Agriculture - Machine Learning - Udacity

Say Machine Learning to someone, and if they recognize the term, they’ll probably think, “tech company.” But while the origin stories of transformative technologies like machine learning, deep learning, and artificial intelligence often seem to take root in Silicon Valley, the truth is these are industry-agnostic innovations. Their impact is being felt across countless fields you might never have thought of as being ripe for technological advancement. Here are 3 industries already implementing machine learning techniques:

Agriculture

Yes, agriculture. Farming, to be precise. Surprising? Think about it like this: If you were a farmer, and someone came to you and said, there’s a technology out there that can accurately predict your crop yields, would you be interested? Of course you would! Well, this is exactly what Descartes Labs does. They use machine learning to analyze massive satellite imagery data sets in the service of producing vastly more accurate crop yield predictions, with the ultimate goal of helping to address global food shortages.

“There are lots of companies focusing on continuing to increase yield. What we do is make that yield more efficient once the corn gets into the market. We hope that lowers the price of food, maximizes the profit of farmers, allows grain traders to operate more efficiently, and ultimately means we can deploy our precious food resources more efficiently to the people who need them.” —Mark Johnson, Co-founder and CEO, Descartes Labs, from an article published by the Center for Data Innovation

Conservation

The Nature Conservancy’s mission begins with these words: “The mission of The Nature Conservancy is to conserve the lands and waters on which all life depends.” Doesn’t sound much like the mission of a tech company, does it? Talk to Matt Merrifield however, the company’s CTO, and you’ll learn a great deal about how The Nature Conservancy is using machine learning to do everything from mapping roosting bird populations to monitoring the health of tuna fisheries.

“We needed to do a lot of mapping to understand where the biodiversity was. So that’s still a big part of the job. But now we’re starting to see ourselves expand into other technologies like machine learning and artificial intelligence, where there’s a lot of value in making sense of all the data that we get about natural systems in the world.”—Matt Merrifield, CTO, The Nature Conservancy, from an interview with Udacity

Construction

Safety and efficiency are critical for any company that wishes to succeed in the construction industry. But if in your mind’s eye that statement conjures up images of orange safety cones and yellow hard hats, think again. Instead, think drones and data. Two companies in particular, Skycatch and Skyward, combine to offer complementary services that allow construction companies to gather and analyze data collected by drones—this process in turn powers improvements in safety and efficiency on job sites. As but one example, commercial construction firm The Beck Group has utilized these technologies to reduce their rework costs by 25%.

“Thanks to deep learning, we can automatically identify job-site changes, track heavy equipment and log productivity for each worker and machine on site.” —Christian Sanz, Founder and CEO, Skycatch, from an article in Entrepreneur

So many different industries are being impacted by the rise of technologies like machine learning, deep learning, and artificial intelligence. Some we know about—entertainment, for example: Siri, Netflix, Pandora, and the like. Retail as well; we all know that Amazon is using technology to suggest new products for us. But similar advancements are being made in less-obvious industries like the ones we’ve discussed above, not to mention healthcare, finance, security, education, transportation, and manufacturing.

Look around you. If you notice something getting a lot more efficient and accurate all of a sudden, there’s probably some machine learning going on!

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