Description:

Hosted By: Ben Lorica

Crowdsourcing marketplaces like Elance-oDesk or CrowdFlower give us access to people all over the world that can solve various tasks, such as virtual personal assistants, image labelers, or people that can clean up gnarly datasets. Humans can solve tasks that artificial intelligence is not yet able to solve, or needs help in solving, without having to resort to complex machine learning or statistics. But humans are quirky: give them bad instructions, allow them to get bored, or make them do too repetitive a task, and they will start making mistakes. In this webcast, I'll explain how to effectively benefit from crowd workers to solve your most challenging tasks, using examples from the wild and from our work at GoDaddy.

Machine learning and crowdsourcing are at the core of most of the problems we solve on the Locu team at GoDaddy. When possible, we automate tasks with the help of trained regressions and classifiers. However, it's not always possible to build machine-only decision-making tools, and we often need to marry machines and crowds. During the webcast, I will highlight how we build human-machine hybrids and benefit from active learning workflows. I'll also discuss learnings from 17 conversations with companies that make heavy use of crowd work that Aditya Parameswaran and I have collected for our upcoming book.

About Adam Marcus

Adam is the Director of Data on the Locu team at GoDaddy, where he focuses on crowdsourcing and building open source data infrastructure. He completed his Ph.D. in Computer Science at MIT in 2012. His dissertation was on database systems and human computation. He is a recipient of the NSF and NDSEG fellowships, and has previously worked at ITA, Google, IBM, and FactSet. In his free time, he builds course content to get people excited about data and programming.

Twitter: @marcua

About Ben Lorica

Ben Lorica is the Chief Data Scientist and Director of Content Strategy for Data at O'Reilly Media, Inc.. He has applied Business Intelligence, Data Mining, Machine Learning and Statistical Analysis in a variety of settings including Direct Marketing, Consumer and Market Research, Targeted Advertising, Text Mining, and Financial Engineering. His background includes stints with an investment management company, internet startups, and financial services.

Twitter: @bigdata

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