CrowdFlower, the essential data enrichment platform for data science teams, today announced its new AI product at the Rich Data Summit. This new module in the CrowdFlower platform combines machine learning and human-labeled training data sets to create predictive models that can be applied against new data. With this capability data scientists can now reduce the cost and increase the speed with which they enrich their data, without sacrificing the quality they demand.
“In the past year technology industry giants such as IBM, Google, Amazon and Microsoft have all launched their machine learning platforms. It’s an exciting time to be part of a data science team as this new capability becomes economically feasible for tens of thousands of companies for the first time,” said Lukas Biewald, chief executive officer and founder at CrowdFlower. “But machine learning platforms by themselves are incomplete — to be commercially viable they need both more training data and better training data.”
With AI, CrowdFlower customers will be able to apply a predictive model against new data sets. For rows of data that fall below a customer-defined confidence level, units can be routed to human contributors to complete the enrichment task such as sentiment analysis or data categorization. By combining machine and human intelligence in a single platform, CrowdFlower can intelligently assign data enrichment tasks to either humans or a machine based on the customer requirements for scope, quality and cost.
“The industry has been having the wrong debate about human-versus-machine intelligence,” said Biewald. “Human intelligence and machine intelligence aren’t in competition; they’re natural complements that reinforce each other. Humans’ great strength is the cognitive ability of the brain, which brings into play context, meaning and judgment, and machines’ great strengths are consistency and speed. By combining the best of human and machine intelligence into a single platform like CrowdFlower, the result is more data and higher quality data delivered faster and at lower cost. Data scientists who feed their machine learning platforms with high quality large scale human-labeled data sets can make the transition from interesting science experiment to a commercially viable business process generating millions of dollars of value for their company.”