Editor's Note: A need exists within environmental health agencies to increase their capacity to perform in an environment of diminishing resources. With limited resources and increasing demands, we need to seek new approaches to the business of environmental health. Acutely aware of these challenges, NEHA has initiated a partnership with Accela called Building Capacity--a joint effort to educate, reinforce, and build upon successes within the profession using technology to improve efficiency and extend the impact of environmental health agencies.
The Journal is pleased to publish this column from Accela that will provide readers with insight into the Building Capacity initiative, as well as be a conduit for fostering the capacity building of environmental health agencies across the country. The conclusions of this column are those of the author(s) and do not necessarily represent the views of NEHA.
Darryl Booth is the general manager of environmental health at Accela and has been monitoring regulatory and data tracking needs of agencies across the U.S. for almost 20 years. He serves as technical advisor to NEHA's informatics and technology section.
Google Maps rapidly recommends the fastest route, considering vast amounts of crowd-sourced traffic data. Facebook automatically suggests the names of friends in uploaded photos and proposes you "tag" them, thereby validating its assumptions and improving future results. Mobile phones and personal voice assistants rely on voice-to-text, filtering through background noises, languages, and accents. These are commonplace examples of machine learning and artificial intelligence (AI). Many other impactful stories emerge when applied to medicine (e.g., imaging and diagnosis), business transactions, complex climate models, autonomous vehicles, and more. The rapid growth is fueled by low-cost, large-scale computing power and ubiquitous connectivity. Yet, beyond the benefits we receive as consumers, what additional factors should we pursue as environmental health professionals and data managers?
The term AI covers a long list of disciplines that make machines smarter (or make machines seem smarter). AI incorporates concepts such as machine learning, deep learning, natural language processing, image processing, and automated speech recognition. It takes a data scientist to understand it all but we can learn.
Many AI applications begin by training the system to observe previous...