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Can AI Detect Disinformation? A New Special Operations Program May Find Out:
For all the U.S. military’s technical advantages over adversaries, it still struggles to counter disinformation. A new software tool to be developed for the U.S. Air Force and Special Operations Command, or SOCOM, may help change that.
“If you don’t compete in the information space, regardless of how good your operations are, your activities are, you will probably eat a shit sandwich of disinformation or false reporting later on,” Raymond “Tony” Thomas, a former SOCOM chief, said in an interview. “We certainly experienced that at the tactical level. That was the epiphany where we would have good raids, good strikes, etc. and the bad guys would spin it so fast that we would be eating collateral damage claims, etc. So the information space in that very tactical space is key."
Primer [is] a company that on Thursday announced a Small Business Innovation Research contract to develop software over the next year to help analysts better—and much more quickly—survey the information landscape and hopefully detect false narratives that show up in the public space.
Primer’s neural network technology can scan large amounts of text and extract themes and other information based on the frequency and prominence of words and phrases. It’s the sort of thing that can be very useful if you have a lot of text you want to very quickly summarize in an accurate headline, a capability they demonstrate here. To train their headline-writing neural net, they used a corpus “of millions of publicly available document-title pairs: news articles and headlines” according to their paper on the subject.
The new contract will help Primer to build a platform “to automatically identify and assess suspected disinformation,” according to a press release from the company.
[We were shown] an example of where the technology is today, in the context of the emerging conflict between Armenia and Azerbaijan. The network can find news, sources and social media posts about the conflict and segment that information into groups, based on who is saying what about a particular event or incident, such as a military strike. This immediately gives the user a sense of what different groups and different governments are claiming. You can also see how those reporting entities have changed the way they’ve discussed the situation in question overtime. Essentially, at present, the network gives you much of the same information that you might get from a newspaper story covering an incident or event.
The hope over the next 12 months is to add data that comes from operators responding and interacting with the product and the information it presents. Those users in SOCOM and the Air Force will be able to determine—and provide information on—which of the sources is the most credible, based on what they’ve seen. Their input will allow the network over time to develop a sense of which claims are more likely to be factual based on the source and what other sources are saying that’s different. “The next level of this system is one that’s… more predictive, allows you to see and make inferences that you can test along the way.”
Eventually, the platform will be able to award a particular claim or news item a sort of accuracy score based on those factors,
https://www.defenseone.com/technolo...ecial-operations-program-may-find-out/168972/
maximus otter
For all the U.S. military’s technical advantages over adversaries, it still struggles to counter disinformation. A new software tool to be developed for the U.S. Air Force and Special Operations Command, or SOCOM, may help change that.
“If you don’t compete in the information space, regardless of how good your operations are, your activities are, you will probably eat a shit sandwich of disinformation or false reporting later on,” Raymond “Tony” Thomas, a former SOCOM chief, said in an interview. “We certainly experienced that at the tactical level. That was the epiphany where we would have good raids, good strikes, etc. and the bad guys would spin it so fast that we would be eating collateral damage claims, etc. So the information space in that very tactical space is key."
Primer [is] a company that on Thursday announced a Small Business Innovation Research contract to develop software over the next year to help analysts better—and much more quickly—survey the information landscape and hopefully detect false narratives that show up in the public space.
Primer’s neural network technology can scan large amounts of text and extract themes and other information based on the frequency and prominence of words and phrases. It’s the sort of thing that can be very useful if you have a lot of text you want to very quickly summarize in an accurate headline, a capability they demonstrate here. To train their headline-writing neural net, they used a corpus “of millions of publicly available document-title pairs: news articles and headlines” according to their paper on the subject.
The new contract will help Primer to build a platform “to automatically identify and assess suspected disinformation,” according to a press release from the company.
[We were shown] an example of where the technology is today, in the context of the emerging conflict between Armenia and Azerbaijan. The network can find news, sources and social media posts about the conflict and segment that information into groups, based on who is saying what about a particular event or incident, such as a military strike. This immediately gives the user a sense of what different groups and different governments are claiming. You can also see how those reporting entities have changed the way they’ve discussed the situation in question overtime. Essentially, at present, the network gives you much of the same information that you might get from a newspaper story covering an incident or event.
The hope over the next 12 months is to add data that comes from operators responding and interacting with the product and the information it presents. Those users in SOCOM and the Air Force will be able to determine—and provide information on—which of the sources is the most credible, based on what they’ve seen. Their input will allow the network over time to develop a sense of which claims are more likely to be factual based on the source and what other sources are saying that’s different. “The next level of this system is one that’s… more predictive, allows you to see and make inferences that you can test along the way.”
Eventually, the platform will be able to award a particular claim or news item a sort of accuracy score based on those factors,
https://www.defenseone.com/technolo...ecial-operations-program-may-find-out/168972/
maximus otter