They wouldn’t call it Big Data if it weren’t … huge. And machines learning from one another is an evolving frontier in which projects and tasks and any number of activities are made more efficient and evolve continuously.
But at the root of all this is human endeavor. With human endeavor comes fallibility. As a result, machine learning can be only as robust as its data inputs and models. The algorithms that help determine such learning also play a role in commerce, as consumers are tracked through the research, decision and buying processes. Data can be proactive, offering up ideas on what a consumer could buy, might buy and perhaps should buy.
Join Cathy O’ Neill, PhD, data scientist and author of Weapons of Math Destruction, and Nity Sharma, CEO and cofounder at Simpl, to suss out what separates good data from bad and how it can be applied effectively, or not. Join the conversation at Innovation Project 2017.
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