Reservoir computing is a promising computational framework based on recurrent neural networks (RNNs), which essentially maps input data onto a high-dimensional computational space, keeping some parameters of artificial neural networks (ANNs) fixed while updating others. This framework could help to improve the performance of machine learning algorithms, while also reducing the amount of data required to adequately train them.Reservoir computing is a promising computational framework based on recurrent neural networks (RNNs), which essentially maps input data onto a high-dimensional computational space, keeping some parameters of artificial neural networks (ANNs) fixed while updating others. This framework could help to improve the performance of machine learning algorithms, while also reducing the amount of data required to adequately train them.Computer Sciences[#item_full_content]
From cameras to self-driving cars, many of today’s technologies depend on artificial intelligence to extract meaning from visual information. Today’s AI technology has artificial neural networks at its core, and most of the time we can trust these AI computer vision systems to see things the way we do—but sometimes they falter. According to MIT and IBM research scientists, one way to improve computer vision is to instruct the artificial neural networks that they rely on to deliberately mimic the way the brain’s biological neural network processes visual images.From cameras to self-driving cars, many of today’s technologies depend on artificial intelligence to extract meaning from visual information. Today’s AI technology has artificial neural networks at its core, and most of the time we can trust these AI computer vision systems to see things the way we do—but sometimes they falter. According to MIT and IBM research scientists, one way to improve computer vision is to instruct the artificial neural networks that they rely on to deliberately mimic the way the brain’s biological neural network processes visual images.[#item_full_content]
From cameras to self-driving cars, many of today’s technologies depend on artificial intelligence to extract meaning from visual information. Today’s AI technology has artificial neural networks at its core, and most of the time we can trust these AI computer vision systems to see things the way we do—but sometimes they falter. According to MIT and IBM research scientists, one way to improve computer vision is to instruct the artificial neural networks that they rely on to deliberately mimic the way the brain’s biological neural network processes visual images.From cameras to self-driving cars, many of today’s technologies depend on artificial intelligence to extract meaning from visual information. Today’s AI technology has artificial neural networks at its core, and most of the time we can trust these AI computer vision systems to see things the way we do—but sometimes they falter. According to MIT and IBM research scientists, one way to improve computer vision is to instruct the artificial neural networks that they rely on to deliberately mimic the way the brain’s biological neural network processes visual images.Computer Sciences[#item_full_content]
Researchers at The Ohio State University have developed new software to aid in the development, evaluation and demonstration of safer autonomous, or driverless, vehicles.Researchers at The Ohio State University have developed new software to aid in the development, evaluation and demonstration of safer autonomous, or driverless, vehicles.Automotive[#item_full_content]
In 1611, Johannes Kepler—known for his laws of planetary motion—offered a solution to the question concerning the densest possible way to arrange equal-sized spheres. The famed astronomer took on this problem when asked how to stack cannonballs so as to take up the least amount of space. Kepler concluded that the best configuration is a so-called face-centered cubic lattice—an approach commonly used in grocery stores for displaying oranges: Every cannonball should rest in the cavity left by the four cannonballs (lined up in a tight, two-by-two square) lying directly below it. This was merely a conjecture, however, that was not proven until almost 400 years later by a University of Michigan mathematician.In 1611, Johannes Kepler—known for his laws of planetary motion—offered a solution to the question concerning the densest possible way to arrange equal-sized spheres. The famed astronomer took on this problem when asked how to stack cannonballs so as to take up the least amount of space. Kepler concluded that the best configuration is a so-called face-centered cubic lattice—an approach commonly used in grocery stores for displaying oranges: Every cannonball should rest in the cavity left by the four cannonballs (lined up in a tight, two-by-two square) lying directly below it. This was merely a conjecture, however, that was not proven until almost 400 years later by a University of Michigan mathematician.[#item_full_content]
The South Australian government has announced a trial of AI technologies in eight public high schools.The South Australian government has announced a trial of AI technologies in eight public high schools.Consumer & Gadgets[#item_full_content]
A team led by Nagoya University researchers in Japan has successfully predicted crystal orientation by teaching artificial intelligence (AI) using optical photographs of polycrystalline materials. The results were published in APL Machine Learning.A team led by Nagoya University researchers in Japan has successfully predicted crystal orientation by teaching artificial intelligence (AI) using optical photographs of polycrystalline materials. The results were published in APL Machine Learning.Engineering[#item_full_content]
The mind-blowing growth of artificial intelligence poses many questions that have no answers yet, the United Nations admitted Thursday at its AI summit, attended by some exceptionally life-like humanoid robots.The mind-blowing growth of artificial intelligence poses many questions that have no answers yet, the United Nations admitted Thursday at its AI summit, attended by some exceptionally life-like humanoid robots.Machine learning & AI[#item_full_content]
Researchers have combined research with real and robotic insects to better understand how they sense forces in their limbs while walking, providing new insights into the biomechanics and neural dynamics of insects and informing new applications for large legged robots. They presented their findings at the SEB Centenary Conference 2023.Researchers have combined research with real and robotic insects to better understand how they sense forces in their limbs while walking, providing new insights into the biomechanics and neural dynamics of insects and informing new applications for large legged robots. They presented their findings at the SEB Centenary Conference 2023.[#item_full_content]
New theoretical research proves that machine learning on quantum computers requires far simpler data than previously believed. The finding paves a path to maximizing the usability of today’s noisy, intermediate-scale quantum computers for simulating quantum systems and other tasks better than classical digital computers, while also offering promise for optimizing quantum sensors.New theoretical research proves that machine learning on quantum computers requires far simpler data than previously believed. The finding paves a path to maximizing the usability of today’s noisy, intermediate-scale quantum computers for simulating quantum systems and other tasks better than classical digital computers, while also offering promise for optimizing quantum sensors.Machine learning & AI[#item_full_content]
