AI News, AI to the rescue: 5 ways machine learning can assist during emergency situations

AI to the rescue: 5 ways machine learning can assist during emergency situations

At the wee hours of the night, on January 4 this year, over 9.8 million people experienced a magnitude 4.4 earthquake that rumbled across the San Francisco Bay Area.

In the past 6 months, the United States alone has witnessed four back-to-back storms from one brutal hurricane season, and a massive wildfire with almost 2 million acres of land ablaze.

  Artificial intelligence and machine learning tools can also aggregate and crunch data from multiple resources such as crowd-sourced mapping materials or Google maps.

Machine learning approaches then combine all this data together, remove unreliable data, and identify informative sources to generate heat maps.

DigitalGlobe releases pre- and post-event imagery for select natural disasters each year, and their crowdsourcing platform, Tomnod, will prioritize micro-tasking to accelerate damage assessments.

They took pre and post-disaster imagery and utilized crowd-sourced data analysis and machine learning to identify locations affected by the quakes that had not yet been assessed or received aid.

This initiative is to help emergency call centers improve operations and public safety by using Watson’s speech-to-text and analytics programs.

Using Watson’s speech-to-text function, the context of each call is fed into the AI’s analytics program allowing improvements in how call centers respond to emergencies.

These vital stats can help on-the-ground aid workers to reach the point of crisis sooner and direct their efforts to the needy.

In addition, AI and predictive analytics software can analyze digital content from Twitter, Facebook, and Youtube to provide early warnings, ground-level location data, and real-time report verification.

In fact, AI could also be used to view the unstructured data and background of pictures and videos posted to social channels and compare them to find missing people.

The chatbot can interact with the victim, or other citizens in the vicinity via popular social media channels and ask them to upload information such as location, a photo, and some description.

AI for Digital Response (AIDR) is a free and open platform which uses machine intelligence to automatically filter and classify social media messages related to emergencies, disasters, and humanitarian crises.

AI systems and voice assistants can analyze massive amounts of calls, determine what type of incident occurred and verify the location.

Machine learning approaches such as predictive analytics can also analyze past events to identify and extract patterns and populations vulnerable to natural calamities.

A large number of supervised and unsupervised learning approaches are used to identify at-risk areas and improve predictions of future events.

Predictive machine learning models can also help officials distribute supplies to where people are going, rather than where they were by analyzing real-time behavior and movement of people.

Artificial neural networks take in information such as region, country, and natural disaster type to predict the potential monetary impact of natural disasters.

These advanced drones could expedite access to real-time information at disaster sites using video capturing capabilities and also deliver lightweight physical goods to hard to reach areas.

It has the potential to eliminate outages before they are detected and give disaster response leaders an informed, clearer picture of the disaster area, ultimately saving lives.

This AI Can Help Emergency Responders Diagnose a Heart Attack

For every minute that someone has a heart attack, their chances of survival decreases approximately 10 percent with each minute that passes.

Most heart attacks aren't properly diagnosed until first responders are on the scene, which leaves emergency operators to estimate the severity of the situation while on the phone.

However, one impressive AI could be a key in diagnosing heart attacks by using clues that most human operators wouldn't pick up.

The Corti Signal is an AI developed by the company of the same name that uses real-time speech analysis with advanced machine learning to help read the context clues of critical conversations.

'We have developed a multitude of deep neural networks that listen directly to a sound stream and extract the most important features.

However, Corti heard the patient's breathing and analyzed the rattling noises to determine that his heart had stopped and that the man had fallen because he'd gone into cardiac arrest.

This A.I. eavesdrops on emergency calls to warn of possible cardiac arrests

When you phone 911, you’re patched through to a trained human who is able to properly triage your phone call.

This can be especially powerful in an emergency use case where mistakes can be fatal.” As the company’s Chief Technology Officer Lars Maaloe told us, the technology framework uses deep learning neural networks trained on years of historical emergency calls.

“At Copenhagen EMS, our technology is able to give emergency call takers diagnostic advice in natural language, and it’s integrated directly into the software they are already using.

We are extremely skeptical of the idea of rushing to replace trained medical personnel with A.I., since from both ethical and professional perspective we prefer human contact when it comes to our health.

How AI boosts emergency response in the new age of super disasters

Houston Mayor Sylvester Turner said 911 operators received 56,000 calls in less than 24 hours during one of the first days of the disaster.[2] This mammoth bottleneck prevents dispatchers from quickly directing first responders to people in need who don’t have the time or phone battery life to keep calling for help.

It listens to the content of calls to prioritize the emergency, such as a fender bender versus senior citizens stuck in a house with five feet of water.” During Orlando’s Pulse nightclub massacre, victims had to stay quiet to not draw attention.

That meant they couldn’t call for help, but also couldn’t text 911, because most emergency dispatch centers in the US aren’t equipped to receive text messages, photos and videos — or tap into the detailed location services of mobile phones.[3] Instead, they texted family and friends to call 911.

It sniffs out the ‘ground truth’ by looking at all the other sources contradicting what people are saying in social media.” If someone tweets an image that shows a flooded airport, AI can instantly confirm the geo-location and grab sensor data, photos, and surveillance or drone videos to quickly verify if that image is real or fake, such as an image taken during a different, unrelated disaster.

It can search millions of hours of video footage for a person with blonde hair, a red shirt, and a beard, and then cross-check that against surveillance, drone or body-worn camera video, pinpointing where the missing person was last located and at what time.

A Danish AI system that can identify cardiac arrest from a 911 call 95 percent of the time might soon be coming to the US, according to a report in Fast Company.  Corti, which launched from the company of the same name in 2016, is an example of how AI can augment, not supplant, human healthcare workers.

Once they have dispatched an ambulance, they must try to determine the nature of the injury — and do everything possible to stabilize the patient — by talking to family members or bystanders who are often panicked and generally lack medical training.

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