Breakthroughs, discoveries, and DIY tips sent every weekday. If you own an iPhone 16 or an iPhone 17, you'll find different buttons around the sides of your smartphone. Older iPhones have fewer, depending on the model, but all of these buttons are multitaskers: They come with secondary functions as well as primary ones. For example, did you know you can use either of the volume buttons to snap pictures when you're in the Camera app? This can make it easier to capture photos, compared to trying to hit the circular button on screen.

Emergency response services are critical to public safety, with 9-1-1 call-takers playing a key role in ensuring timely and effective emergency operations. To ensure call-taking performance consistency, quality assurance is implemented to evaluate and refine call-takers' skillsets. However, traditional human-led evaluations struggle with high call volumes, leading to low coverage and delayed assessments. We introduce LogiDebrief, an AI-driven framework that automates traditional 9-1-1 call debriefing by integrating Signal-Temporal Logic (STL) with Large Language Models (LLMs) for fully-covered rigorous performance evaluation. LogiDebrief formalizes call-taking requirements as logical specifications, enabling systematic assessment of 9-1-1 calls against procedural guidelines. It employs a three-step verification process: (1) contextual understanding to identify responder types, incident classifications, and critical conditions; (2) STL-based runtime checking with LLM integration to ensure compliance; and (3) automated aggregation of results into quality assurance reports. Beyond its technical contributions, LogiDebrief has demonstrated real-world impact. Successfully deployed at Metro Nashville Department of Emergency Communications, it has assisted in debriefing 1,701 real-world calls, saving 311.85 hours of active engagement. Empirical evaluation with real-world data confirms its accuracy, while a case study and extensive user study highlight its effectiveness in enhancing call-taking performance.

The Bee Cave Police Department announced on Friday they would introduce the new AV8 autonomous drone pilot program to help respond to 911 calls. A police department in Texas may soon be using drones to respond to emergency calls, according to Police Chief Brian Jones. The Bee Cave Police Department offered a demonstration of their autonomous drone system on Friday morning, known as "AV8." The demonstration was part of a pilot project to test the AV8, which also consists of a computer system to keep drones on-target. Both are built by eve Vehicles, which is a start-up based in Austin.

A well-crafted police patrol route design is vital in providing community safety and security in the society. Previous works have largely focused on predicting crime events with historical crime data. The usage of large-scale mobility data collected from Location-Based Social Network, or check-ins, and Point of Interests (POI) data for designing an effective police patrol is largely understudied. Given that there are multiple police officers being on duty in a real-life situation, this makes the problem more complex to solve. In this paper, we formulate the dynamic crime patrol planning problem for multiple police officers using check-ins, crime, incident response data, and POI information. We propose a joint learning and non-random optimisation method for the representation of possible solutions where multiple police officers patrol the high crime risk areas simultaneously first rather than the low crime risk areas. Later, meta-heuristic Genetic Algorithm (GA) and Cuckoo Search (CS) are implemented to find the optimal routes. The performance of the proposed solution is verified and compared with several state-of-art methods using real-world datasets.

Artificial intelligence (AI) is already embedded in a range of digital services. Voice assistants such as Alexa, car routing or content translation all involve machine learning - the most popular form of artificial intelligence technology. There are many warnings these days about AI, such as the ethics behind these machine driven decision systems or threats of automation and the loss of many jobs. Very little is reported about how artificial intelligence can improve public services and can have positive social impact. Smart algorithms combined with cloud computing power allow unprecedented forms of data analysis that would take much longer if humans were doing it.

Artificial intelligence (AI) is already embedded in a range of digital services. Voice assistants such as Alexa, car routing or content translation all involve machine learning – the most popular form of artificial intelligence technology. There are many warnings these days about AI, such as the ethics behind these machine driven decision systems or threats of automation and the loss of many jobs. Very little is reported about how artificial intelligence can improve public services and can have positive social impact. Smart algorithms combined with cloud computing power allow unprecedented forms of data analysis that would take much longer if humans were doing it.

In this video I talk about an AI that is used in Denmark to listen to emergency calls to detect heart attacks before humans so that emergency service can be dispatched earlier. If you would like more information on this topic, please feel free to visit my website and sign up for content updates! I write articles every week on various different topics such as Big Data, Artificial Intelligence & Machine Learning.

I can't think of many better uses for artificial intelligence (AI) technology than to save lives. Danish startup Corti must agree because they built, tested and deployed an AI-enabled system that helps emergency dispatchers make life-saving decisions. Currently, Corti's expertise is in identifying cardiac arrest--when the heart has fully stopped--but the company has plans to extend its life-saving assistance and diagnoses to other ailments. There are numerous out-of-hospital cardiac arrests annually--3,500 in Denmark, 30,000 in the UK and more than 350,000 in the United States. They are often deadly, responsible for 15 percent of all deaths in Western countries.

I can't think of many better uses for artificial intelligence (AI) technology than to save lives. Danish startup Corti must agree because they built, tested and deployed an AI-enabled system that helps emergency dispatchers make life-saving decisions. Currently, Corti's expertise is in identifying cardiac arrest--when the heart has fully stopped--but the company has plans to extend its life-saving assistance and diagnoses to other ailments. There are numerous out-of-hospital cardiac arrests annually--3,500 in Denmark, 30,000 in the UK and more than 350,000 in the United States. They are often deadly, responsible for 15 percent of all deaths in Western countries.

Preventative medicine is all the rage. It can cut long-term healthcare costs, help patients avoid painful procedures, and overall drives longer, better quality lives. So when does preventative medicine make the move from yearly checkups and traditional tests like mammograms and colonoscopies to something more high tech? We may be standing at that turning point right now. Several new AI tools are currently being introduced that have the potential to detect future heart attack risks or even prioritize and direct emergency calls based on the likelihood the patient is having a cardiac event.