{"id":2558,"date":"2020-09-03T20:29:10","date_gmt":"2020-09-03T20:29:10","guid":{"rendered":"https:\/\/techclot.com\/index.php\/2020\/09\/03\/ais-data-hunger-will-drive-intelligence-collection\/"},"modified":"2020-09-03T20:29:10","modified_gmt":"2020-09-03T20:29:10","slug":"ais-data-hunger-will-drive-intelligence-collection","status":"publish","type":"post","link":"https:\/\/techclot.com\/index.php\/2020\/09\/03\/ais-data-hunger-will-drive-intelligence-collection\/","title":{"rendered":"AI’s Data Hunger Will Drive Intelligence Collection"},"content":{"rendered":"

AI’s Data Hunger Will Drive Intelligence Collection<\/a><\/p>\n

\"Air<\/p>\n

A sensor analyst at work at Joint Space Operations Center, Vandenberg Air Force Base, California<\/p>\n<\/div>\n

WASHINGTON: Artificial intelligence<\/a> has an insatiable appetite for data \u2013 but if you feed it the wrong kind of data<\/a>, it\u2019s going to choke. To get clean-enough data in large enough quantities for machine-learning algorithms to actually learn something from it, officials say that the intelligence community<\/a> needs to change how drones, satellites, and other sensors performs their mission every day.<\/p>\n

The turning point will be \u201cwhen we start seeing collection requirements \u2026 for the production of training-quality datasets, versus the support of a tactical operation,\u201d said David Spirk, who became the Defense Department\u2019s Chief Data Officer in June<\/a> and is now finalizing the DoD\u2019s new data strategy. \u201cI don\u2019t know that we\u2019ve entirely made that turn yet, but I think we\u2019re talking about it.\u201d<\/p>\n

For example, the US has collected vast amounts of data on the Central Command theater, said Spirk, who served<\/a> in Afghanistan himself as a Marine Corps intel specialist. But, he told the AFCEA AI+ML conference<\/a> yesterday, that data collection was driven by urgent tactical needs, without a systematic approach to archiving it, curating it, and making it accessible for machine learning.<\/p>\n

\"Air<\/p>\n

Predator drone over Afghanistan<\/p>\n<\/div>\n

That\u2019s understandable. Artificial intelligence in the modern sense was in its infancy on Sept. 11th<\/sup>, 2001, and the Pentagon did not systematically embrace AI until 2014<\/a>, long after the peak of fighting in Afghanistan and Iraq. But it means that the military has vast archives of \u201clegacy data\u201d \u2013 from drone video to maintenance records \u2013 that are in poorly catalogued, inconsistently formatted or otherwise too messy for a machine-learning algorithm to use without a massive and costly clean-up.<\/p>\n

\u201cIs that juice worth the squeeze?\u201d asked Capt. Michael Kanaan<\/a>, an Air Force intelligence officer who heads the USAF-MIT Artificial Intelligence Accelerator. In many cases, you could spend a lot of time and money cleaning up out of date, low-quality data that no longer reflects how your agency does analysis today. You get a much better return on investment, he told the conference, by \u201cdigitizing your [current] workflows\u201d and ensuring they produce \u201ctraining-quality data\u201d going forward.<\/p>\n

It\u2019s crucial to set up \u201ca strong data management culture\u201d to govern your data collection from the beginning, said Terrence Busch, the Defense Intelligence Agency<\/a>\u2019s technical director for the Machine-assisted Analytic Rapid-repository System<\/a> (MARS).<\/p>\n

It took DIA years of effort and \u201ca lot of back-end money\u201d to set up the processes, training, and technology required for data management, Busch told the conference. \u201cIt\u2019s not exciting work, [and] a lot of folks didn\u2019t want to invest in it,\u201d he said \u2013 but now that system is in place, the new data that DIA collects is much more accessible for AI.<\/p>\n

At the same time, another DIA official warned the conference, you don\u2019t want to clean up your data too much<\/em>, because you might erase a seemingly irrelevant detail that turns out to be useful later on.<\/p>\n

\u201cWe never want to throw it away, [because] we don\u2019t know if it\u2019s going to have value later,\u201d said Brian Drake, DIA\u2019s director of artificial intelligence. \u201cThe concept we are socializing inside of our agency is something we\u2019ve done since World War Two, which is creating a \u2018gold copy\u2019 of that data\u201d: a copy of the data as originally collected, with all its flaws, that\u2019s archived and kept unchanged in perpetuity for the benefit of future analysts.<\/p>\n

\u201cWe have to have an honest conversation with our vendors on that point,\u201d Drake told the conference, \u201c[because] we do find some data sets that come to us that have been pre-prepped and labeled,\u201d especially when it comes to imagery. While that cleaned-up data is often great for the immediate task at hand in the contract, he said, DIA needs the raw material as well.<\/p>\n

Getting everyone from contracting officers to analysts thinking about AI-quality data is a long-term effort, Busch said. \u201cDown in the workforce level<\/a>, culture adaptation is slow,\u201d he said.  \u201cWe\u2019ve spent at least 10 years getting people acculturated to big data, getting used to automation.\u201d<\/p>\n

That cultural revolution now needs to spread beyond the intelligence community. \u201cEvery single soldier, airman, sailor, Coast Guardsmen<\/a> is really a data officer in the future,\u201d said Greg Garcia, the Army\u2019s Chief Data Officer<\/a>. \u201cEvery single individual, no matter what their specialty is, has to think about data.\u201d<\/p>\n<\/p>\n

Published at Thu, 03 Sep 2020 17:48:45 +0000<\/p>\n

AI’s Data Hunger Will Drive Intelligence Collection<\/a><\/p>\n

\"Air<\/p>\n

A sensor analyst at work at Joint Space Operations Center, Vandenberg Air Force Base, California<\/p>\n<\/div>\n

WASHINGTON: Artificial intelligence<\/a> has an insatiable appetite for data \u2013 but if you feed it the wrong kind of data<\/a>, it\u2019s going to choke. To get clean-enough data in large enough quantities for machine-learning algorithms to actually learn something from it, officials say that the intelligence community<\/a> needs to change how drones, satellites, and other sensors performs their mission every day.<\/p>\n

The turning point will be \u201cwhen we start seeing collection requirements \u2026 for the production of training-quality datasets, versus the support of a tactical operation,\u201d said David Spirk, who became the Defense Department\u2019s Chief Data Officer in June<\/a> and is now finalizing the DoD\u2019s new data strategy. \u201cI don\u2019t know that we\u2019ve entirely made that turn yet, but I think we\u2019re talking about it.\u201d<\/p>\n

For example, the US has collected vast amounts of data on the Central Command theater, said Spirk, who served<\/a> in Afghanistan himself as a Marine Corps intel specialist. But, he told the AFCEA AI+ML conference<\/a> yesterday, that data collection was driven by urgent tactical needs, without a systematic approach to archiving it, curating it, and making it accessible for machine learning.<\/p>\n

\"Air<\/p>\n

Predator drone over Afghanistan<\/p>\n<\/div>\n

That\u2019s understandable. Artificial intelligence in the modern sense was in its infancy on Sept. 11th<\/sup>, 2001, and the Pentagon did not systematically embrace AI until 2014<\/a>, long after the peak of fighting in Afghanistan and Iraq. But it means that the military has vast archives of \u201clegacy data\u201d \u2013 from drone video to maintenance records \u2013 that are in poorly catalogued, inconsistently formatted or otherwise too messy for a machine-learning algorithm to use without a massive and costly clean-up.<\/p>\n

\u201cIs that juice worth the squeeze?\u201d asked Capt. Michael Kanaan<\/a>, an Air Force intelligence officer who heads the USAF-MIT Artificial Intelligence Accelerator. In many cases, you could spend a lot of time and money cleaning up out of date, low-quality data that no longer reflects how your agency does analysis today. You get a much better return on investment, he told the conference, by \u201cdigitizing your [current] workflows\u201d and ensuring they produce \u201ctraining-quality data\u201d going forward.<\/p>\n

It\u2019s crucial to set up \u201ca strong data management culture\u201d to govern your data collection from the beginning, said Terrence Busch, the Defense Intelligence Agency<\/a>\u2019s technical director for the Machine-assisted Analytic Rapid-repository System<\/a> (MARS).<\/p>\n

It took DIA years of effort and \u201ca lot of back-end money\u201d to set up the processes, training, and technology required for data management, Busch told the conference. \u201cIt\u2019s not exciting work, [and] a lot of folks didn\u2019t want to invest in it,\u201d he said \u2013 but now that system is in place, the new data that DIA collects is much more accessible for AI.<\/p>\n

At the same time, another DIA official warned the conference, you don\u2019t want to clean up your data too much<\/em>, because you might erase a seemingly irrelevant detail that turns out to be useful later on.<\/p>\n

\u201cWe never want to throw it away, [because] we don\u2019t know if it\u2019s going to have value later,\u201d said Brian Drake, DIA\u2019s director of artificial intelligence. \u201cThe concept we are socializing inside of our agency is something we\u2019ve done since World War Two, which is creating a \u2018gold copy\u2019 of that data\u201d: a copy of the data as originally collected, with all its flaws, that\u2019s archived and kept unchanged in perpetuity for the benefit of future analysts.<\/p>\n

\u201cWe have to have an honest conversation with our vendors on that point,\u201d Drake told the conference, \u201c[because] we do find some data sets that come to us that have been pre-prepped and labeled,\u201d especially when it comes to imagery. While that cleaned-up data is often great for the immediate task at hand in the contract, he said, DIA needs the raw material as well.<\/p>\n

Getting everyone from contracting officers to analysts thinking about AI-quality data is a long-term effort, Busch said. \u201cDown in the workforce level<\/a>, culture adaptation is slow,\u201d he said.  \u201cWe\u2019ve spent at least 10 years getting people acculturated to big data, getting used to automation.\u201d<\/p>\n

That cultural revolution now needs to spread beyond the intelligence community. \u201cEvery single soldier, airman, sailor, Coast Guardsmen<\/a> is really a data officer in the future,\u201d said Greg Garcia, the Army\u2019s Chief Data Officer<\/a>. \u201cEvery single individual, no matter what their specialty is, has to think about data.\u201d<\/p>\n<\/p>\n

Published at Thu, 03 Sep 2020 17:48:45 +0000<\/p>\n","protected":false},"excerpt":{"rendered":"

AI’s Data Hunger Will Drive Intelligence Collection A sensor analyst at work at Joint Space…<\/p>\n","protected":false},"author":3,"featured_media":2556,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[3],"tags":[],"class_list":["post-2558","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/techclot.com\/wp-content\/uploads\/2020\/09\/xhhmOX.jpg?fit=1024%2C677&ssl=1","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p3orZX-Fg","jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts\/2558","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/comments?post=2558"}],"version-history":[{"count":0,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts\/2558\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/media\/2556"}],"wp:attachment":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/media?parent=2558"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/categories?post=2558"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/tags?post=2558"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}