Crowdsourcing in the age of artificial intelligence: How the crowd will train machines

It was over 10 years ago that I was introduced to the concept of crowdsourcing. I was a student at London Business School when a professor one day came into the classroom with a jar of pennies.

He asked us each to take a look at the jar and guess the correct amount of money inside. The jar went around the classroom and I gave it an estimate of £30 in good faith. The professor duly wrote down each of our 100 guesses on the whiteboard and then opened a sealed envelope where the real amount was revealed: £18.76.

While my initial lesson learned was that I shouldn’t ever try a career in penny guessing, the amazing surprise was still in store for me: The professor calculated the average of all our 100 guesses and it magically came down to £18.76. The wisdom of the crowd was spot on and was better than 99 percent of our own estimates (only one of us actually guessed the right amount).

Just a few months later I founded a crowdsourcing company with my fellow student Janeen. We attracted the best security and defence innovators to solve complex challenges for industry and government clients. That was 2005, and the initial wave of crowdsourcing was born with InnoCentive, Wikipedia, and Amazon Mechanical Turk leading the pack in accessing and working with the knowledge and ideas of the crowd.

Existing companies also started various crowdsourcing initiatives to access the minds of customers and suppliers to co-create products like My Starbucks Ideas.

Over the last few years, crowdsourcing has evolved into a more pragmatic approach for corporates, who access the crowd not for co-creation of products or their ingenuity but rather as trainer for their AI systems.

Eric Schmidt, the Chairman and long serving CEO of Google, said back in 2016 that the next Google will be a crowdsourcing AI company.