Technology is changing how we practice medicine. Sensors and wearables are getting smaller and cheaper, and algorithms are becoming powerful enough to predict medical outcomes. Yet despite rapid advances, healthcare lags behind other industries in truly putting these technologies to use. A major barrier to entry is the cross-disciplinary approach required to create such tools, requiring knowledge from many people across many fields. We aim to drive the field forward by unpacking that barrier, providing a brief introduction to core concepts and terms that define digital medicine. Specifically, we contrast "clinical research" versus routine "clinical care," outlining the security, ethical, regulatory, and legal issues developers must consider as digital medicine products go to market. We classify types of digital measurements and how to use and validate these measures in different settings. To make this resource engaging and accessible, we have included illustrations and figures ...

Our first article (What is AI?) highlighted that Artificial intelligence already has a huge impact on our lives. People are concerned about AI replacing jobs or being misused, with good reason. So here we take a broad look at the ethics of AI. AI is software: it's no more intrinsically good or bad than a database or website. Because AI has great power, the way we apply it is critically important.

Police departments across the nation are generating leads and making arrests by feeding celebrity photos, CGI renderings, and manipulated images into facial recognition software. Often unbeknownst to the public, law enforcement is identifying suspects based on "all manner of'probe photos,' photos of unknown individuals submitted for search against a police or driver license database," a study published on Thursday by the Georgetown Law Center on Privacy and Technology reported. The new research comes on the heels of a landmark privacy vote on Tuesday in San Francisco, which is now the first US city to ban the use of facial recognition technology by police and government agencies. A recent groundswell of opposition has led to the passage of legislation that aims to protect marginalized communities from spy technology. These systems "threaten to fundamentally change the nature of our public spaces," said Clare Garvie, author of the study and senior associate at the Georgetown Law Center on Privacy and Technology.

Here's what you need to know in business news. The city's Board of Supervisors voted on Tuesday to prohibit the use of facial recognition technology within city limits. It's a somewhat symbolic move: The police there don't currently use the stuff, and the places where it is in use -- seaports and airports -- are under federal jurisdiction and therefore unaffected by the new regulation. The major television networks tried to sell their fall advertising slots in an annual pageant known as the upfronts. In a week of star-studded presentations, skits and boozy mingling, representatives of major advertisers flocked to New York to see what the networks have in store.

Field canals improvement projects (FCIPs) are one of the ambitious projects constructed to save fresh water. To finance this project, Conceptual cost models are important to accurately predict preliminary costs at the early stages of the project. The first step is to develop a conceptual cost model to identify key cost drivers affecting the project. Therefore, input variables selection remains an important part of model development, as the poor variables selection can decrease model precision. The study discovered the most important drivers of FCIPs based on a qualitative approach and a quantitative approach. Subsequently, the study has developed a parametric cost model based on machine learning methods such as regression methods, artificial neural networks, fuzzy model and case-based reasoning.

A "wireless fingerprint" which exploits hardware imperfections unique to each device is a potentially powerful tool for wireless security. Such a fingerprint should be able to distinguish between devices sending the same message, and should be robust against standard spoofing techniques. Since the information in wireless signals resides in complex baseband, in this paper, we explore the use of neural networks with complex-valued weights to learn fingerprints using supervised learning. We demonstrate that, while there are potential benefits to using sections of the signal beyond just the preamble to learn fingerprints, the network cheats when it can, using information such as transmitter ID (which can be easily spoofed) to artificially inflate performance. We also show that noise augmentation by inserting additional white Gaussian noise can lead to significant performance gains, which indicates that this counter-intuitive strategy helps in learning more robust fingerprints. We provide results for two different wireless protocols, WiFi and ADS-B, demonstrating the effectiveness of the proposed method.

As lawmakers grapple with how to shape legislation dealing with artificial intelligence, the clerk of the House is developing an AI tool to automate the process of analyzing differences between bills, amendments and current laws. That's according to Robert F. Reeves, the deputy clerk of the House, who on Friday told the Select Committee on the Modernization of Congress that his office is working on an "artificial intelligence engine" that may be ready as soon as next year. The idea, Reeves said, is to offer members and staff a tool that would accurately compare legislative text. He said it's already available to Office of Legislative Counsel staffers, who then must check the accuracy with human intelligence. It's about 90 percent there, he told the panel.

Artificial Intelligence (AI) is deemed to be one of the biggest technological innovations of this decade. However, like with all innovations, we must focus on fundamental applications first before we quite literally reach for the stars. AI has huge potential for wireless networks and for the people that must protect them, as well as those who try and attack them. So how will AI come into play this year and how will it shape the future? Let's focus on wireless networks and cybersecurity first

Tree ensembles, such as random forests and AdaBoost, are ubiquitous machine learning models known for achieving strong predictive performance across a wide variety of domains. However, this strong performance comes at the cost of interpretability (i.e. users are unable to understand the relationships a trained random forest has learned and why it is making its predictions). In particular, it is challenging to understand how the contribution of a particular feature, or group of features, varies as their value changes. To address this, we introduce Disentangled Attribution Curves (DAC), a method to provide interpretations of tree ensemble methods in the form of (multivariate) feature importance curves. For a given variable, or group of variables, DAC plots the importance of a variable(s) as their value changes. We validate DAC on real data by showing that the curves can be used to increase the accuracy of logistic regression while maintaining interpretability, by including DAC as an additional feature. In simulation studies, DAC is shown to out-perform competing methods in the recovery of conditional expectations. Finally, through a case-study on the bike-sharing dataset, we demonstrate the use of DAC to uncover novel insights into a dataset.

Wing, an offshoot of Google's parent company, Alphabet, will launch drone deliveries to one of Finland's most populous areas next month according to a recent blog post from the company. Pilot deliveries will be rolled out in the Vousari district of Finland's capital, Helsinki, and will deliver products from gourmet supermarket Herkku foods and Cafe Monami. As noted by Wing, deliveries will include'fresh Finnish pastries, meatballs for two, and a range of other meals and snacks' that can be delivered in minutes. Wing will launch deliveries for customers in Finland starting next month. Wing, the first commercial drone company approved by the FAA in the U.S. will start delivering in Virginia. The drones is powered entirely by electric and can fly up to 120 km/h (almost 75 mph).