NG911 and Big Data – Is It TMI?

It’s no secret that, according to VcloudNews, humans and machines generated 2.5 quintillion bytes of information each day on the Internet. How big this is? It would fill 100 million Blu-ray discs, which if stacked, would measure the height of four Eiffel Towers on top of each other. With this much data being generated, we reached a point in our lives when humans can no longer be expected to process information at a usable rate by themselves. This is where the “Age of Artificial Intelligence” needs to come into existence if we are going to utilize this information in any useful way.

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Understandably, AI scares people. We are often afraid of what we don’t understand. However, when used properly, AI can be an assistive technology and not in control. Intelligence is already built into some of the most common events in our lives. For example, after washing, I put my clothes in the dryer. I use the automatic setting, and press start. I’ now free to walk away, go for dinner, or even take a nap if I choose. The dryer is “programmed” to run for a period of time, and on a newer machine, a sensor indicates the moisture content of my delicates. In either case, when criteria is met, the heat shuts off and my close continue to tumble for a period of time. This simple logic has made my dumb clothes dryer “artificially intelligent”.

This can be applied to any process, including many predefined procedures found in public safety 911 centers. Remember though, this is not about replacing the call taker or dispatcher; it is about providing them with relevant additional information, based on predetermined indicators, helping them arrive at a decision point quickly.

Let’s look at two outcomes of the same scenario, first using today’s technology, and then using artificial intelligence to augment and assist in the decision-making process.

David is speeding down the highway in his 2018 GM vehicle. His fiancée Susan is sitting next to him in the passenger seat. A deer runs out impacting him head-on. David violently swerves into the median, where the car overturns four times landing on its roof leaving both occupants unconscious and seriously injured. Sensors in the vehicle detect the high Delta-V (rate of deceleration), the deployment of both passenger and driver airbags, as well as the detection of specific crush zones on the vehicle.

Today:
The in vehicle system (IVS) generates a call to the OnStar call center, flagged as an emergency. The call is routed to an ACD queue staffed by emergency medical dispatchers. Vehicle data and location information appears on their screen. An attempt to communicate with the occupants verbally is initiated, and a three-way conference with the public safety agency responsible is started. Information is passed on verbally to the 911 call center, where local protocol for dispatch is followed.

Tomorrow:
The same situation initiates a different process or workflow. In addition to notifying OnStar and attempting to get a call taker in verbal contact with the vehicle, an IP enabled SIP session is set up into the local Emergency Services IP Network (ESInet) where the 911 call taker is presented with the telematics data and bridged into a three way audio bridge between the vehicle, the OnStar call taker, and the PSAP. The system does an analysis of the data, indicating an 80% chance of entrapment and lower leg trauma on the driver.

The dispatcher is prompted to dispatch recommended resources which include heavy rescue, advanced life support, and a medical air evacuation unit. They also have the ability to edit resources desired and then dispatch with a single button.

Bed counts and staffing levels are examined at the local hospitals, the availability of an orthopedic surgeon and operating room is determined, and based on big data, a destination facility is recommended. A single touch to confirm or edit, and the data is on its way to the hospital where staff can prepare for the patient arrival.

This situation has brought to light the efficient use of AI to determine the best response and action, all while remaining under complete control of a human. Resources become more efficient and effective and are available sooner for other missions. While many may be afraid of AI replacing humans, thanks to Arnold Schwarzenegger in the Terminator movies, I don’t see Skynet being right around the corner.

An added benefit is that AI is available from the cloud, making it affordable to agencies from New York City in a large-scale deployment, or as small as Sparta Police in New Jersey with their two positions, where I cut my teeth on dispatching 37 years ago.

It’s not about building a data center to process data in the building anymore. It’s about using the cloud through multiple resilient paths; sharing the workload with other agencies who will also be available to provide coverage and backup for when “the big one” hits, no matter where or when that might be. This radically changes the curriculum for a Public Safety career, however the skill sets required are also taught for positions in the commercial space, and best practices remain across verticals.

Screenshot 2018-05-06 17.20.35

As I’ve said before, AI is not just about HAL, and getting pod bay doors open.
Besides, in addition to being intelligent, HAL copped an attitude . . .

But in reality he was just programmed that way.

fletch-sig

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