Artificial Intelligence & Machine Learningin Public Safety

December 15, 2019

Artificial Intelligence (AI), and specifically Machine Learning (ML), are being tested in an increasing number of fields, including data-centric environments. Image or text analysis, speech recognition, chatbot interactions, custom machine learning models… all these are elements that could enable the AI journey of a public safety and security organisation. This document dives into different aspects of integrating AI & ML in Public Safety activities, at different levels and in different domains of activity. It presents ethical and regulatory considerations, real examples from Public Safety Answering Points (PSAPs) and Emergency Response Organisations (EROs), and also initiatives that can benefit the public sector greatly, with a series of recommendations at the end.

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NetCom Learning

NetCom Learning have serviced over 80% of Fortune 100 companies. We've helped over 10,000 organizations achieve their business goals by offering a full complement of Managed Learning Services, including IT and business training, curriculum design and content development, learning delivery and administration, consulting and advisory services, and management of learning technology.

OTHER WHITEPAPERS
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Protecting the Human Side of Cybersecurity

whitePaper | August 1, 2020

The majority of enterprise companies have hundreds of apps deployed in the cloud, and that trend is expected to continue, according to a new survey by IDG. That said, 52% of companies find that securing those apps continues to be challenging. But that doesn’t mean that the remaining 48% are fully confident in their cloud app security or are fully aware of all of the facets of cloud security they should be considering. “Cloud security is a broad issue,” says Jim Fulton, Director, Cloud & Edge Protection Solutions for Forcepoint. “From what I’ve seen, about 10% of companies really understand all of the facets.

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Cloud SOS: Treating a case of cloud failure

whitePaper | August 5, 2020

Your cloud-enabled future loomed bright. Major budgets were allocated. Agonising hours were spent herding stakeholders. You’re now using cloud, but your teams and the wider business aren’t reaping the expected benefits. Even with the best intentions, tech leaders are struggling to fill those skills gaps, particularly when it comes to cloud implementation, migration and net new deployments. As a result, the business is held back – from capitalising on the cloud’s potential to deliver value internally (boosting efficiency and agility) and externally (delivering products and services that boost revenue).

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Green Mechanical

whitePaper | April 16, 2020

Since its inception in 1983, Green Mechanical has grown from 1 to 3 offices, taking on larger, more complex projects. However, success came with its own unique challenges. With employees spread across multiple cities and states, Green Mechanical built custom workflows for various systems to ensure collaborators had access to the information they needed. That meant employees had to keep track of multiple remote logins to complete simple tasks like printing job cost reports. Additionally, some employees used digital processes, while others used pen-and-paper. As a result, teams were often working off outdated information.

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WHAT JOBS ARE AFFECTED BY AI?

whitePaper | December 15, 2019

The debate between experts over how automation will affect the future of work has been one of the most active cottage industries in labor economics in recent years. Numerous scholars forecast major disruptions of human work; others minimize those impacts. And yet, the field has nevertheless managed to generate a number of shared insights, with none more consistent than the finding that least welloff will suffer automation’s greatest shocks on the labor market.

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Leveraging a comprehensive reference architecturefor AI that significantly reduces time-to-insight

whitePaper | September 29, 2021

Penguin Computing’s Analytics Practice acts as a single point of reference for hardware, software, architectural design, and much more, enabling organizations to focus their energy on their AI projects. Access this white paper to learn more about this secure, end-to-end solution designed to alleviate AI infrastructure woes.

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WHY MACHINE LEARNING IS THE FUTURE OFPREDICTIVE AND INDUSTRIAL MAINTENANCE

whitePaper | July 10, 2020

There’s no arguing that preventing failures and accidents is critical for industry. Unexpected incidents can grind operations to a halt for extended periods of time and necessitate expensive repairs. Just 12 hours of downtime for an oil production platform could cost six to eight million dollars in lost production opportunity alone. A single day of grounding for a plane costs roughly four to five million dollars. Because of these disruptions, industrial sectors are always on the lookout for newer, better maintenance methods, and the approach on everyone’s lips right now is predictive maintenance. While everyone agrees on the name, there is less consensus on what it means or how to implement it. But to truly unlock the potential of predictive maintenance, it needs to be paired with artificial intelligence (AI) and machine learning (ML).

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Spotlight

NetCom Learning

NetCom Learning have serviced over 80% of Fortune 100 companies. We've helped over 10,000 organizations achieve their business goals by offering a full complement of Managed Learning Services, including IT and business training, curriculum design and content development, learning delivery and administration, consulting and advisory services, and management of learning technology.

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