This tough, $700 phone is meant for first responders, but you can buy it if you want

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I have a soft spot for ultra-rugged devices. Sure, they may not always be the best phones out there when it comes to things like cameras or razor-sharp displays, but they’re a fascinating example of what happens when you take a multipurpose device like a smartphone and turn it into a gadget that’s designed for just one thing. In this case, that’s sheer and almost ridiculously overengineered ruggedness. Joining the ring as a new contender for the toughest phone yet is the Sonim XP8, which recently went on sale at AT&T, via Android Police.

As an Android phone, there’s not much to see here. It still runs Android Nougat, instead of the newer Oreo, and internally, there’s a Snapdragon 630 processor, 4GB of RAM, 64GB of storage (which can be…

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Nokia made a smart, fashionable jacket for first responders

Nokia has been creating unconventional products for years, like that weird touchscreen made of ice from 2010. And now, at MWC 2018, the company is showing off a new, offbeat project that's in the works: the CHASE (connected health and safety equipmen…
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iOS 11.3 to add support Advanced Mobile Location for first responders

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In iOS 11.3, Apple is adopting support for Advanced Mobile Location, a much more accurate way for phones making emergency calls to report the location of the caller. The move should spur more governments to add support for AML to assist in responding to life-threatening emergencies.
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iOS 11.3 Will Support Life-Saving Feature That Sends an iPhone’s Precise Location to Responders in Emergencies

Apple today previewed what to expect in iOS 11.3, including new Animoji, health records, ARKit improvements, the ability to turn off Apple’s power management feature on iPhone 6 and newer, and much more.


At the very bottom of its press release, Apple also briefly mentioned a potentially life saving feature coming in iOS 11.3: support for Advanced Mobile Location [PDF] in countries where it is supported.

Additional iOS 11.3 Features: Support for Advanced Mobile Location (AML) to automatically send a user’s current location when making a call to emergency services in countries where AML is supported.

Advanced Mobile Location will recognize when an emergency call is made and, if not already activated, activate an iPhone’s GPS or Wi-Fi to collect the caller’s precise location information. The device then sends an automatic SMS to the emergency services with the caller’s location, before turning the GPS off again.


Advanced Mobile Location is allegedly up to 4,000 times more accurate than current emergency systems, which rely on cell tower location with a radius of up to several miles, or assisted GPS, which can fail indoors.

Advanced Mobile Location must be supported by carriers. EENA, short for the European Emergency Number Association, said the service is fully operational in several European countries, including the United Kingdom, Estonia, Lithuania, Austria, and Iceland, as well as New Zealand, on all mobile networks.

EENA said AML has saved many lives by more accurately pinpointing a person’s position. Accordingly, several minutes of time can be saved, according to the European Telecommunications Standards Institute:

Ambulance Service measurements show that, on average, 30 seconds per call can be saved if a precise location is automatically provided, and several minutes can be saved where callers are unable to verbally describe their location due to stress, injury, language or simple unfamiliarity with an area.

A few years ago, Google implemented a similar AML-based solution called Emergency Location Service into Google Play services that automatically works on Android devices running its Gingerbread operating system or newer.

EENA called on Apple to support Advanced Mobile Location last August, and starting with iOS 11.3 this spring, its wish will be fulfilled.

Related Roundup: iOS 11

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Deep Learning Could Help First Responders Offer Critical Aid in the Wake of Disasters

Applied Deep Learning

From hurricanes to wildfires, 2017 brought the world a number of natural disasters — as well as some tech to deal with them. We have more information than ever following a disaster thanks to unmanned aerial vehicles (UAVs) and sophisticated satellites that can capture images of disasters from the air, but we are still working on ways to process the data so it is valuable for relief efforts. That’s where deep learning comes in, says the World Bank in collaboration with WeRobotics and OpenAerialMap.

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On Jan. 10, 2018, World Bank issued an artificial intelligence (AI) challenge to explore how deep learning could be used in the wake of natural disasters. Deep learning  is what enables AI to recognize patterns in images, sounds, and other data using a neural network that mirrors our own grey matter. This deep learning software is what helps Alexa recognize speech patterns, Google Translate to interpret entire sentences, and Facebook’s AI labs to automatically identify and tag users in uploaded photographs.

AI could be used to catalog aerial images in the critical periods following disasters and help first responders and humanitarian aid agencies aggregate information. Sorting images quickly en masse would make it easier to assess which areas need immediate assistance, what the clearest paths in and out of a disaster site are, and where the most infrastructure damage is.

The AI challenge announcement by WeRobotics founder Patrick Meier focuses on Pacific Island countries, which are vulnerable to earthquakes, tsunamis, storm surges, volcanic eruptions, landslides, and droughts. In the last decade alone, major cyclones have caused millions of dollars of damage in hundreds of islands, including Fiji and Samoa, Meier wrote.

Identifying Trees and Roads

The World Bank’s UAVs for Disaster Resilience Program captured about 80 square km (31 square miles) of high-resolution aerial imagery in the island of Tonga. Now, the World Bank is challenging participants to develop machine learning algorithms that will analyze this imagery without human assistance. In future, that learning will be “applied to new imagery to speed up baseline analysis and damage assessments,” according to the announcement.

In 2013, a magnitude 8 earthquake and its aftershocks destroyed infrastructure in the Solomon Islands. Credit: Wikimedia Commons.

In particular, developers should focus on trees and roads. The algorithms need to identify all coconut, banana, papaya, and mango trees and their locations with at least 80 percent accuracy, since the loss of those critical food production trees would impact both food security for island residents and their economies following a disaster.

The automated imagery analysis should also assess road conditions, like whether they are paved and how many lanes they have. Road assessments for disaster area could allow first responders to plan which roads to use to transport aid effectively.

In an era of increased social media, tailored advertising, and big data, it’s easy to forget that AI can be used for more than just improving home technology and the user experience. This challenge from the World Bank and its collaborators is a welcomed reminder that deep learning could prove useful in humanitarian aid efforts as well.

The post Deep Learning Could Help First Responders Offer Critical Aid in the Wake of Disasters appeared first on Futurism.

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European emergency agency requests Apple enable AML location tracking in iPhone for first responders

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The European Emergency Number Association is asking that Apple go beyond it’s Apple Watch emergency location sending feature, and requests that Advanced Mobile Location be integrated into iOS and the iPhone to assist in locating people in an emergency.
AppleInsider – Frontpage News

Facebook’s new maps will help responders during natural disasters

When natural disasters hit, the role of a site like Facebook switches from a fun social networking platform to a tool that can be used to save lives. Fully aware of the role it plays in natural disasters, Facebook has today announced that it will be…
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