Businesses rely on analytics more every day, with one of the primary reasons being that they’re exceptionally helpful for developing artificially intelligent technology systems that can aid in their missions. One of the qualities of a good AI program is machine learning, which is the ability of a system to look at information to identify and learn from trends. In fact, this practical aspect of AI is one of the reasons why it’s becoming popular in the business setting.
What is Machine Learning?
The main idea of machine learning is to sift through a huge amount of data to find applications for it in the real world. An algorithm is what determines how the system will use the data collected. Either the program uses the data as it sees fit, or it can be told to use it in a certain way. Here are three ways machine learning is used, as reported by TechRepublic:
- Supervised learning: The "trainer" will present the computer with certain rules that connect an input (an object's feature, like "smooth," for example) with an output (the object itself, like a marble).
- Unsupervised learning: The computer is given inputs and is left alone to discover patterns.
- Reinforcement learning: A computer system receives input continuously (in the case of a driverless car receiving input about the road, for example) and is constantly improving.
In some cases, “deep learning” may be used in order to provide a different approach to big data. Deep learning uses algorithms layered in ways that allow systems to process data and reach predictions. If you think that sounds familiar, that’s because it is--the big difference between deep learning and machine learning is that deep learning doesn’t rely on the assistance of users.
How It’s Being Used
One of the best ways to understand how machine learning is being used is by looking to IBM’s Watson, the Jeopardy-winning robot. Another example is Google DeepMind, which can use machine learning to excel at the complex board game, Go. Furthermore, Amazon and Microsoft have machine learning platforms that can be used by organizations that want to build their own machine learning tools.
In terms of practical business use, machine learning can be applied in several ways. Some organizations have implemented automated help desk solutions which use “chatbots” to answer frequently asked questions. Others, like Dominos, have innovated to allow customers to place orders through Facebook Messenger and Twitter, using intelligent technology that can alter orders and suggest favorite menu items. Even Uber, the ride-hailing app that’s making waves in the industry, is using chatbots to help users request rides and receive updates.
Notice a pattern? Machine learning is being used by organizations that don’t have time to deal with individual questions, or by those who deal with tedious or repetitive tasks. Of course, not every client wants to talk to an automated chatbot, so the support extension still has a lifeline to hold onto for a while to come.
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