Moore Meets Malthus - Machine Intelligence in Food Production
A recent confluence of factors has created unique opportunities to apply machine intelligence to agricultural production to increase farming efficiency and profitability. Technology is key to meeting the growing production demands caused by increasing global population and changing consumption patterns. To effectively address the challenge, we must utilize increasingly localized precision agriculture, analyze enormous data volumes, and bring machine learning powered solutions to the market. This talk considers the opportunities and challenges in building and delivering analytics-driven tools to augment human decision making as a means to address the world’s largest optimization problem: optimizing global food production.
Erik leads the data science and research organization, which applies large-scale statistical machine learning and data science to solve challenging problems in numerous domains including climatology, agronomic modeling and geospatial applications. Erik's contributions to The Climate Corporation include defining the data science vision and leading the research underpinning pioneering products including Climate Basic and Climate Pro. Previously, Erik worked at several Bay-area start ups. He has a B.S. in Computer Science from Arizona State University and a PhD in Mathematics from University of Wisconsin-Madison.