The results of new simulations negate the argument that some objects thought to be black holes are instead hypothetical exotic systems called bosonic stars.
Artificial intelligence (AI) tools have achieved promising results on numerous tasks and could soon assist professionals in various settings. In recent years, computer scientists have been exploring the potential of these tools for detecting signs of different physical and psychiatric conditions.
Depression is one of the most widespread psychiatric disorders, affecting approximately 9.5% of American adults every year. Tools that can automatically detect signs of depression might help to reduce suicide rates, as they would allow doctors to promptly identify people in need of psychological support.
Researchers at Jinhua Advanced Research Institute and Harbin University of Science and Technology have recently developed a deep learning algorithm that could detect depression from a person’s speech. This model, introduced in a paper published in Mobile Networks and Applications, was trained to recognize emotions in human speech by analyzing different relevant features.
Quantum sensing represents one of the most promising applications of quantum technologies, with the aim of using quantum resources to improve measurement sensitivity. In particular, sensing of optical phases is one of the most investigated problems, considered key to developing mass-produced technological devices.
Optimal usage of quantum sensors requires regular characterization and calibration. In general, such calibration is an extremely complex and resource-intensive task—especially when considering systems for estimating multiple parameters, due to the sheer volume of required measurements as well as the computational time needed to analyze those measurements. Machine-learning algorithms present a powerful tool to address that complexity. The discovery of suitable protocols for algorithm usage is vital for the development of sensors for precise quantum-enhanced measurements.
A particular type of machine-learning algorithm known as “reinforcement learning” (RL) relies on an intelligent agent guided by rewards: Depending on the rewards it receives, it learns to perform the right actions to achieve the desired optimization. The first experimental realizations using RL algorithms for the optimization of quantum problems have been reported only very recently. Most of them still rely on prior knowledge of the model describing the system. What is desirable is instead a completely model-free approach, which is possible when the agent’s reward does not depend on the explicit system model.
To escape the uncanny valley, Disney created a humanoid robot that mimics the minor eye and head movements people make during interactions.
When Mount Vesuvius erupted in A.D. 79, it carbonized a book on rulers who followed Alexander the Great. Now, machine learning is deciphering the “lost book.”
Reddit users have tried to force OpenAI’s ChatGPT to violate its own rules on violent content and political commentary, with an alter ego named DAN.
Does the secret to reaching extreme old age lie in lifestyle or genetics? Story at a glance America’s population is aging, with more people living to be 100. Reaching extreme old age depends on multiple factors like location, gender, lifestyle and parental age of death.
This strange behavior doesn’t apply only to light. If you were to get in a rocket and blast off through a rotating universe, you, too, would get caught up in the rotation. And because of that rotation, your movement would double back on itself. When you returned to your starting point, however, you would find yourself arriving before you had left.
In a manner of speaking, a rotating universe would be capable of rotating your future into your own past, allowing you to travel back in time.
An explosion of cyberattacks is infecting servers around the world with crippling ransomware by exploiting a vulnerability that was patched two years ago, it was widely reported on Monday.
The hacks exploit a flaw in ESXi, a hypervisor VMware sells to cloud hosts and other large-scale enterprises to consolidate their hardware resources. ESXi is what’s known as a bare-metal, or Type 1, hypervisor, meaning it’s essentially its own operating system that runs directly on server hardware. By contrast, servers running the more familiar Type 2 class of hypervisors, such as Oracle’s VirtualBox, run as apps on top of a host operating system. The Type 2 hypervisors then run virtual machines that host their own guest OSes, such as Windows, Linux, or, less commonly, macOS.
