Crowdsourcing in the age of artificial intelligence: How the crowd will train machines | Industry – InfotechBot | Prosyscom USA

By Prosyscom
In September 1, 2018
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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.

Second-generation crowdsourcing

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. He said that if he wanted to start a new company, he would crowdsource a lot of labeled data from a crowd of specialists (he used the example of dermatologists) in order to train an AI system that would be able to learn and eventually be better at a task than these individuals and then sell the product back to them.

And there are large enterprises who use crowdsourcing for services typically performed by contractors or employees. Swisscom for instance acquired crowdsourcing platform Mila to outsource its maintenance and repair work to the crowd. The company ultimately seems to want to gather data from these crowd workers via mobile and AR so that it can eventually train an AI system to perform most of that human work.

So is the crowd now just cannon fodder for AI?

The point of crowdsourcing was always to outsource “micro tasks” that didn’t take an individual much effort (like guessing pennies in a jar) but delivered real value when executed by a crowd.

In the age of AI we see a parallel, where one labeled data set is useless but thousands together create value.

But I predict that it will go a step further. Today we are accessing the knowledge of the crowd to label data — for instance to label a picture to be a sunset or sunrise. But the next step will be for the crowd to provide data sets, too. For instance, I could provide health information about myself on a daily basis in a format that an AI system requires (formatted data) in order for firms to develop new drugs. One amazing non-profit venture I once advised, CancerBase, is trying something like that for curing cancer. In this next generation of crowdsourcing, the crowd will either get paid for its data or, as in the case of CancerBase, will provide data for free to help advance a good cause for humanity.

So how will this affect my university’s penny guessing problem in the future? Instead of asking students to guess the number of pennies, the professor will ask them each to take a picture of a random set of pennies from their wallets. Students will then label that picture with the amount their photo contains and send it to an AI app, which will determine the right number in any jar going forward.

Welcome to a brave new crowdsourcing world where we are the data providers.

Simon Schneider is an entrepreneur in the crowdsourcing economy and director at ECSI.

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