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AI, machine learning blossom in agriculture and pest control

Artificial intelligenceArtificial Intelligence knows many different definitions, but in general it can be defined as a machine completing complex tasks intelligently, meaning that it mirrors human intelligence and evolves with time. ()

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Artificial intelligence () is rising in prominence with the proliferation of chatbotsChatbots are computer programs which were engineered to converse in spoken or written form with humans. They are usually used in dialogue systems with a limited topic range. For example, they can answer basic customer questions or help you buy the correct train ticket., virtual assistants and other conversational tools that companies are using to improve customer service, productivity, and operational efficiency. But 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 products to churn through terabytes of precision agricultural data to create the best corn crops, while pest control companies are using -based image-recognition technology to identify and treat various types of bugs and vermin. Such markedly different scenarios underscore how has evolved from science fiction to practical solutions that can potentially help companies get a leg up on their competition.

SwissCognitive Logo is any technology that emulates human performance by , 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 (), a sub-field of , as algorithmsAn algorithm is a fixed set of instructions for a computer. It can be very simple like "as long as the incoming number is smaller than 10, print "Hello World!". It can also be very complicated such as the algorithms behind self-driving cars. leveraging technologies that operate based on existing information and are used in both unsupervisedIn unsupervised learning, the experience the algorithm needs to learn is still represent through a lot of data. However, there are no labels included on which piece of data belongs to with category. Meaning, the algorithm has to learn for itself how many categories are needed, and what belongs into which category. and supervised .

Corporate call centers use and tools to help agents communicate more efficiently, and sometimes more importantly, with customers. Some companies are using and 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 and tools are growing, as Forrester Research projects investments will rise 300 percent in 2017 from 2016. IDC believes will grow to become $47 billion markets by 2020.

boosts genetic yield in corn crops

Beck’s Hybrids, which competes with the much larger Monsanto, DuPont, Land O’ Lakes, Syngenta and other precision agricultural providers, is using a […]

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  2. SIBMedu

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