Writers argue advancements in specialized robots for hospitals, medical practices could hit the market and save lives.

Canadian startup DarwinAI and researchers from the University of Waterloo are open-sourcing COVID-Net, a convolutional neural network that aims to detect COVID-19 in X-ray imagery. In response to the pandemic, a global community of health care and AI researchers have produced a number of AI systems for identifying COVID-19 in CT scans. Companies like Alibaba and AI startups RadLogics and Lunit claim they've created systems capable of recognizing COVID-19 in X-ray or CT scans with more than 90% accuracy. Early work from Chinese medical researchers and a system published in the journal Radiology last week demonstrated similar results. Like other companies making AI to detect COVID-19 from chest X-rays, DarwinAI said it's creating COVID-Net and the accompanying COVIDx data set to give doctors a way to quickly triage and screen potential cases.

To effect behavior change a successful algorithm must make high-quality decisions in real-time. For example, a mobile health (mHealth) application designed to increase physical activity must make contextually relevant suggestions to motivate users. While machine learning offers solutions for certain stylized settings, such as when batch data can be processed offline, there is a dearth of approaches which can deliver high-quality solutions under the specific constraints of mHealth. We propose an algorithm which provides users with contextualized and personalized physical activity suggestions. This algorithm is able to overcome a challenge critical to mHealth that complex models be trained efficiently. We propose a tractable streamlined empirical Bayes procedure which fits linear mixed effects models in large-data settings. Our procedure takes advantage of sparsity introduced by hierarchical random effects to efficiently learn the posterior distribution of a linear mixed effects model. A key contribution of this work is that we provide explicit updates in order to learn both fixed effects, random effects and hyper-parameter values. We demonstrate the success of this approach in a mobile health (mHealth) reinforcement learning application, a domain in which fast computations are crucial for real time interventions. Not only is our approach computationally efficient, it is also easily implemented with closed form matrix algebraic updates and we show improvements over state of the art approaches both in speed and accuracy of up to 99% and 56% respectively.

Machine learning-based multi-label medical text classifications can be used to enhance the understanding of the human body and aid the need for patient care. We present a broad study on clinical natural language processing techniques to maximise a feature representing text when predicting medical codes on patients with multi-morbidity. We present results of multi-label medical text classification problems with 18, 50 and 155 labels. We compare several variations to embeddings, text tagging, and pre-processing. For imbalanced data we show that labels which occur infrequently, benefit the most from additional features incorporated in embeddings. We also show that high dimensional embeddings pre-trained using health-related data present a significant improvement in a multi-label setting, similarly to the way they improve performance for binary classification. High dimensional embeddings from this research are made available for public use.

Any motor vehicle enthusiast will tell you, with uncanny accuracy, the specs of the latest luxury rides. They will verbally pick a Lamborghini apart and elucidate on its superior qualities. Collaborative robot market is projected to grow at CAGR of 41.8%

This article was originally posted by Shahul ES on the Neptune blog. Exploratory data analysis is one of the most important parts of any machine learning workflow and Natural Language Processing is no different. But which tools you should choose to explore and visualize text data efficiently? In this article, we will discuss and implement nearly all the major techniques that you can use to understand your text data and give you a complete(ish) tour into Python tools that get the job done. In this article, we will use a million news headlines dataset from Kaggle. Now, we can take a look at the data. The dataset contains only two columns, the published date, and the news heading.

Amazon's voice assistant, Alexa, can now help users who are worried about having been infected with novel coronavirus. According to the company, users can now query any device equipped with Alexa with phrases like'Alexa, what do I do if I think I have coronavirus?' and the assistant will begin to quiz them about their symptoms. The assistant will then provide users with information pulled from the Centers for Disease Control and Prevention in an effort to provide sound advice on what to do. Amazon's line of Alexa-enabled devices like the Echo (pictured) can now provide users guidance on what to do if they think they may have novel coronavirus As a part of the update, users can now also ask Alexa to'sing along' while they wash their hands to help them time the task for 20 seconds - the recommended amount of time for proper sanitation. That feature is currently available in Australia, Brazil, Canada, France, India, the UK, and the US and mirrors a similar feature rolled out by Google on its home assistants. The feature most closely mirrors one rolled out by Apple this week which updated its own voice assistant, Siri, to help provide users with guidance on coronavirus.

Experts are set to unleash a'pandemic drone' to help limit the spread of coronavirus. The drone is fitted with sensors and computer vision, allowing it to monitor and detect people with infectious respiratory conditions. The system could also identify people sneezing and coughing in crowds, offices, airports, cruise ships, aged care homes and other places where groups of people may work or congregate. Its creators hope to deploy the drone in six months and in various hotspots where'the most amount of detection is currently required.' Experts are set to unleash a'pandemic drone' to help limit the spread of coronavirus.

Spending on artificial intelligence in Asia Pacific is set to soar over the next five years as the demand for deep learning ramps up. By 2024, the APAC region is estimated to account for about 30 per cent of the global AI platform revenue at approximately U.S.$97.5 billion, according to research firm GlobalData. But that figure is expected to increase with businesses and the rising number of start-ups specialising in the technology and advancement in the space supporting higher computational capabilities. Sunil Kumar Verma, lead ICT analyst at GlobalData, said the APAC market is already deploying deep learning-based AI technology for offline automation, safety and security for businesses and assets. "In addition, AI hardware optimisation with increased computing speed on small devices will result in the cost reduction and drive deep learning adoption across the region," Verma said.

Ethics of AI: While artificial intelligence promises significant benefits, there are concerns it could make unethical decisions. Prefer to listen to this story? Here it is in audio format. Artificial intelligence (AI) is fast becoming important for accountants and businesses, and how it is used raises several ethical issues and questions. While autonomous AI algorithms teach themselves, concerns have been raised that some machine learning techniques are essentially "black boxes" that make it technically impossible to fully understand how the machine arrived at a result. It will become increasingly important to develop AI algorithms that are transparent to inspection, auditable, secure and robust against manipulation and misuse.