System Bits: Sept. 18


Better AI technique for chemistry predictions CalTech researchers have found a new technique that uses machine learning more effectively to predict how complex chemicals will react to reagents. The tool is a new twist on similar machine learning techniques to find more effective catalysts without having the time-consuming trial-and-error research, making it a time-saver for drug researchers. ... » read more

System Bits: Sept. 11


Researchers ‘teleport’ a quantum gate In a key architectural step for building modular quantum computers, Yale University researchers have demonstrated the teleportation of a quantum gate between two qubits, on demand. [caption id="attachment_24137942" align="alignleft" width="300"] A network overview of the modular quantum architecture demonstrated in the new study.Source: Yale Universit... » read more

System Bits: Sept. 4


Quantum material is both conductor, insulator University of Michigan researchers reminded that quantum materials are a type of odd substance that could be many times more efficient at conducting electricity through a mobile device like an iPhone than the commonly used conductor silicon if physicists could figure out how they work. Now, a University of Michigan physicist has taken a step clo... » read more

System Bits: Aug. 28


Characterizing quantum computers To accelerate and simplify the imposing task of diagnosing quantum computers, a Rice University computer scientist and his colleagues have proposed a method to do just this. The development of a nonconventional method as a diagnostic tool for powerful, next-generation computers that depend on the spooky actions of quantum bits — aka qubits — which are sw... » read more

System Bits: Aug. 21


Two types of computers create faster, less energy-intensive image processor for autonomous cars, security cameras, medical devices Stanford University researchers reminded that the image recognition technology that underlies today’s autonomous cars and aerial drones depends on artificial intelligence. These are the computers that essentially teach themselves to recognize objects like a dog, ... » read more

System Bits: Aug. 14


Machine-learning system determines the fewest, smallest doses that could still shrink brain tumors In an effort to improve the quality of life for patients by reducing toxic chemotherapy and radiotherapy dosing for glioblastoma, the most aggressive form of brain cancer, MIT researchers are employing novel machine-learning techniques. According to the team, glioblastoma is a malignant tumor ... » read more

System Bits: Aug. 7


ML leverages existing hospital patient data to detect trouble Focusing on emergency and critical care patients, a University of Michigan spinout, Fifth Eye, has developed a system that combines a machine learning algorithm with signal processing to monitor the autonomic nervous system of hospital patients and interprets the data every two minutes, which can sometimes be almost two days faster ... » read more

System Bits: July 31


Computers that perceive human emotion As part of the growing field of “affective computing,” MIT researchers have developed a machine-learning model that takes computers a step closer to interpreting our emotions as naturally as humans do. Affective computing uses robots and computers to analyze facial expressions, interpret emotions, and respond accordingly. Applications include, for ... » read more

System Bits: July 24


Computers that mimic the human brain According to a group of researchers led by the Jülich Research Centre in Germany, a computer built to mimic the brain’s neural networks produces similar results to that of the best brain-simulation supercomputer software currently used for neural-signaling research. The custom-built computer named SpiNNaker, which the team said has been tested for ac... » read more

System Bits: July 16


Test tube AI neural network In a significant step towards demonstrating the capacity to program artificial intelligence into synthetic biomolecular circuits, Caltech researchers have developed an artificial neural network made out of DNA that can solve a classic machine learning problem: correctly identifying handwritten numbers. The work was done in the laboratory of Lulu Qian, assistant p... » read more

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