Artificial intelligence is helping the Army keep its Stryker armored vehicles in fighting shape. Army officials are using IBM's Watson AI system in combination with onboard sensor data, repair manuals and 15 years of maintenance data to predict mechanical problems before they happen. IBM and the Army's Redstone Arsenal post in Alabama demonstrated Watson's abilities on 350 Stryker vehicles during a field test that began in mid-2016. The Army is now reviewing the results of that test to evaluate Watson's ability to assist human mechanics, and the early insights are encouraging. The Watson AI enabled the pilot program's leaders to create the equivalent of a "personalized medicine" plan for each of the vehicles tested, said Sam Gordy, general manager of IBM U.S. Federal.
Wherever that will lead is, at the time of the writing of this article, still not certain, but regardless of the direction, it's clear that advancing progress with artificial intelligence is a key strategic element for both major parties. Over the course of the past few years, governments around the world have taken strong positions on advancing their strategies around AI adoption. Certainly heading into the new year it seems that the pace of adoption won't be slowing any time soon. At the recent Data for AI conference, we had an opportunity to get insights into how the government plans to continue and accelerate its adoption of AI in an interview with Ellery Taylor, Acting Director of the Office of Acquisition Management and Innovation Division, at the US General Services Administration (GSA). In this article he shares his outlook for the future of AI and how it is being adopted in the government.
NASA-funded researchers applied artificial intelligence to Facebook user location data captured as two fires wrecked northern California in 2018 and gained new insight into people's evacuation movements and behaviors when disaster strikes, which could strengthen future response. The Defense Innovation Unit and Carnegie Mellon University's Software Engineering Institute are collectively crafting datasets to teach AI tools to assess buildings and structures after natural crises occur, and ultimately augment and increase the accuracy of damage estimates. These are two of many examples detailed in a new report from the Partnership for Public Service and Microsoft that explores how the maturing technology can improve disaster resilience and response, and considerations and actions governments should pursue when adopting AI to boost preparedness, recovery and relief. The report suggests agencies improve data collection and access, make proactive instead of reactive moves, collaborate with other organizations--and more. "While some governments, companies and universities have already used AI in this field, most are still in the early stages of use," officials wrote in the report.
You're not the only one nervous about AI -in light of rapid AI growth and adoption, the U.S. Government recently held three Subcommittee Meetings designed to understand the implications posed by the widespread adoption of AI technology in the public and private sectors. So why is the US Government concerned about AI in society, and what role should it be considering in the private sector? Sid Mair, senior vice president of Federal Systems at Penguin Computing, weighs in. Beginning his technology career at NASA, Sid brings more than 30 years of expertise across all aspects of the federal market, including the Department of Defense, Homeland Security, civilian agencies in both classified and unclassified areas, as well the Executive and Congressional branches of government. Penguin Computing most recently built the world's largest AI cluster in the private sector.
Assessing regulatory compliance of personal financial advice is currently a complex manual process. In Australia, only 5%- 15% of advice documents are audited annually and 75% of these are found to be non-compliant(ASI 2018b). This paper describes a pilot with an Australian government regulation agency where Artificial Intelligence (AI) models based on techniques such natural language processing (NLP), machine learning and deep learning were developed to methodically characterise the regulatory risk status of personal financial advice documents. The solution provides traffic light rating of advice documents for various risk factors enabling comprehensive coverage of documents in the review and allowing rapid identification of documents that are at high risk of non-compliance with government regulations. This pilot serves as a case study of public-private partnership in developing AI systems for government and public sector.