasies.Imagine a world where your smartphone can detect your mood just by the way you type a message or the tone of your voice. Picture a car that adjusts its music playlist based on your stress levels during rush hour traffic. These scenarios are not just futuristic fantasies.[#item_full_content]
Researchers at EPFL have developed a new, uniquely modular machine learning model for flexible decision-making. It is able to input any mode of text, video, image, sound, and time-series and then output any number, or combination, of predictions.Researchers at EPFL have developed a new, uniquely modular machine learning model for flexible decision-making. It is able to input any mode of text, video, image, sound, and time-series and then output any number, or combination, of predictions.[#item_full_content]
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]
