Top 5 Machine Learning Use Cases for 2018
Top 5 Machine Learning Use Cases for 2018
It's safe to say that machine learning will have a widespread impact in nearly every industry in the next decade. The question becomes: How will *your* business use it?
Join the DZone community and get the full member experience.Join For Free
Insight for I&O leaders on deploying AIOps platforms to enhance performance monitoring today. Read the Guide.
Artificial intelligence (AI) is being deployed more and more frequently across industries. Far from the typical sci-fi fantasy depiction of robots with human-level intelligence, AI’s purpose in business today is frequently to automate tasks that humans have done in the past, such as picking products at a warehouse for order fulfillment, but also to find hidden patterns in data that lead to actionable insights.
Machine learning is a central subset of AI systems and has become an essential tool for data-driven businesses. At its core, machine learning is the process whereby a computer is given “the ability to learn without being explicitly programmed.” That may seem fairly vague, but there are many excellent use cases that highlight why this technology will have a long-lasting impact on business and society. Following are five examples of machine learning that we believe highlight the technology and its capabilities and will only continue to shine in 2018.
Being able to have a crystal ball for the stock market has always been something investment firms, hedge funds, and traders have dreamed of. In the past, individuals would comb through reams of data, looking for even the smallest hint into what the market was going to do (check out the film The Big Short for a great example of this). Today, that work is largely being done by computers. Using machine learning, AI can more accurately predict when a stock is going to go up or down, and then execute trades based on parameters set by the trading firm.
This is one of the biggest areas where machine learning is having an impact. With the explosion of smartphones, social media, and other digital forms of communication, consumers are now generating more data than ever before. In fact, we’re generating approximately 2.5 quintillion bytes of data every day, or approximately 50,000 gigabytes/second. That’s 500% growth from 15 years ago. Among all that data is a wealth of information on nearly every single person that goes far beyond traditional marketing demographics; it includes likes, dislikes, buying patterns, media consumption patterns, and many, many more data points. Marketers are using machine learning to identify consumers and personalize marketing messages, offers, and more. Not only is the content being personalized, but so are placements, allowing marketers to serve up the right message to the right consumer at exactly the right time.
Wouldn’t it be great if we could identify a life-threatening disease before it becomes serious? Thanks to machine learning, that is starting to become a reality. Although the technology is still new and not yet perfected, clinicians, doctors, and computer scientists are developing systems that can detect diseases sooner. A great example of this is an early breast cancer detection system developed at the University of Chicago. The team used machine learning to analyze mammograms of more than 22,000 women who were routinely examined over five years to identify potential breast cancer cases more accurately than a typical mammogram.
We’ve talked about how vehicle telematics is fueling the growth and maturation of the connected cars industry. Machine learning is playing a huge role in transitioning vehicles from simply being connected to being self-driving. Machine learning is being used to analyze the growing amount of data that connected cars generate with the goal of optimizing traffic patterns, identifying road hazards, and other factors that can impact passenger safety in self-driving vehicles.
Last year, there were approximately 200,000 malware samples collected and more than 4,000 ransomware attacks each day. And these numbers continue to grow. Data security firms are turning to machine learning to analyze attacks like these in an effort to more accurately and more swiftly identify attacks so they can be better defended.
Of course, these are just the tip of the iceberg. There are many, many more industries where machine learning is being deployed. It is safe to say that machine learning is going to have widespread impact in nearly every industry in the next decade. The question becomes: How will your business use it?
Published at DZone with permission of Dana Meschiany , DZone MVB. See the original article here.
Opinions expressed by DZone contributors are their own.