Until recently, only large corporations and firms with large client bases have used the insights from big data analysis as a strategic tool. However, it’s now more attainable for small- to medium-sized business. In addition, Cloud computing has revolutionized the way data is stored—critical files and data can be accessed from a centralized database through any Internet-enabled device.
“These emerging technology trends allow digital businesses to easily improve their products and services where they never find themselves without the data functionality or communication they need,” says Sanjay Parthasarathy, COE of Indix in an interview with R&D Magazine.
Machine learning
Machine learning is a field of computer science that’s centered on getting machines to act without being explicitly programmed for a task. It requires a deep level of expertise in the areas of data mining, data science and statistical pattern recognition in order to build and apply learning algorithms. Machine learning is used in a variety of fields—medicine (diagnostics), transportation (self-driving cars), ecommerce (recommendations)—and is one of the critical elements within artificial intelligence (AI).
“The excitement for machine learning comes from the combination of the necessity, the proven marketplace success and the sheer cool factor,” says Parthasarathy.
With digital data growing at an exponential rate, data science and machine learning are the only ways to harvest and make good use of data. “Connected mobile devices and the Internet of Things (IoT) are accelerating that growth,” says Parthasarathy. “And proven success with business and consumer application of machine learning such as Google’s self-driving cars, cancer diagnostics with IBM’s Watson and Netflix’s recommendations are just a few examples where machine learning is booth cool and practical.”
At a simple level, it takes data scientists, algorithms and really large data sets to train a machine to recognize human-known things. “In the world of machine learning, rudimentary algorithm using more data has been shown to outperform more sophisticated algorithms using smaller data sets,” says Parthasarathy.
The benefit of machine learning for business, science and humanity are only limited by creativity and the access to data on which algorithms must be trained.
The future of machine learning
The future for machine learning is one where machine learning and data science will be pervasive in business and how people live every day.
“One example is how machine learning technology will revolutionize the way we shop,” says Parthasarathy. “Some day, every person will have a personalized shopping experience.” For example, almost every person will have mobile devices with a super-concierge, or an Ambient Shopping Assistant, attending to their product needs and wants.
“It might beat a likeness to digital assistants used today, such as Siri or Cortana, but it will be able to offer much more customized and intuitive services,” says Parthasarathy. “Over time, based on what you choose to share with it, ASHA will quickly help you find the products you want and need. You will train it; it will learn from you; and it will generate increasingly relevant product offers.”
At it’s core will be machine learning and data science. So, in this case, it’s closer than we might think.
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