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How Machine Learning is Disrupting These 5 Industries

Selena Ricks-Good
Posted by: Selena Ricks-Good on Sep 6, 2016 1:05:00 PM
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Machine learning has the potential to disrupt every aspect of business as we know it, yet the application of machine learning in the enterprise world is nascent. Here are some of the industries using this disruptive technology to stay ahead of trends.

As users increasingly reveal information online, companies are catching on to using big data technology to predict business outcomes. Machine learning and artificial intelligence (AI) are being used to make data-driven decisions for activities such as buying behavior, process optimization, equipment maintenance — and even for brewing craft beer.

Advancements in systems that can digest large quantities of voice, image, and structured text data are increasingly making it possible to complete functions across many different sectors in conversational, efficient ways.

Additionally, most of the current research in AI is freely available and enormous quantities of data are being released, and it’s no surprise that machine learning technology is being implemented commercially at an unprecedented rate. A recent survey by SoftServe found that 62 percent of medium to large organizations in the US and UK plan to implement machine learning for business strategies by 2018.

Interested in learning how your organization can benefit from this groundbreaking technology? Read on and discover the breakthroughs happening in your industry.

The North Face’s XPS.

Retail

Machine learning is already playing a major role in your consumer life. Buy supplies on Amazon or watch movies on Netflix, and a machine learning system recommends what else you might like. Data sources such as social media, purchase history, consumer demand, and market trends are making it easy for companies to cozy up to our buying habits like never before.

We are starting to see consumer-facing AI, such as The North Face’s Expert Personal Shopper (XPS) platform, which leverages IBM’s Watson natural language processing. Instead of navigating pages and pages of traditional online shopping, simply type descriptors such as “casual hiking jacket for San Francisco in October” and XPS quickly recommends custom search results.

Behind the scenes, machine learning and deep analytics are also changing the operational approaches of retailers and manufacturers. From supply chain optimization to producing customized, built-to-order products in a timely fashion, machine learning is ultimately helping companies perform better for their customers.

Media/Publishing

The media and publishing industry is finding a number of ways to use machine learning. In the wake of the rise of both search and social media, an entire industry has grown around analysis of content and engagement. Bounds of data is now available because of content, from sentiment to brand reputation. Media and publishing companies are using machine learning to determine their audience’s mood and intent to determine which books, articles, videos and other media will gain the most traction. Advertisements are optimized in real time.

Some publishers, such as the Associated Press, are even using machine learning to write the news. On a broader scale, Microsoft just announced that it is bringing machine learning to its Office 365 suite. For students, the new Office tool “Researcher” will make writing term papers almost as easy as cooking ramen. This cloud-based service will suggest relevant quotes and materials from reliable sources, and even add citations and references automatically. “Editor,” another new Office tool, is designed to help a variety of writers sharpen their syntax by utilizing natural language processing capabilities.



Finance

The financial industry has been one of the first adopters of machine learning. The technology has been used to optimize portfolios. Microlender Branch uses machine learning to scan data of users in developing countries (with their permission), approving or denying loans depending on what it finds. Hedge fund managers and asset managers have been studying Twitter to gain an edge over their competition. Twitter data has even been accurate at predicting unemployment levels in the US.

Digital finance assistants are launching all over. Most recently, Cloud accounting software maker Sage announced its Pegg bot, an AI system that can be used with Slack and Facebook Messenger. By using natural language and viewing data such as receipts and invoices, Pegg can give users control over pain-producing tasks such as filing expense reports and tracking freelance income. As Pegg moves out of Beta mode it will be interesting to see how it deals with security issues.

Business Operations/Human Resources

It may sound risky to trust a bot to handle the nuts and bolts of a company’s core functions, but with smarter business process management, companies can save extraordinary amounts of time, resources, labor, and risk. Google is utilizing its AI company DeepMind to reduce the energy costs of cooling its data centers by 40 percent.

Machine learning can also be used to streamline HR practices, such as measuring employee satisfaction, predicting turnover, and analyzing large quantities of resumes. Banks, credit card companies, payment companies such as PayPal, and businesses of all sizes are using machine learning for security solutions to reduce risk, prevent fraud and detect cyber threats.


Transportation

From Uber to Waze to the promise of self-driving cars, machine learning is making major inroads in the transportation sector (pun intended). Enterprise use cases such as fleet management companies and insurance providers are collecting data on drivers and vehicles to track events like seat belt use, sharp corner turning, and speeding in order to recommend safer practices.

At Mission Data, we are using machine learning in a current Labs project to enable DC’s Capital Bikeshare users to gain more insight into the availability of bikes at a given station. We built an application that uses a combination of historical bikeshare and weather data with statistical methods to predict whether a station will be empty or full of bikes. For bikeshare system users these two states can be very stressful, and knowing the likelihood of this occurring allows riders to prepare in the event they may not be able to borrow or return a bike in a timely fashion.

Leveraging machine learning and AI requires business acumen

For those of us practicing and developing machine learning and AI, the key to leveraging the tremendous power of these tools is to make them accessible to organizations of all sizes. The real trailblazers in this field are finding the relevant business challenges that need smart data to help solve those issues. One thing is for certain — no matter how advanced these tools become, humans will always be needed to pave the path.

Originally published September 6, 2016 in our journal over at Medium 


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Topics: Trends