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Real-Time Disaster Response Leveraging...
Today, in the face of increasingly destructive natural disasters, human-machine teaming is essential to helping first responders save lives. Automation of high-skill, labor-intensive tasks can act as a force multiplier by reducing cognitive burden while extracting meaningful insights about a disaster, allowing first responders to focus on higher-order challenges, like decision making. Critical to effective response efforts, machine learning al...
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Life From Space: The Future of Technolo...
Space isn’t the next frontier: it’s the current one. Governments around the world, alongside private actors, are relying on space and space-based communication--and that reliance will only grow. This webcast, hosted by Defense One, will explore: The role that artificial intelligence will play in realizing the future for space capability The role of micro-satellites, AI, and next-generation IT in creating new capabilities in space....
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Fraud Beyond the Pandemic: The Role of...
Public sector agencies’ digital strategies were put to the test by the pandemic. With legacy and manual systems pushed to their limits by the new digital age, agency leaders were faced with the unprecedented task of scaling digital solutions which suddenly became the default channel for millions of interactions with the population. Public sector professionals stepped up to the challenge. However, as the effects of the pandemic ease, indi...
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AI, Machine Learning and Strengthening...
The federal government is dedicated to advancing U.S. leadership in the research, development and use of artificial intelligence (AI), to promote both national security and economic prosperity. Using AI to improve cybersecurity is a logical target for its implementation. It is especially important as the threat landscape continues to evolve. The World Economic Forum (WEF) reports that about 1.7 billion IoT devices have been installed to date i...
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AI in Action: Explore Best Practices on...
Amidst external threats and changing technologies, today’s federal IT leader’s face numerous challenges of security and operations when it comes to developing and preparing for the next generation of AI and data security solutions. Though no shortage of uses for so-called “AIOps” exist, and myriad tools for deployment are available, many leaders remain at a crossroads on what’s next- everything from proper researc...
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Advancing Technology and Innovation For...
The US Department of Commerce is responsible for promoting economic growth, job creation, and international trade. It has a wide range of responsibilities and 13 different bureaus and offices. These include Census Bureau, National Institute of Standards and Technology (NIST), National Oceanic and Atmospheric Administration (NOAA), Patent and Trademark Office (USPTO), and others. The CIO of the Department of Commerce is responsible for developi...
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More Securely Innovate the Enterprise
In the continually changing landscape of modern governance, federal civilian agencies must prioritize secure innovation within their environment. While harnessing technological advancements is crucial for enhancing public services and operational efficiency, it must be done hand in hand with a resolute dedication to cybersecurity. Red Hat hosted the More Securely Innovate the Enterprise event in Denver where government and IT professionals cam...
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Revolutionizing Government Corresponden...
Agencies struggle with a high volume of correspondence from citizens and constituents, often lacking streamlined processes for processes for integrated and timely request handling and relying on Excel spreadsheets and email exchanges among team members introducing operational inefficiencies, lack of visibility, compliance challenges, standardization, and risk of errors. Nuvitek presents a robust AI-driven Citizen Engagement Solution crafted o...
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Accelerating AI Insights Delivery with...
U.S. Department of Justice (DOJ) and U.S. Department of Homeland Security (DHS) agencies are adopting AI/ML to enhance their mission capabilities, though the degree of adoption varies among teams. Common challenges include: Complexities and time needed for iterative cycles to train, test, select and retrain the ML model for optimal prediction accuracy Need for collaboration among cross-functional teams to refine workflows, processes and share...
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