Cell phones have simplified so many parts of our lives. Need to schedule a ride? There’s an app for that. Need to deposit a check? Just snap a pic. Need to check in with your doctor? Book a video appointment.

But there’s one major thing they’ve complicated: emergency call response times. Now that people in distress can dial 911 from anywhere, mobile carriers and public safety officials have had to work out different systems for quickly identifying the precise locations of callers. As numerous stories show, in an emergency, every second matters.

Here’s how a 911 call makes it from the scene of an emergency to first responders.

Let’s start with a scenario: You’re at the scene of a car accident. You reach into your pocket for your cell phone and dial 911.

“At the time that the call is placed, your phone will use location services, sort of a special flavor of location services that’s for emergency calls,” Brandon Abley, CTO of the National Emergency Number Association, told Tech Brew. “It’s built into Apple and Google devices and will send the best available location it has for the purposes of routing.”

This “special flavor” can include aggregating data points like nearby wi-fi networks and third-party data from the caller’s phone to help pinpoint their location, he said.

Keep reading here.—KG

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Flooding is becoming more frequent and severe as the climate crisis intensifies, and it’s only likely to get worse. That can make the job of predicting where and when these disasters will hit challenging, especially in areas with little historical or real-time data from which to draw.

A new paper from researchers at Google posits that machine learning could hold some answers. The tech giant claims an AI model, which is trained on publicly available data, can predict river-based flooding up to five days in advance with a reliability “similar to or better” that of current systems.

Google VP of Engineering and Research Yossi Matias said what’s particularly notable about this model is that it’s broadly applicable on a global scale, which could provide more accurate forecasting for developing areas underserved by stream gauges, or water measurement stations along rivers.

“The real question was, how can we build a model that is not going to be built for one locale, but is a global model, where we can take whatever data [on what] happens anywhere in the world and put that into the prediction modeling,” Matias told Tech Brew. “The breakthrough is really to, one, show that we can actually take such a global model and do that and, two, to actually measure that in a systematic way.”

Keep reading here.—PK

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How would you describe your job to someone who doesn’t work in tech?

As senior director of ethical innovation (AI ethics and accessibility), I drive Adobe’s global AI ethics governance structure by developing processes, tools, training, and other resources to ensure that our AI solutions reflect our core values. When we talk about AI ethics, we have outlined what that means for Adobe through our AI ethics principles of accountability, responsibility and transparency, but at the end of the day, my job is to make sure that our AI innovations are developed responsibly to deliver outputs that address the needs of our end-users, while minimizing harm and bias.

That means making sure we are training on safe and inclusive datasets, sending all AI-powered features through our AI ethics assessment, and, finally, ensuring high-risk AI features go through our AI Ethics Committee and Review Board.

Keep reading here.

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