Machine Learning in PracticeBefore talking about how machine learning is being used, it’s useful to first make a distinction between ML and Artificial Intelligence. Artificial Intelligence is an umbrella term. It refers to the idea of a computer (or any machine) behaving in ways a human would consider intelligent. Machine learning is one of those ways: an application of AI in which programs are encouraged to correct and learn from their own mistakes when exposed to data. The most profitable enterprise uses of ML involve classifying unstructured data and using it to produce business insights. Here are a few popular applications, listed with their average rate of adoption from the MITTR survey: Text mining and emotion analysis (both 47%): These techniques overlap so often it’s easiest to list them together. Text mining is the use of machine learning to take chunks of text from emails, social media posts, or other sources and analyze them to determine the subject. Emotion analysis (also called sentiment analysis) estimates the mood of a text’s writer. These two disciplines are combined very effectively to produce insight about how customers interact with a brand online as well as accurately directing emails to the proper departments without the need for human interference. Image recognition, classification, and tagging (47%): Being able to identify the subject of an unlabeled picture is one of ML’s core strengths. Imagine an algorithm that could sort and tag incoming pictures of damaged merchandise for insurance claims or detect whether pictures uploaded to a social media page violated community guidelines. Natural Language Processing (45%): NLP is the backbone of the advanced chatbots used for online customer service, but that’s not its only enterprise application. A scientist can sort terabytes of online papers using NLP to create a customized list of suggested sources for their research. Likewise, the time it takes lawyers to find relevant case precedents is drastically reduced. Recommendations (42%): E-commerce brought in $394.9 billion in the United States last year, growing 15.1% from 2015. At the same time the average attention span of an online shopper dropped 30%. Smart machine learning strategies by companies like Amazon and Netflix have conditioned consumers to expect to find their product they want without scrolling through multiple pages. Implementing a machine learning-enable recommendations engine is a good first step to holding a shopper’s attention through checkout. ML programs can also apply affinity analysis to suggest additional items that go along with products already placed in the cart.
Real World ResultsThe true measure of success is how machine learning performs in the real world. What kind of ROI are early adopters seeing? Can machine learning really deliver on its promises? The answer to both questions seems to be “yes”. While machine learning has a long way to go before realizing its full potential, it is advanced enough now to grant a significant competitive edge to its users. Take a look at some of the most common enterprise goals of machine learning programs:
- Better data analysis and insights (50%)
- Faster analysis and more timely insight (45%)
- Improved internal efficiency (39%)
- Better understanding of customers (35%)
- Better data analysis and insights (45%)
- Faster analysis and more timely insights (35%)
- Improved internal efficiency (30%)
- Better understanding of customers (27%)
Moving ForwardA recurring theme throughout the survey is the belief that machine learning will give companies a significant competitive edge over their peers. 26% of current ML users feel it already has for their organization. Machine learning may be a young field, but results like these show it can stand up to the demands of enterprise.
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