Normally, when we think of a rolling object, we tend to imagine a torus (like a bicycle wheel) or a sphere (like a tennis ball) that will always follow a straight path when rolling. However, the world of mathematics and science is always open to exploring new ideas and concepts. This is why researchers have been studying shapes, like oloids, sphericons and more, which do not roll in straight lines.Normally, when we think of a rolling object, we tend to imagine a torus (like a bicycle wheel) or a sphere (like a tennis ball) that will always follow a straight path when rolling. However, the world of mathematics and science is always open to exploring new ideas and concepts. This is why researchers have been studying shapes, like oloids, sphericons and more, which do not roll in straight lines.[#item_full_content]

Are large language models sentient? If they are, how would we know?Are large language models sentient? If they are, how would we know?[#item_full_content]

Machine learning models are now commonly used in various professional fields, while also underpinning the functioning of many smartphone applications, software packages and online services. While most people are exposed to these models and interact with them in some form or the other, very few fully understand their functioning and underlying processes.Machine learning models are now commonly used in various professional fields, while also underpinning the functioning of many smartphone applications, software packages and online services. While most people are exposed to these models and interact with them in some form or the other, very few fully understand their functioning and underlying processes.[#item_full_content]

Lurking just under the surface of popular online applications like Dropbox and Discord is a threat lying in wait to infect users unlucky enough to cross its path.Lurking just under the surface of popular online applications like Dropbox and Discord is a threat lying in wait to infect users unlucky enough to cross its path.[#item_full_content]

Computing is at an inflection point. Moore’s Law, which predicts that the number of transistors on an electronic chip will double each year, is slowing down due to the physical limits of fitting more transistors on affordable microchips. These increases in computer power are slowing down as the demand grows for high-performance computers that can support increasingly complex artificial intelligence models. This inconvenience has led engineers to explore new methods for expanding the computational capabilities of their machines, but a solution remains unclear.Computing is at an inflection point. Moore’s Law, which predicts that the number of transistors on an electronic chip will double each year, is slowing down due to the physical limits of fitting more transistors on affordable microchips. These increases in computer power are slowing down as the demand grows for high-performance computers that can support increasingly complex artificial intelligence models. This inconvenience has led engineers to explore new methods for expanding the computational capabilities of their machines, but a solution remains unclear.[#item_full_content]

Information sharing can lead to better, more accurate predictions when neural network mechanisms are concerned and by using shared information among groups of similarly-minded people, next-item recommendation technology can be improved over the current conventional methods.Information sharing can lead to better, more accurate predictions when neural network mechanisms are concerned and by using shared information among groups of similarly-minded people, next-item recommendation technology can be improved over the current conventional methods.[#item_full_content]

When artificial intelligence robots that have been designed to use algorithms to complete source search tasks, such as search and rescue operations during a fire, encounter a disturbance, they are often unable to complete their task. Proposed solutions have ranged from trying to improve algorithms to introducing additional robots, but these AI-driven robots still encounter fatal problems.When artificial intelligence robots that have been designed to use algorithms to complete source search tasks, such as search and rescue operations during a fire, encounter a disturbance, they are often unable to complete their task. Proposed solutions have ranged from trying to improve algorithms to introducing additional robots, but these AI-driven robots still encounter fatal problems.[#item_full_content]

Label distribution learning (LDL) is a new learning paradigm to deal with label ambiguity. Compared with traditional supervised learning scenarios, annotation with label distribution is more expensive. Direct use of existing active learning (AL) approaches, which aim to reduce the annotation cost in traditional learning, may lead to the degradation of their performance.Label distribution learning (LDL) is a new learning paradigm to deal with label ambiguity. Compared with traditional supervised learning scenarios, annotation with label distribution is more expensive. Direct use of existing active learning (AL) approaches, which aim to reduce the annotation cost in traditional learning, may lead to the degradation of their performance.[#item_full_content]

A research team from Skoltech and other institutions have pioneered a new fast way to distinguish weighted goods at a supermarket. Unlike existing systems, the algorithm will make neural network training faster when new types of produce arrive. The paper is published in the IEEE Access journal.A research team from Skoltech and other institutions have pioneered a new fast way to distinguish weighted goods at a supermarket. Unlike existing systems, the algorithm will make neural network training faster when new types of produce arrive. The paper is published in the IEEE Access journal.[#item_full_content]

Digital Volume Correlation (DVC) is a powerful image analysis technique used in the field of materials science and engineering to study the mechanical behavior and deformation of complex 3D structures. By comparing voxel intensities in a pair of 3D digital images captured at different states of loading or deformation, DVC allows researchers to track and quantify displacements, strains, and other mechanical properties with high precision and non-invasively.Digital Volume Correlation (DVC) is a powerful image analysis technique used in the field of materials science and engineering to study the mechanical behavior and deformation of complex 3D structures. By comparing voxel intensities in a pair of 3D digital images captured at different states of loading or deformation, DVC allows researchers to track and quantify displacements, strains, and other mechanical properties with high precision and non-invasively.[#item_full_content]