The photos look like a scene out of science fiction: Robot dogs patrolling the US-Mexico border, climbing over harsh terrain to search for threats and contraband.
But these images are real.
The Department of Homeland Security recently released them as it revealed details about how it’s testing the technology.
The Google-backed AI firm taught a reinforcement learning algorithm to control the fiery plasma inside a tokamak nuclear fusion reactor.
Researchers have developed self-healing, biodegradable, 3D-printed materials that could be used in the development of realistic artificial hands and other soft robotics applications.
The low-cost jelly-like materials, developed by researchers at the University of Cambridge, can sense strain, temperature and humidity. And unlike earlier self-healing robots, they can also partially repair themselves at room temperature.
The results are reported in the journal NPG Asia Materials.
The Martian rover has done the most!
NASA’s Perseverance rover has been on Mars for a full Earth year. During that time, the little robot has gotten pretty familiar with Mars’ terrain and set off a historic mission to find out if life ever existed on the Red Planet.
Perseverance landed on Mars on February 18 with an unprecedented task of collecting samples from the Martian landscape, storing them in tiny tubes, and leaving them on Mars for a future pickup mission.
Here are some of the highlights of that mission so far, and what to look forward to from the Perseverance rover in the future.
In recent years, scientists have introduced a wide variety of robots of all shapes and sizes. Among these are microswimmers, carefully engineered microstructures that can move in water and other liquids.
Microswimmers could have numerous interesting applications, for instance allowing doctors to deliver drugs to targeted regions inside the human body, or scientists to introduce specific substances in water-based environments. While some of these robotic systems achieved remarkable results, most of them were found to be unable to efficiently move inside the human body.
Researchers at the Max Planck Institute for Intelligent Systems (MPI-IS) have recently developed new light-driven microswimmers that could be more suited for navigating within biological systems, including body fluids. These microswimmers, introduced in a paper published in Science Robotics, are simple microparticles based on the two-dimensional (2D) carbon nitride poly(heptazine imide) or PHI.
In a paper published by Science, DeepMind demonstrates how neural networks can improve approximation of the Density Functional (a method used to describe electron interactions in chemical systems). This illustrates deep learning’s promise in accurately simulating matter at the quantum mechanical.
In a paper published in the scientific journal Science, DeepMind demonstrates how neural networks can be used to describe electron interactions in chemical systems more accurately than existing methods.
Density Functional Theory, established in the 1960s, describes the mapping between electron density and interaction energy. For more than 50 years, the exact nature of mapping between electron density and interaction energy — the so-called density functional — has remained unknown. In a significant advancement for the field, DeepMind has shown that neural networks can be used to build a more accurate map of the density and interaction between electrons than was previously attainable.
By expressing the functional as a neural network and incorporating exact properties into the training data, DeepMind was able to train the model to learn functionals free from two important systematic errors — the delocalization error and spin symmetry breaking — resulting in a better description of a broad class of chemical reactions.