Recent works have shown that applying Machine Learning to Electronic Health Records (EHR) can strongly accelerate precision medicine. This requires developing models based on diverse EHR sources. Federated Learning (FL) has enabled predictive modeling using distributed training which lifted the need of sharing data and compromising privacy. Since models are distributed in FL, it is attractive to devise ensembles of Deep Neural Networks that also assess model uncertainty. We propose a new FL model called Federated Uncertainty-Aware Learning Algorithm (FUALA) that improves on Federated Averaging (FedAvg) in the context of EHR. FUALA embeds uncertainty information in two ways: It reduces the contribution of models with high uncertainty in the aggregated model. It also introduces model ensembling at prediction time by keeping the last layers of each hospital from the final round. In FUALA, the Federator (central node) sends at each round the average model to all hospitals as well as a randomly assigned hospital model update to estimate its generalization on that hospital own data. Each hospital sends back its model update as well a generalization estimation of the assigned model. At prediction time, the model outputs C predictions for each sample where C is the number of hospital models. The experimental analysis conducted on a cohort of 87K deliveries for the task of preterm-birth prediction showed that the proposed approach outperforms FedAvg when evaluated on out-of-distribution data. We illustrated how uncertainty could be measured using the proposed approach.

The days of construction projects running behind schedule and over budget could soon be over as AI technology tries to solve the U.S. productivity puzzle. Disperse, an AI-powered construction firm, has raised fresh finance to expand into the U.S. in a bid to tackle inefficiencies on building sites. The company's technology uses visual snapshots of construction projects to alert managers about potential problems before they happen. The construction sector has been grappling with low levels of productivity for decades, with underinvestment in technology one of the key factors. Closing the productivity gap in global construction could be worth $1.6 trillion a year, with a third of that coming in the U.S., according to the McKinsey Global Institute.

A few years ago, Paul Allen, the co-founder of Microsoft, published the results of something called the Great Elephant Census, which counted all the savanna elephants in Africa. What it found rocked the conservation world: In the seven years between 2007 and 2014, Africa's savanna elephant population decreased by about a third and was on track to disappear completely from some African countries in as few as 10 years. To reverse that trend, researchers landed on a technology that is rewriting the rules for everything from our household appliances to our cars: artificial intelligence. AI's ability to find patterns in enormous volumes of information is demystifying not just elephant behavior but human behavior -- specifically poacher behavior -- too. "AI can process huge amounts of information to tell us where the elephants are, how many there are," said Cornell University researcher Peter Wrege. "And ideally tell us what they are doing."

A few years ago, Paul Allen, the co-founder of Microsoft, published the results of something called the Great Elephant Census, which counted all the savanna elephants in Africa. What it found rocked the conservation world: In the seven years between 2007 and 2014, Africa's savanna elephant population decreased by about a third and was on track to disappear completely from some African countries in as few as 10 years. To reverse that trend, researchers landed on a technology that is rewriting the rules for everything from our household appliances to our cars: artificial intelligence. AI's ability to find patterns in enormous volumes of information is demystifying not just elephant behavior but human behavior -- specifically poacher behavior -- too. "AI can process huge amounts of information to tell us where the elephants are, how many there are," said Cornell University researcher Peter Wrege. "And ideally tell us what they are doing."

Have you ever felt overwhelmed by the sheer number of images or documents, or hours of video footage you needed to sort through for a report? Training a machine to do the work for you may be the answer. Learn how artificial intelligence can improve your reporting with the new course from the Knight Center for Journalism in the Americas and instructor John Keefe, "Hands-on Machine Learning Solutions for Journalists." The four-week Big Online Course (BOC) runs from Nov. 18 to Dec. 15, 2019 and costs $95, which includes a certificate for those who successfully complete the course requirements. "At the end of this class, students will have a much better understanding of machine learning. They will actually be able to sort documents, especially images, based on the criteria they set up," said Keefe, who uses these techniques in his work as investigations editor at Quartz.

We're looking for a Senior Data Analyst to join the Decision Science team at Zapier, with a focus on either Marketing data or Product & Revenue data. Decision Science & Analytics is responsible for driving data insights, experimentation and quantitative research at Zapier. We work across Product & Revenue, Marketing, Finance and Customer Support, steering our business stakeholders to take data-informed decisions and deepening business understanding of opportunities and weaknesses. Data Analysts in Decision Science are semi-embedded into different business zones, developing tight-knit thought partnerships with key stakeholders. If you are a creative Data Analyst interested in helping to grow a product that helps the world automate their work so they can get back to living, this may be the right challenge for you!

Pocket-size ultrasound devices that cost 50 times less than the machines in hospitals (and connect to your phone). These are just some of the innovations now transforming medicine at a remarkable pace. No one can predict the future, but it can at least be glimpsed in the dozen inventions and concepts below. Like the people behind them, they stand at the vanguard of health care. Neither exhaustive nor exclusive, the list is, rather, representative of the recasting of public health and medical science likely to come in the 2020s.

Recently, the \textit{Tensor Nuclear Norm~(TNN)} regularization based on t-SVD has been widely used in various low tubal-rank tensor recovery tasks. However, these models usually require smooth change of data along the third dimension to ensure their low rank structures. In this paper, we propose a new definition of tensor rank named \textit{tensor Q-rank} by a column orthonormal matrix $\mathbf{Q}$, and further make $\mathbf{Q}$ data-dependent. With $\mathbf{Q}$ satisfying our orthogonal proximal constraint, the data tensor may have a more significant low tensor Q-rank structure than that of low tubal-rank structure. We also provide a corresponding envelope of our rank function and apply it to the low rank tensor completion problem. Then we give an effective algorithm and briefly analyze why our method works better than TNN based methods in the case of complex data with low sampling rate. Finally, experimental results on real-world datasets demonstrate the superiority of our proposed model in the tensor completion problem.

Multidimensional data have become ubiquitous and are frequently involved in situations where the information is aggregated over multiple data atoms. The aggregation can be over time or other features, such as geographical location or group affiliation. We often have access to multiple aggregated views of the same data, each aggregated in one or more dimensions, especially when data are collected or measured by different agencies. However, data mining and machine learning models require detailed data for personalized analysis and prediction. Thus, data disaggregation algorithms are becoming increasingly important in various domains. The goal of this paper is to reconstruct finer-scale data from multiple coarse views, aggregated over different (subsets of) dimensions. The proposed method, called PREMA, leverages low-rank tensor factorization tools to provide recovery guarantees under certain conditions. PREMA is flexible in the sense that it can perform disaggregation on data that have missing entries, i.e., partially observed. The proposed method considers challenging scenarios: i) the available views of the data are aggregated in two dimensions, i.e., double aggregation, and ii) the aggregation patterns are unknown. Experiments on real data from different domains, i.e., sales data from retail companies, crime counts, and weather observations, are presented to showcase the effectiveness of PREMA.

Suggest to Samsung's Virtual Personal Assistant Bixby "Let's talk dirty," and the female voice will respond with a honeyed accent, "I don't want to end up on Santa's naughty list." Ask the same question to the program's male voice and it replies "I've read that soil erosion is a real dirt problem." In South Africa, where I live and conduct my research into gender biases in artificial intelligence, Samsung now offers Bixby in various voices depending on which language you choose. The voices of Julia, Lisa and Stephanie are coquettish and eager. John is clever and straightforward.