The Monroe News reports the Michigan Department of Transportation and the village of Dundee are monitoring the Michigan highway 50 bridge over the river as the water rises. A flood warning is in effect for Dundee, Blissfield and other area communities.

The robotics work programme implements the robotics strategy developed by SPARC, the Public-Private Partnership for Robotics in Europe (see the Strategic Research Agenda). EuRobotics regularly publishes video interviews with projects, so that you can find out more about their activities. You can also see many of these projects at the upcoming European Robotics Forum (ERF) in Tampere Finland March 13-15. Make ROS-Industrial the open-source industrial standard for intelligent industrial robots, and put Europe in a leading position within this global initiative. Presently, potential users are waiting for improved quality and quantity of ROS-Industrial components, but both can improve only when more parties contribute and use ROS-Industrial.

It's called the "grain," a small IoT device implanted into the back of people's skulls to record their memories. Human experiences are simply played back on "redo mode" using a smart button remote. The technology promises to reduce crime, terrorism and simplify human relationships with greater transparency. While this is a description of Netflix's Black Mirror episode, "The Entire History of You," in reality the concept is not as far-fetched as it may seem. This week life came closer to imitating art with the $19 million grant by the US Department of Defense to a group of six universities to begin work on "neurograins."

Amazon's Alexa smart speakers certainly helped normalize the idea of a digital assistant inside the house, but the Google Home is helping spur the expansion of a connected ecosystem. As a result, if all you're looking for is an unobtrusive device in your home to answer questions and act as an entertainment controller, either one of these smart speakers will serve you well. If you're looking for a more contextualized experience, however, or you're merely so tied to the Google ecosystem that you'd rather not play outside of the sandbox, there are reasons to choose a Google Assistant-enabled smart speaker over an Alexa one. Like the fact that there are some commands and services that are compatible with the Google Home and not with the Amazon Echo. I hate the washing the dishes.

AirPods have been a huge success. Future models of Apple's wireless earbuds will reportedly feature better support for digital voice assistant Siri and water resistance. According to Bloomberg, citing unnamed sources, Apple is expected to roll out a version of AirPods as early as this year with hands-free Siri support. Instead of tapping the headphones to activate Siri, users could simply say "Hey Siri" as they would when calling the assistant on an iPhone hands free. Another model expected to launch next year would offer water resistance, allowing AirPods to withstand splashes of water, such as when it's raining.

A retinal fundus image is a photograph of the back of the eye taken through the pupil. For more than 100 years these images have been used for detecting eye disease. Now Google has introduced a surprising new use for retinal images: combined with artificial intelligence, they can also predict a patient's risk of heart attack or stoke. Research arm Google Brain today published a paper in the journal Nature Biomedical Engineering which demonstrates how deep learning models can use retinal images to detect a patient's age, gender, smoking status and systolic blood pressure; calculate cardiovascular risk factors; and predict the risk of major adverse cardiac events occurring over the next five years. A problem with today's mainstream cardiovascular risk calculators such as the Pooled Cohort Equations, Framingham, and Systematic Coronary Risk Evaluation is that they require the input of multiple features such as blood pressure, body mass index, glucose and cholesterol levels, etc. to generate a disease risk result. A study by the American College of Cardiology's Practice Innovation And Clinical Excellence Program concluded that the data required to calculate 10-year risk was available for less than 30% of patients.

Artificial intelligence risks being exploited by terrorists to mount driverless car crashes and cyber attacks because the technology is being rapidly developed without thought for its downsides, Oxford and Cambridge researchers have warned.

These are all introductory content and are the more popular ones you'll find.

Data science is an emerging field, and roles, as well as qualifications, aren't clear-cut at the moment. Given the murkiness surrounding the field and the potential lack of analytics expertise at companies seeking to hire a data scientist or team of data scientists, the task of building an analytics team or hiring a company's first data scientist can be daunting. However, with a brief overview of data scientist types and example questions to assess each type, hiring managers can provide recruiters with a more tailored profile and better assess candidates on skills likely needed to fill the role. Data scientists typically have skills in 3 main areas: mathematics/statistics/machine learning, coding/software engineering, and expertise in the industry in which they seek employment (see chart below). Most mature data scientists have a strong skills in 2 of these 3 areas, yielding software/math folks (who are typically found in tech companies or production roles), math/domain folks (more of a traditional statistician or scientific researcher), or software/domain (less common but often involved in data pipelines and business intelligence roles).

Artificial intelligence (AI) has the potential to revolutionize disease diagnosis and management by performing classification difficult for human experts and by rapidly reviewing immense amounts of images. Despite its potential, clinical interpretability and feasible preparation of AI remains challenging. The traditional algorithmic approach to image analysis for classification previously relied on (1) handcrafted object segmentation, followed by (2) identification of each segmented object using statistical classifiers or shallow neural computational machine-learning classifiers designed specifically for each class of objects, and finally (3) classification of the image (Goldbaum et al., 1996xSee all ReferencesGoldbaum et al., 1996). Creating and refining multiple classifiers required many skilled people and much time and was computationally expensive (Chaudhuri et al., 1989xDetection of blood vessels in retinal images using two-dimensional matched filters. The development of convolutional neural network layers has allowed for significant gains in the ability to classify images and detect objects in a picture (Krizhevsky et al., 2017xImageNet classification with deep convolutional neural networks. These are multiple processing layers to which image analysis filters, or convolutions, are applied.