new Govini report<\/a>. \u201cMilitary revolutions \u2018are periods of discontinuous change that render obsolete or subordinate existing means for conducting war.\u2019 The U.S. military can either lead the coming revolution, or fall victim to it.\u201d<\/p>\n\n\n\nWork\u2019s warning comes as DoD leaders appear to be warming up to the necessary research and development investments that will underpin a \u201crevolution\u201d in artificial intelligence, cloud, big data analytics and related key technologies that enable man-machine teaming and autonomous capabilities.<\/p>\n\n\n\n
Of the $7.4 billion spent on AI, big data and cloud in fiscal 2017, AI accounted for only 33 percent but \u201ccontributed significantly to the overall growth in spending from [fiscal] 2012.\u201d DoD spending in the three AI segments \u2013 learning and intelligence, advanced computing and AI systems \u2013 grew the most from 2012 through 2017 by 13.7 percent, 11.6 percent and 16.4 percent, respectively, the Govini report stated.<\/p>\n\n\n\n
Within AI, big winners for 2017 so far include:<\/p>\n\n\n\n
\u00b7 Investment in natural language processing under learning and intelligence, which accounted for nearly $83 million (an increase over 2012 of more almost 17 percent)<\/p>\n\n\n\n
\u00b7 Deep learning spending increased the second-most by 14.9 percent to $238.1 million<\/p>\n\n\n\n
\u00b7 Machine learning followed with an increase of 14.3 percent to $195.5 million<\/p>\n\n\n\n
\u00b7 Within the advanced computing segment, quantum computing spending increased the most by 16.8 percent to reach $68.8 million<\/p>\n\n\n\n
\u00b7 Neuromorphic engineering followed with an increase of 13.7 percent, reaching $97.8 million<\/p>\n\n\n\n
\u00b7 Supercomputing, the largest and most mature sub-segment, had CAGR of 9.7 percent with spending reaching $258.4 million in FY2017.<\/p>\n\n\n\n
Within the AI Systems segment, computer vision \u2013 an area heavy on intelligence, surveillance and reconnaissance \u2013 spending grew the most, by 19 percent between 2012 and 2017. Specifically advancements included the areas of image definition, widening field-of-view and micro chips for processing imagery data, the report noted.<\/p>\n\n\n\n
But the Third Offset is not all about AI.<\/p>\n\n\n\n
\u201cWhile advancing AI has been a priority of DoD, other investments in foundational categories such as big data and cloud must be made in order for AI to reach its full potential,\u201d the report\u2019s authors wrote. To that end, big data technologies related to collecting, processing and analyzing data represent the largest category, accounting for 54.4 percent of total spending from 2012 through 2017.<\/p>\n\n\n\n
Big data can\u2019t be done well without cloud, which is currently the smallest of the three spending categories, growing by 9 percent to reach $1.4 million in 2017, the study said. \u201cHowever, with the recent announcement that DoD is accelerating a shift to the cloud, this number is likely to rise,\u201d according to the report.<\/p>\n\n\n\n
Will it be enough? Only time \u2013 and how DoD and the commercial sector invest in, develop and deploy these technologies \u2013 will tell. The report is clear that an all-in approach is necessary, particularly on the part of the military.<\/p>\n\n\n\n
\u201cThe Third Offset Strategy\u2019s desirable ends are well within reach if tied to an urgent and concerted DoD initiative to pursue them,\u201d wrote Work, who said the initiative requires two parallel efforts.<\/p>\n\n\n\n
\u201cThe first is a robust, focused, and prioritized research and development program designed to explore and field the technologies necessary to implement the strategy\u2019s vision. This is the realm of the Office of the Secretary of Defense, specifically the new Undersecretary of Defense for Research and Engineering,\u201d Work wrote. \u201cThe second is the development of new operational concepts and organizational constructs to exploit these new technologies. This effort will fall primarily to the military services and combatant commands to implement.\u201d<\/p>\n\n\n\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
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