Английская Википедия:Artificial Intelligence for Digital Response

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Версия от 04:08, 3 февраля 2024; EducationBot (обсуждение | вклад) (Новая страница: «{{Английская Википедия/Панель перехода}} '''Artificial Intelligence for Digital Response''' ('''AIDR''') is a free and open source platform to filter and classify social media messages related to emergencies, disasters, and humanitarian crises.<ref>{{cite web|title=AIDR (Artificial Intelligence for Digital Response) — Social Tech Guide|url=http://www.socialtech.org.uk/projects/aidr-artificial-intelligenc...»)
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Artificial Intelligence for Digital Response (AIDR) is a free and open source platform to filter and classify social media messages related to emergencies, disasters, and humanitarian crises.[1][2][3] It has been developed by the Qatar Computing Research Institute and awarded the Grand Prize for the 2015 Open Source Software World Challenge.[4][5][6]

Muhammad Imran stated that he and his team "have developed novel computational techniques and technologies, which can help gain insightful and actionable information from online sources to enable rapid decision-making" - according to him the system "combines human intelligence with machine learning techniques, to solve many real-world challenges during mass emergencies and health issues".[2]

How to use

It can be used by logging in with ones Twitter credentials and by collecting tweets by specifying keywords or hashtags, like #ChileEarthquake, and possibly a geographical region as well.[7]

Use

  • It has been deployed in conjunction with UNICEF in Zambia to classify short messages related to AIDS/HIV received through the U-Report platform.[8][9]

Related talks and events

  • Muhammad Imran delivered a keynote talk on the science behind the AIDR system at the International Conference on Information Systems for Crisis Response And Management (ISCRAM).[11]
  • Abdelkader Lattab and Ji Lucas also presented the system at the 2016 QCRI-IBM Data Science Connect event.[12]

See also

References

Шаблон:Reflist

External links