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

System Bits: July 10


Foldable electronic switches and sensors Using inexpensive materials, UC Berkeley engineers have created a method to fabricate foldable electronic switches and sensors directly onto paper, along with prototype generators, supercapacitors and other electronic devices for what they said is a range of applications. Besides the fact that it is readily available and low cost, the team pointed ou... » read more

System Bits: July 3


Machine learning network for personalized autism therapy MIT Media Lab researchers have developed a personalized deep learning network for therapy use with children with autism spectrum conditions. They reminded these children often have trouble recognizing the emotional states of people around them, such as distinguishing a happy face from a fearful face. To help with this, some therapists... » read more

System Bits: June 26


I’m enjoying a very busy Design Automation Conference this week in San Francisco, and on the lookout for interesting research topics here. In the meantime, enjoy a few interesting items from around the globe. AI platform diagnoses Zika and other pathogens University of Campinas (UNICAMP) researchers in Brazil have developed an AI platform that can diagnose several diseases with a high deg... » read more

System Bits: June 19


ML algorithm 3D scan comparison up to 1,000 times faster To address the issue of medical image registration that typically takes two hours or more to meticulously align each of potentially a million pixels in the combined scans, MIT researchers have created a machine-learning algorithm they say can register brain scans and other 3D images more than 1,000 times more quickly using novel learning... » read more

System Bits: June 12


Writing complex ML/DL analytics algorithms Rice University researchers in the DARPA-funded Pliny Project believe they have the answer for every stressed-out systems programmer who has struggled to implement complex objects and workflows on ‘big data’ platforms like Spark and thought: “Isn’t there a better way?” Their answer: Yes with PlinyCompute, which the team describes as “a sys... » read more

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