In a departure from using AI and machine learning tools for tasks such as automating customer service, some companies are applying the technologies to grow better corn crops and exterminate bugs and vermin.
Artificial intelligence (AI) is rising in prominence with the proliferation of chatbots, virtual assistants and other conversational tools that companies are using to improve customer service, productivity and operational efficiency. But AI is also helping to automate and streamline tasks in data-intensive industries traditionally ruled by rigorous science and good old-fashioned human analysis.
Seed retailers, for example, are using AI products to churn through terabytes of precision agricultural data to create the best corn crops, while pest control companies are using AI-based image-recognition technology to identify and treat various types of bugs and vermin. Such markedly different scenarios underscore how AI has evolved from science fiction to practical solutions that can potentially help companies get a leg up on their competition.
AI is any technology that emulates human performance by learning, reaching conclusions, understanding complex content, engaging in natural dialogs with people or replacing people for non-routine tasks, according to Gartner. The researcher defines machine learning (ML), a sub-field of AI, as algorithms leveraging technologies that operate based on existing information and are used in both unsupervised and supervised learning.
Corporate call centers use AI and ML tools to help agents communicate more efficiently, and sometimes more importanlty, with customers. Some companies are using AI and ML to ferret out employees who are likely to leave their positions, based on their behavioral patterns, as well as details on their commute distance from work.
But enterprise use cases for AI and ML tools are growing, as Forrester Research projects AI investments will rise 300 percent in 2017 from 2016. IDC believes AI will grow to become $47 billion market by 2020.
Beck's Hybrids, which competes with the much larger Monsanto, DuPont, Land O' Lakes, Syngenta and other precision agricultural providers, is using an AI product to analyze large amounts of data to determine which corn breeds and which conditions will produce the highest yields. The company’s geneticists need to know how sun light, rain, location, terrain and could affect growth and profits for the more than 30,000 different types of seeds it offers.
In testing, the company's five corn breeders collect 3,000 to 5,000 data points from combines, weather stations, DNA marker labs and drones for each 20-foot strip of land, according to Brad Fruth, information systems manager of Beck’s Hybrids. While data collection is plentiful, deriving insights from that data is another story.
“How in the world is a small to mid-sized company [going] get down to the nitty-gritty to find out what is changing as far as variables and what is working without hiring 50 data scientists," Fruth says, summing up the challenge he faced a year ago. There’s no lack of data-crunching software such as Hadoop, but Beck's lacks the engineering resources to spin up a cluster and write custom algorithms to get at the heart of the data. And in an industry where growers get one shot a year to get the right crop yields, Fruth wasn't going to take his chances. “We just didn’t like what we saw on the market and it wasn’t feasible for us to be able to churn through all of this data and get real insights from it,” Fruth says.
A reseller clued Fruth into Nutonian, whose AI software Eureqa automatically builds and interprets analytics models from data and presents information in a way that a non-technical user can understand. No data scientists required. Fruth says the software worked quickly, displaying results in just five minutes that would normally take corn growers eight to nine weeks to cobble together in spreadsheets.
With Eureqa, which runs millions of equations per second, Fruth says the results are enabling geneticists to refine their questions, sharpening their approach to the data. “We want to make sure that we’re testing at the right field at the right time,” Fruth says. “That is a perfect world for us.”
While geneticists are using AI to breathe life into corn crops, Rentokil is using AI to kill bugs and vermin.
Some of the company's 5,000 pest control technicians are using an Android mobile app developed by Accenture to identify bugs. A technician stumped by a type of bug or rodent can take a picture of the pest and run the app, called PestID. The picture calls home to Google’s image classification and machine learning software to sift through a number of pest images and identify the intruder, according to Nisha Sharma, a managing director in Accenture's mobility group.
When a positive identification is made, the app immediately provides remediation solutions, which have been pre-populated, to help the technician decide treatment plans, including proper chemicals and recommendations for homeowners.
Sharma says technicians told Accenture that PestID was crucial because various pests require different chemicals for treatment. She adds that every time a technician snaps a picture it will enhance the recognition and classification capabilities of the ML algorithms.
Keith Chisholm, Rentokil’s head of North America IT and partnerships, says technicians trialing PestID in North America are providing “very positive feedback” of their experiences.
“Being able to work with Accenture to take advantage of the latest Google CloudML and Android technologies and design something totally new puts us in control of our own future,” Chisolm says.
Gartner has positioned Vodafone as a "Leader" in its Magic Quadrant for Managed M2M Services, Worldwide report 2017, for the fourth consecutive year