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HyperRec: Efficient Recommender Systems with Hyperdimensional Computing


A group of researchers are taking a different approach to AI. The University of California at San Diego, the University of California at Irvine, San Diego State University and DGIST recently presented a paper on a new hardware algorithm based on hyperdimensional (HD) computing, which is a brain-inspired computing model. The new algorithm, called HyperRec, uses data that is modeled with bina... » read more

Final Report: National Security Commission on AI


  In August 2018, Section 1051 of the John S. McCain National Defense Authorization Act for Fiscal Year 2019 established the National Security Commission on Artificial Intelligence as an independent Commission “to consider the methods and means necessary to advance the development of artificial intelligence, machine learning, and associated technologies to comprehensively address the... » read more

Enabling Efficient and Flexible FPGA Virtualization for Deep Learning in the Cloud


SOURCE: Shulin Zeng, Guohao Dai, Hanbo Sun, Kai Zhong, Guangjun Ge, Kaiyuan Guo, Yu Wang, Huazhong Yang(Tsinghua University, Beijing, China).  Published on arXiv:2003.12101 [cs.DC])   ABSTRACT: "FPGAs have shown great potential in providing low-latency and energy-efficient solutions for deep neural network (DNN) inference applications. Currently, the majority of FPGA-based DNN accel... » read more

Learning properties of ordered and disordered materials from multi-fidelity data


Source: Chen, C., Zuo, Y., Ye, W. et al. Learning properties of ordered and disordered materials from multi-fidelity data. Nat Comput Sci 1, 46–53 (2021). https://doi.org/10.1038/s43588-020-00002-x Abstract: "Predicting the properties of a material from the arrangement of its atoms is a fundamental goal in materials science. While machine learning has emerged in recent years as a n... » read more

Improving the Performance Of Deep Neural Networks


Source: North Carolina State University. Authors: Xilai Li, Wei Sun, and Tianfu Wu Abstract: "In state-of-the-art deep neural networks, both feature normalization and feature attention have become ubiquitous. They are usually studied as separate modules, however. In this paper, we propose a light-weight integration between the two schema and present Attentive Normalization (AN). Instead of l... » read more

AI Roadmap: A human-centric approach to AI in aviation


Source: EASA European Union Aviation Safety Agency February 2020 "EASA published its Artificial Intelligence Roadmap 1.0 which establishes the Agency’s initial vision on the safety and ethical dimensions of development of AI in the aviation domain. The AI Roadmap 1.0 is to be viewed as a starting point, intended to serve as a basis for discussion with the Agency’s stakeholders. It... » read more

Plasticine: A Reconfigurable Architecture For Parallel Patterns (Stanford)


Source: Stanford University Stanford University has been developing Plasticine, which allows parallel patterns to be reconfigured. "ABSTRACT Reconfigurable architectures have gained popularity in recent years as they allow the design of energy-efficient accelerators. Fine-grain fabrics (e.g. FPGAs) have traditionally suffered from performance and power inefficiencies due to bit-level ... » read more

Machine Learning Based Prediction: Health Behavior on BP


Source: UC San Diego Jacobs School of Engineering, Po-Han Chiang and Sujit Dey, Mobile Systems Design Lab, Dept. of Electrical and Computer Engineering Using wearable off-the-shelf technology and machine learning, UC San Diego researchers have developed a method to predict an individual’s blood pressure and provide personalized recommendations to lower it based on this data. The researc... » read more

How Neural Networks Think (MIT)


Source: MIT’s Computer Science and Artificial Intelligence Laboratory, David Alvarez-Melis and Tommi S. Jaakkola Technical paper link MIT article General-purpose neural net training Artificial-intelligence research has been transformed by machine-learning systems called neural networks, which learn how to perform tasks by analyzing huge volumes of training data, reminded MIT research... » read more

New AI algorithm monitors sleep with radio waves (MIT & Mass General)


Source: MIT and Massachusetts General Hospital. Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi Jaakkola, Matt Bianchi Monitoring sleep with AI To make it easier to diagnose and study sleep problems, researchers at MIT and Massachusetts General Hospital have devised a new way to monitor sleep stages without sensors attached to the body by using a device that employs an advanced artific... » read more

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