Artificial Intelligence (AI) continues to dominate tech headlines. Now, rather than learning what the technology could mean for government, we're reading about where it's being implemented, and the results being achieved. A recent report found that AI is no longer considered optional, but rather a critical component to managing and using large amounts of data. IT leaders in government are looking to AI to automate routine, data-oriented tasks, ease access to diverse sets of data, prioritize tasks based on the benefit to the organization, and generally keep track of ever-growing streams of data.
The Intelligence Community (IC) has long been a top consumer and analyzer of data in government. Not surprisingly, they have embraced AI technology to supplement the work of analysts by reducing the amount of manual data sorting with machine-assisted, high-level cognitive analysis. AI is being used to help triage so the highly-trained analysts can spend their time making sense of the data collected by looking at the most valuable and seemingly connected pieces.
Health and Human Services (HHS) implemented an AI solution when they needed to quickly procure Hazmat suits to meet the response to an Ebola outbreak. Procurement officials were able to use AI to make like-to-like comparisons among products. After the initial tactical analysis, the acquisition teams were able to use the data gathered on department wide pricing and the terms and conditions to better define parameters for ten categories of purchases.
Despite the successful implementations in many agencies, AI is still in the pilot and introductory phase. The Air Force is making it easier to begin experimenting with AI. Because the DoD has strict rules about what can be put on their networks, it is difficult to introduce new technologies into the production environment. The Air Force has created a workaround with the Air Force Cognitive Engine (ACE) software platform, a software ecosystem that can connect core infrastructures that are required for successful AI development (people, algorithms, data, and computational resources).
HHS is looking to use AI to analyze dated regulations as part of their AI for deregulation project. The pilot has found that 85 percent of HHS regulations from before 1990 have not been edited and are most likely obsolete. Using AI to flag regulations with the term "telegram," for example, will begin the prioritization of data that needs to be looked at by humans.
Artificial Intelligence (AI) is a hot buzzword being thrown around in technical as well as business circles as a way to increase the efficiency of organizations. More than just a buzzword or "next big thing," it is now official policy of the United States. This February the President issued an executive order directing federal agencies to invest more money and resources into the development of artificial intelligence technologies to ensure the U.S. keeps pace with the world in using AI (and related technology) for business, innovation, and defense.
On the heels of the executive order, the DoD outlined its AI plans which include using AI technology to improve situational awareness and decision-making, increasing the safety of operating vehicles in rapidly changing situations, implementing predictive maintenance, and streamlining business processes.
But with all of this focus and excitement around AI, there are many groups raising concerns. Paramount is the federal workforce who sees AI technology potentially taking over their work. A recent survey found that while 50 percent of workers were optimistic that AI would have a positive impact, 29 percent said they could see new technologies being implemented "without regard for how they will benefit employees' current responsibilities." Across government, technology leaders are working to ease fears, stating that technology will take on the rote, manual tasks that humans tend to dread, freeing up people to spend additional time on more strategic, meaningful work.
Another group wary of AI's broad impact are security experts who say that with new, more advanced technologies come new, more advanced threats. In an effort to get in front of these threats, DARPA has launched the Guaranteeing AI Robustness against Deception (GARD) program. This program aims to develop theories, algorithms, and testbeds to aid in the creation of ML models that will defend against a wide range of attacks. Continue reading
The GEOINT Symposium is the nation's largest gathering of geospatial intelligence stakeholders from across industry, academia, and government. Hosted by the United States Geospatial Intelligence Foundation (USGIF), the event has become the gathering place for 4,000+ members of the worldwide geospatial community.
Geospatial Intelligence (GEOINT) was recognized as a discipline in the mid 1990s when the imagery and mapping disciplines were combined into a single DoD agency that was later re-named the National Geospatial-Intelligence Agency (NGA). The combination proved that together, these two technologies provided an incredible opportunity for new intelligence and analysis. The term "GEOINT" was coined by the honorable James Clapper and a community of mapping and imagery intelligence analysts began to grow.
The first GEOINT Symposium was held in a hotel meeting room with the expectation of 100 attendees, but even that first event drew many more to the standing room-only sessions. Since then, the Symposium has grown year after year to become the flagship event for networking and professional development among the defense and intelligence communities and others who use geospatial technology including first responders, law enforcement, and beyond. Continue reading
When your grandma is using her face to unlock her iPhone, you know a technology has gone mainstream. Facial Recognition "is a biometric software application capable of uniquely identifying or verifying a person by comparing and analyzing patterns based on the person's facial contours." In the last four years, there has been a jump in the use of the technology as vendors have begun to use convolutional neural networks (CNN), a deep learning methodology and algorithms, for model training. A National Institute of Standards and Technology test of vendors in 2018 showed a 95% reduction in error rate compared to a similar test completed in 2014. Applications of facial recognition in government include security (access to devices, data, and physical locations), law enforcement (matching video footage of a crime to a database of suspects), and identity verification for travel.
While the technology has come a long way, many argue it still has a way to go before it can be used widely in areas as critical as criminal justice and security. There are calls for regulation by the FTC and other federal entities. While there are accuracy benchmarks that vendors must pass to be used in government, in many cases, the groups used in benchmarks are not as diverse as those that the system will interact with once fielded. Regulation proponents argue that much of the facial recognition technology was designed with the majority of subjects being white males. When the system faces (pun intended) women with dark skin, the accuracy they promise plummets significantly.
With these challenges both in technology and policy, there are a number of events to help sort out the next steps in introducing facial recognition. Continue reading
Local governments are quickly becoming home to some of the most innovative applications of big data, analytics, machine learning, IoT, and artificial intelligence. This embrace of new technology is borne out of necessity. Local governments have had to get creative to meet the needs of citizens, demanding a more digital government, while dealing with tight budgets. Cities have introduced apps that allow citizens to report potholes, they have installed "smart" lighting to conserve energy, government organizations have opened up data to allow people to apply for permits online and see the status of their case, and so much more. Additionally, local governments are taking a new look at how to better use and correlate all of the data they hold to enhance city and public health planning.
In the midst of these exciting applications of new technologies, there are challenges. Privacy is a huge concern, both from a data perspective as well as images and information captured from IoT devices across a city. There's also a communication and publicity challenge. Citizen-centric apps and services do no good if people don't know they exist or don't use them. Similarly, there is a learning curve for employees and citizens, and developing the right training to encourage new technology use is critical. Continue reading