The landscape of artificial intelligence (AI) applications has traditionally been dominated by the use of resource-intensive servers centralized in industrialized nations. However, recent years have witnessed the emergence of small, energy-efficient devices for AI applications, a concept known as tiny machine learning (TinyML).The landscape of artificial intelligence (AI) applications has traditionally been dominated by the use of resource-intensive servers centralized in industrialized nations. However, recent years have witnessed the emergence of small, energy-efficient devices for AI applications, a concept known as tiny machine learning (TinyML).[#item_full_content]

In several applications of computer vision, such as augmented reality and self-driving cars, estimating the distance between objects and the camera is an essential task. Depth from focus/defocus is one of the techniques that achieve such a process using the blur in the images as a clue. Depth from focus/defocus usually requires a stack of images of the same scene taken with different focus distances, a technique known as “focal stack.”In several applications of computer vision, such as augmented reality and self-driving cars, estimating the distance between objects and the camera is an essential task. Depth from focus/defocus is one of the techniques that achieve such a process using the blur in the images as a clue. Depth from focus/defocus usually requires a stack of images of the same scene taken with different focus distances, a technique known as “focal stack.”[#item_full_content]

A team of AI researchers at Google’s DeepMind project, working with a colleague from the University of Southern California, has developed a vehicle for allowing large language models (LLMs) to find and use task-intrinsic reasoning structures as a means for improving returned results.A team of AI researchers at Google’s DeepMind project, working with a colleague from the University of Southern California, has developed a vehicle for allowing large language models (LLMs) to find and use task-intrinsic reasoning structures as a means for improving returned results.[#item_full_content]

Researchers have developed a new deep learning model that promises to significantly improve audio quality in real-world scenarios by taking advantage of a previously underutilized tool: Human perception.Researchers have developed a new deep learning model that promises to significantly improve audio quality in real-world scenarios by taking advantage of a previously underutilized tool: Human perception.[#item_full_content]

Behrooz Tahmasebi—an MIT Ph.D. student in the Department of Electrical Engineering and Computer Science (EECS) and an affiliate of the Computer Science and Artificial Intelligence Laboratory (CSAIL)—was taking a mathematics course on differential equations in late 2021 when a glimmer of inspiration struck. In that class, he learned for the first time about Weyl’s law, which had been formulated 110 years earlier by the German mathematician Hermann Weyl.Behrooz Tahmasebi—an MIT Ph.D. student in the Department of Electrical Engineering and Computer Science (EECS) and an affiliate of the Computer Science and Artificial Intelligence Laboratory (CSAIL)—was taking a mathematics course on differential equations in late 2021 when a glimmer of inspiration struck. In that class, he learned for the first time about Weyl’s law, which had been formulated 110 years earlier by the German mathematician Hermann Weyl.[#item_full_content]

Traveling time forecasting, the core component in GPS navigation systems and taxi-hailing apps, has attracted widespread attention. Existing research mostly focuses on independent points like traffic flow prediction or route planning, which ignore globality and lack satisfactory dynamic progress to adopt sophisticated traffic conditions.Traveling time forecasting, the core component in GPS navigation systems and taxi-hailing apps, has attracted widespread attention. Existing research mostly focuses on independent points like traffic flow prediction or route planning, which ignore globality and lack satisfactory dynamic progress to adopt sophisticated traffic conditions.[#item_full_content]

Face mask wearing detection is an important technical approach to improve public health safety and real-time monitoring efficiency. However, under extreme lighting or weather conditions, it is difficult to achieve ideal results with existing object detection or face detection algorithms.Face mask wearing detection is an important technical approach to improve public health safety and real-time monitoring efficiency. However, under extreme lighting or weather conditions, it is difficult to achieve ideal results with existing object detection or face detection algorithms.[#item_full_content]

When designers use inaccurate depictions of the human body, the use of artificial intelligence in some applications might not be as safe for those who don’t fit that body type, according to a new study posted to the arXiv preprint server.When designers use inaccurate depictions of the human body, the use of artificial intelligence in some applications might not be as safe for those who don’t fit that body type, according to a new study posted to the arXiv preprint server.[#item_full_content]

Updating passwords for all users of a company or institution’s internal computer systems is stressful and disruptive to both users and IT professionals. Many studies have looked at user struggles and password best practices. But very little research has been done to determine how a password update campaign can be conducted most efficiently and with minimal IT costs. Until now.Updating passwords for all users of a company or institution’s internal computer systems is stressful and disruptive to both users and IT professionals. Many studies have looked at user struggles and password best practices. But very little research has been done to determine how a password update campaign can be conducted most efficiently and with minimal IT costs. Until now.[#item_full_content]

For a machine learning system comprising multiple machine learning models and input data, researchers at University of Tsukuba developed a theoretical model for evaluating the effect of diversity in machine learning models and input data used in a machine learning system on the reliability of its output.For a machine learning system comprising multiple machine learning models and input data, researchers at University of Tsukuba developed a theoretical model for evaluating the effect of diversity in machine learning models and input data used in a machine learning system on the reliability of its output.[#item_full_content]