Agencies today suffer from a “chaos of connectivity” — a morass of systems that are integrated to an extent, but don’t talk to one another — and that prevents agencies from easily accessing large swaths of data. A new platform that addresses all of an organization’s integration patterns can solve the problem.
The emergence of generative AI raises important personnel questions: What skill sets do government agencies need to hire or develop? And how can answer engines remove some technical burdens?
Blanket bans or prohibitions against government agency use of generative AI won’t work. The recent Executive Order on AI takes a much more effective approach to guiding agencies on the proper way to adopt this new technology.
Artificial intelligence (AI), unlike other forms of automation, can learn and generate ideas that elude humans. But what does AI “learning” actually mean? Here are the basics.
This blog details how automation can help agencies successfully enhance their workforce.
Generative AI holds great potential, but the promise comes with risks. Agencies need to make sure their systems serve the public equitably and effectively.
Artificial intelligence is not a singular technology — there are, in fact, many types of AI, each with unique uses and concerns for agencies to consider.
Artificial intelligence technology, including generative AI and large language models, provide many important capabilities, but there are misconceptions about how effective AI actually can be.
Government’s response to national and local crises depends on its ability to collect, analyze and use actionable data. That requires a 360-degree view of available agency data, the kind of perspective that a cloud-based data management platform can provide.
A concept known as explainability helps make artificial intelligence (AI) applications seem trustworthy. But what does explainable AI mean? And how might AI change things for the better? For the worse?