Within the next decade, the Internet of Things (IoT) is expected to grow to an estimated 200 billion connected sensors around the world. Already, we see them integrated into everything from household appliances and vehicles to body-worn tech, such as fitness trackers and medical devices.
This multitude of sensors, as well as a host of other new technologies, are continuously generating new data at a rate that is far beyond what any human can parse through, which is one of the reasons why artificial intelligence (AI) is gaining ground as arguably one of the most disruptive, and necessary, technologies of our time. And the hype around AI is justified. It has the potential to change everything: the way we work, the way we live, and even the way we make decisions.
AI is only as good as the underlying data
There’s a catch, though. Unlike flash drives and mobile apps, AI is not a plug-and-play technology. It takes reliable data to make the systems intelligent, and algorithms can only be as good and accurate as the data used to train them. Flawed data sets or information shielded in data silos deliver equally flawed decisions. Additionally, much like humans, AI relies on reference points and experience to develop its intelligence.
What is needed is careful planning and reliable data to make systems intelligent. The data used to train algorithms must be complete and of high quality to begin creating better services, products, and customer experiences. Only then can the benefits of AI be realized.
Harnessing AI for the data deluge in public safety
For public safety agencies, it will be even more important to not only scrutinize the input of data, but also understand the output of AI because lives are at stake. A powerful innovation for the public safety industry has been the introduction of assistive AI. This type of AI works autonomously in the background to uncover links and find connections between historic and live data but leaves humans in charge of decision-making for what actions to take.
Assistive AI enables public safety agencies to navigate the data deluge by analyzing computer-aided dispatch system data quicker to make critical, timely decisions. For example, it can provide real-time insights to call-takers and dispatchers as complex emergencies unfold. As a result, public safety authorities can significantly change workflows and the way their safety business operates in a modern digital world to better meet the situation at hand, ultimately reducing the impact on available resources and the community.
Conclusion
We are currently seeing an explosion of data that will not slow down anytime soon. For organizations to harness this data, AI will be key, but it is important to properly train the algorithms during deployment of any solution and to ensure clean data is feeding the systems. For public safety agencies, it will be even more important to scrutinize both the input and output of data, with assistive AI being the best path forward. The right AI solution can be a critically assistive tool for agencies working to build safer, smarter cities today and for generations to come.
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