More agencies need AI strategies, says Deloitte report - FedScoop

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Agencies should look to early artificial intelligence adopters in government and industry when crafting strategies for adopting such technologies, according to a new report. Deloitte surveyed about 1,100 executives from U.S. organizations using AI in the third quarter of 2018 -- 10% of them from the public sector -- and found 74% of respondents felt the technologies would be "very" or "critically" important within two years. But government is lagging behind its peers in adopting the new technologies, according to the study. Bill Eggers, executive director of Deloitte's Center for Government Insights, said this reflects agencies' investment and strategizing around AI. "Governments were on the lower end of the AI maturity curve compared to other industries, and it's certainly no surprise that financial services and technology companies were the higher end," Eggers told FedScoop. "The reason why this might be is both a skills gap issue, but also the public sector is investing the least in AI of all the different industries that we looked at."


Solving for good: How new AI use cases can help change the world

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So, you know about the benefits of AI for business -- how it can reduce time spent on manual tasks, improve data-driven decision-making, and allow humans to focus on strategic business initiatives. But have you considered AI for social impact? Initiatives like IBM's Science for Social Good, in which the company partnered with 19 NGO and government agencies, are accelerating the pace of problem-solving to improve global challenges and positively impact human livelihood, while positively influencing business. The cost of computation and the volume of inputs required to solve vast problems using this powerful technology is more affordable and practical than ever before. "AI for good" is becoming an increasingly strategic priority for business and the public sector -- which need each other (and technology) to solve the world's most complex problems.


Natural Language Processing Examples in Government Data

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Tom is an analyst at the US Department of Defense (DoD).1 All day long, he and his team collect and process massive amounts of data from a variety of sources--weather data from the National Weather Service, traffic information from the US Department of Transportation, military troop movements, public website comments, and social media posts--to assess potential threats and inform mission planning. While some of the information Tom's group collects is structured and can be categorized easily (such as tropical storms in progress or active military engagements), the vast majority is simply unstructured text, including social media conversations, comments on public websites, and narrative reports filed by field agents. Because the data is unstructured, it's difficult to find patterns and draw meaningful conclusions. Tom and his team spend much of their day poring over paper and digital documents to detect trends, patterns, and activity that could raise red flags. In response to these kinds of challenges, DoD's Defense Advanced Research Projects Agency (DARPA) recently created the Deep Exploration and Filtering of Text (DEFT) program, which uses natural language processing (NLP), a form of artificial intelligence, to automatically extract relevant information and help analysts derive actionable insights from it.2 Across government, whether in defense, transportation, human services, public safety, or health care, agencies struggle with a similar problem--making sense out of huge volumes of unstructured text to inform decisions, improve services, and save lives.


Deloitte disruption ahead IBM Watson

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Disruption ahead: Deloitte's point of view on IBM Watson8 9. What makes Watson unique In technical terms, IBM Watson is an advanced open-domain question answering (QA) system with deep natural language processing (NLP) capabilities. At this point, the Watson Software as a Service (SaaS) platform is most effectively used to sift through massive amounts of text--documents, emails, social posts, and more--to answer questions in real time. Watson accepts questions posed by the user in natural language and provides the user with a response (or a set of responses) by generating and evaluating various hypotheses around different interpretations of the question and possible answers to it. Unlike keyword-based search engines, which simply retrieve relevant documents, Watson gleans context from the question to provide the user with precise and relevant answers, along with confidence ratings and supporting evidence. Its learning capabilities allow Watson to adapt and improve hypothesis generation and evaluation processes over time through interactions with users. Developers and other users can improve the accuracy of responses by "training" Watson. IBM is also continuing to expand Watson's capabilities to incorporate visualization, reasoning, ability to relate to users, and deeper exploration to gain a broader understanding of the information content. Watson recently launched a new platform service that has the ability to ingest and interpret still and video images, which is another significant type of unstructured data.


It's time to prime the AI engine

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While government is not completely analogous to commercial enterprises, it's a worthy conversation as we all stand to benefit from a more effective and efficient approach to the people's business.