By using an approximate rather than explicit “kernel” function to extract relationships in very large data sets, KAUST researchers have been able to dramatically accelerate the speed of machine learning. The approach promises to greatly improve the speed of artificial intelligence (AI) in the era of big data.
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A research team led by Prof. LI Huiyun from the Shenzhen Institutes of Advanced Technology (SIAT) of the Chinese Academy of Sciences introduced a simple deep reinforcement learning (DRL) algorithm with m-out-of-n bootstrap technique and aggregated multiple deep deterministic policy gradient (DDPG) algorithm structures.
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Demand for electrification of transport has emerged in recent years due to increasing concerns about global warming. The widespread adoption of electric vehicles will result in reduced harmful emissions and cleaner air, among other social and economic benefits. The battery industry is in need of software solutions for battery manufacturers to reduce fabrication and development costs while improving key batteries metrics.
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Rice University computer scientists have overcome a major obstacle in the burgeoning artificial intelligence industry by showing it is possible to speed up deep learning technology without specialized acceleration hardware like graphics processing units (GPUs).
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Scientists from Salk Institute (U.S.), Skoltech (Russia), and the Riken Center for Brain Science (Japan) investigated a theoretical model of how populations of neurons in the visual cortex of the brain may recognize and process faces and their expressions, and how they are organized. The research was recently published in Neural Computation and highlighted on its cover.
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Introductory statistics courses teach us that, when fitting a model to some data, we should have more data than free parameters to avoid the danger of overfitting—fitting noisy data too closely, and thereby failing to fit new data. It is surprising, then, that in modern deep learning the practice is to have orders of magnitude more parameters than data. Despite this, deep networks show good predictive performance, and in fact do better the more parameters they have. Why would that be?
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Researchers at Syracuse University in New York have recently developed a system that can detect the presence of humans in a given environment by analyzing ambient radio frequency (RF) signals. This new system, presented in a paper pre-published on arXiv, employs a convolutional neural network (CNN) trained on a vast amount of RF data.
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Many children with autism face developmental delays, including communication and behavioral challenges and difficulties with social interaction. This makes learning new skills a major challenge, especially in traditional school environments.
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Now researchers are presenting guidelines for how active matter, such as cells and microorganisms, can best be studied using machine learning techniques. The guidelines can help others navigate the new field, which can significantly improve research in active matter.
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Geneva: France’s Minister for Europe and Foreign Affairs, Mr Jean-Yves Le Drian, and Dr Tedros Adhanom Ghebreyesus, Director-General of WHO today met at the Permanent Mission of France to the United Nations in Geneva to discuss the WHO Academy, which will be the world’s largest and most innovative lifelong learning platform in global health.
The WHO Academy aims to reach millions of people worldwide, offering high-tech learning environments at a “hub” in Lyon with “spokes” in the six WHO regions. The Academy will provide learning opportunities for leaders, educators, researchers, health workers, WHO staff and the broader public, and will deliver high quality, multilingual learning, both online and in-person, alongside a cutting-edge simulation centre for health emergencies.
The discussion today follows a Declaration of Intent signed by Emmanuel Macron, President of the French Republic and Dr Tedros on 11 June 2019, and a pledge by France in the amount of US$ 100 million (€ 90 million) to WHO to support the creation of the WHO Academy, which will be established as an internal division within WHO.
“Technology and innovation in learning are creating exciting new opportunities to accelerate progress towards health for all in every country,” said Dr Tedros Adhanom Ghebreyesus, Director-General of WHO. “The WHO Academy will support millions of learners to maximize health impacts with the best evidence, adult learning approaches and state-of-the-art technologies.”
By propelling and strengthening digital innovations and lifelong learning opportunities around the world, the WHO Academy will play an instrumental role towards achieving WHO’s triple billion goal by 2023 and the health Sustainable Development Goals: 1 billion more people will benefit from universal health coverage; 1 billion more will be better protected in health emergencies, and 1 billion more will enjoy improved health and wellbeing.
The funding from France represents a commitment to accelerate the development of the WHO Academy, and confirms the engagement of France — a longstanding partner of WHO and a key actor in global health – and its strong support to the ambitions of 13th WHO General Programme of Work to ensure healthy lives and well-being for all ages.
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https://www.who.int/news-room/detail/24-02-2020-france-pledges-us100-million-for-who-academy
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