Prior to machine learning, modeling through algorithms required people to understand the particular problem domain, extract facts from the existing data, and write large, “heuristics based” programs that used conditional rules to model different possible outcomes from the incoming data, Villanustre says. “These earlier systems required experts to sift through data to understand reality and describe it through conditional statements that a computer could understand,” he says. “This was very tedious, hard work, and better left to computers.”
Machine learning changed that by letting computers extract those facts and represent reality through statistical equations-based models instead, Villanustre says. “This saves countless hours of domain experts’ time and allows them to work with data sets that humans would struggle to deal with otherwise,” he says. “The resulting computer programs are more compact, easier to implement, and more efficient.”
LNRS uses machine learning to describe complete networks of organizations and individuals to identify fraud rings. It also uses the technology to assess and make predictions on credit and insurance risk, identify fraud in health-care-related transactions, and help capture criminals.
“Machine learning is at the core of everything that we do,” Villanustre says. And the company is looking into the latest iterations of the technology. Some of the recent developments around deep belief networks -- generative graphical models composed of multiple layers of latent variables with connections between the layers -- and deep learning are proving to be promising fields of applications, he says.
“It is always important for us to validate these new methodologies with the laws and regulations of the respective countries in which we work to ensure that they can be used in ways that maximize the benefit to individuals and society,” Villanustre says.
Machine learning in the mainstream
The adoption of machine learning is likely to be diverse and across a range of industries, including retail, automotive, financial services, and health care, says Johnston of Deloitte.
In some cases, it will help transform the way companies interact with customers, Johnston says. For example, in the retail industry, machine learning could completely reshape the retail customer experience. The improved ability to use facial recognition as a customer identification tool is being applied in new ways by companies such as Amazon at its Amazon Go stores or through its Alexa platform.
“Amazon Go removes the need for checkouts through the use of computer vision, sensor fusion, and deep or machine learning, and I expect many shopping centers and retailers to start exploring similar options this year,” Johnston said.
The fact that common devices such as smartphones will be equipped with machine learning capabilities means the technology will no longer be limited to theoretical or highly selective applications.
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