The introduction of crowdsourcing was a game changer for the business world — but that was so ten years ago. Okay, to be fair, 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). Back in the day, crowdsourcing quickly became a good solution to effectively outsource projects at a reasonable cost — 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.
Today, with data volumes exploding — most of which were created in the past two years — coupled with the growing dependency businesses have on machine learning and AI capabilities, traditional crowdsourcing simply won’t cut it when it comes to training machine learning/AI models. Enter Intelligent Crowdsourcing.
Join Spare5, the Intelligent Crowdsourcing Platform, to learn:
- How traditional crowdsourcing falls short
- How Intelligent Crowdsourcing works
- Why you need high-quality labeled data to train your machine learning models