A model invented by researchers at MIT and Qatar Computing Research Institute (QCRI) that uses satellite imagery to tag road features in digital maps could help improve GPS navigation. Showing drivers more details about their routes can often help them navigate in unfamiliar locations. Lane counts, for instance, can enable a GPS system to warn drivers of diverging or merging lanes. Incorporating information about parking spots can help drivers plan ahead, while mapping bicycle lanes can help cyclists negotiate busy city streets. Providing updated information on road conditions can also improve planning for disaster relief.

Like the city that hosts the Consumer Electronics Show (CES) there is a lot of noise on the show floor. Sifting through the lights, sounds and people can be an arduous task even for the most experienced CES attendees. Hidden past the North Hall of the Las Vegas Convention Center (LVCC) is a walkway to a tech oasis housed in the Westgate Hotel. This new area hosting SmartCity/IoT innovations is reminiscent of the old Eureka Park complete with folding tables and ballroom carpeting. The fact that such enterprises require their own area separate from the main halls of the LVCC and the startup pavilions of the Sands Hotel is an indication of how urbanization is being redefined by artificial intelligence.

All living organisms carve out environmental niches within which they can maintain relative predictability amidst the ever-increasing entropy around them (1), (2). Humans, for example, go to great lengths to shield themselves from surprise -- we band together in millions to build cities with homes, supplying water, food, gas, and electricity to control the deterioration of our bodies and living spaces amidst heat and cold, wind and storm. The need to discover and maintain such surprise-free equilibria has driven great resourcefulness and skill in organisms across very diverse natural habitats. Motivated by this, we ask: could the motive of preserving order amidst chaos guide the automatic acquisition of useful behaviors in artificial agents? This central problem in artificial intelligence has evoked several candidate solutions, largely focusing on novelty-seeking behaviors (3), (4), (5).

Medical ML/DL system shall facilitate a deep understanding of the underlying healthcare task, which (in most cases) can only be achieved by utilising other forms of patients data. For example, radiology is not all about clinical imaging. Other patient EMR data is crucial for radiologists to derive the precise conclusion for an imaging study. This calls for the integration and data exchange between all healthcare systems. Despite extensive research on data exchange standards for healthcare, there is a huge ignorance in following those standards in healthcare IT systems which broadly affects the quality and efficacy of healthcare data, accumulated through these systems.

Medical image segmentation has been actively studied to automate clinical analysis. Deep learning models generally require a large amount of data, but acquiring medical images is tedious and error-prone. Attention U-Net aims to automatically learn to focus on target structures of varying shapes and sizes; thus, the name of the paper "learning where to look for the Pancreas" by Oktay et al. U-Nets are commonly used for image segmentation tasks because of its performance and efficient use of GPU memory. It aims to achieve high precision that is reliable for clinical usage with fewer training samples because acquiring annotated medical images can be resource-intensive.

This is where one needs to be careful. Our instinct might be to simply exponentiate the log-scale predictions back to raw-scale y. But our instinct would be wrong. Let's see why that is. If you like, you can skip the little bit of math that follows and scroll down to the section called Duan's smearing estimator.

The "Hey, Update My Voice" movement, in partnership with UNESCO, was born out of this context with the goal of teaching respect towards virtual assistants and, in addition, asking tech companies to update their assistants' responses. Because if that happens to them, imagine what happens in real life to real women. Every day around the world, virtual assistants suffer abuse and harassment of all kinds. In Brazil, for example, Lu, the virtual assistant of Magazine Luiza stores, has been victimized by this sort of violence. Worldwide, cases have been reported involving Siri and Alexa, among others.

Increasingly, artificial intelligence is being used in assessing job applications. How is a human to prevail? By making key adjustments to how you present yourself to the technology, advises a South Korean careers consultant Park Seong-jung. He is among the growing number of professionals who focus on helping clients successfully deal with A.I. While this eventuality is yet to take off equally everywhere, some countries like South Korea have seen quicker implementation of hiring technology.

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Over the past few years, the Canada Revenue Agency has been using data analytics and AI, such as machine-learning algorithms that predict tax non-compliance and detect activity in the underground economy. Since 2018, the Department of Justice Canada has licensed the use of Tax Foresight, AI software developed by Blue J Legal Inc. in Toronto, which employs machine learning to predict – with about 90% accuracy, according to the company – how a court might rule on a particular tax scenario. "It's not just about speeding up [analysis] that would otherwise happen," says Benjamin Alarie, co-founder and CEO of Blue J Legal and Osler Chair of Business Law at the University of Toronto. "It's about making [widely] available a really good prediction that would otherwise be the domain of an experienced [lawyer]." AI technology could bring more certainty to the interpretation of tax law, Alarie adds: "Everyone benefits from that."