Official US defence and NATO documents confirm that autonomous weapon systems will kill targets, including civilians, based on tweets, blogs and Instagram
Four former service members – including three sensor operators – issue plea to rethink current airstrike strategy that has ‘fueled feelings of hatred’ toward US
Guardian, 18 November 2015
Phi Beta Iota: Closing down or transferring the drone program should have been John Brennan’s first order of business when he assumed his position as Director of the Central Intelligence Agency. That he did not do so is his epitaph.
In the last week, much has been made of the leaked DoD briefing entitled ISR Support to Small Footprint (CT) Operations – Somalia and Yemen, dated February 2013. To date, all the reports I have read, save one, focus on the “critical shortfalls” of drone warfare revealed in these slides — see, for example The Intercept, which broke the story on October 15 and placed the slides on the net, and this report in Common Dreams, and anti-war progressive outlet. Both of these reports and the briefing slides contain a lot of useful information are well worth careful reading. But there is more.
I joined the Qatar Computing Research Institute (QCRI) well over 3 years ago with a very specific mission and mandate: to develop and deploy next generation humanitarian technologies. So I built the Institute’s Social Innovation Program from the ground up and recruited the majority of the full-time experts (scientists, engineers, research assistants, interns & project manager) who have become integral to the Program’s success.
Recent scientific research has shown that aerial imagery captured during a single 20-minute UAV flight can take more than half-a-day to analyze. We flew several dozen flights during the World Bank’s humanitarian UAV mission in response to Cyclone Pam earlier this year. The imagery we captured would’ve taken a single expert analyst a minimum 20 full-time workdays to make sense of. In other words, aerial imagery is already a Big Data problem. So my team and I are using human computing (crowdsourcing), machine computing (artificial intelligence) and computer vision to make sense of this new Big Data source.