Always thought there would be value in intelligently directing a crowdsourcing effort. Our own experiments showed it was difficult to manage. The below appears to point to a better means to direct the results and feed machine learning methods. Plan to attend.
Georgina FitzGerald webinars@theiegroup.com via mktdns.com
The introduction of crowdsourcing was a game changer for the business world — but that was so ten years ago. It was innovative for its time. It enabled companies to outsource projects to a remote workforce (though that workforce was relatively unknown, which is kinda scary) — but crowdsourcing in its original form is flawed and inadequate.
For example, it was never designed to provide training data for machine learning models — a highly relevant use case today as artificial intelligence (AI) becomes an increasingly large focus across industries. It was never designed to handle specialized tasks in place of in-house employees to free up time for more complex work (example: engineers offloading tasks like annotating and attributing products so they can focus on writing code), though it makes perfect sense to now use crowdsourcing that way.
Register for the 'Intelligent Crowdsourcing: What the Next Generation of Crowdsourcing Looks Like' webinar and discover:
- How traditional crowdsourcing falls short
- How Intelligent Crowdsourcing works
- Why you need high-quality labeled data to train your machine learning models ... "
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