Rise of machine learning

The recent surge of data has empowered a field of computer science that uses statistical techniques to give computer systems the ability to learn: Machine Learning. Modern Machine Learning Algorithms are able to overcome strictly static program instructions and make data-driven predictions that help companies make decisions with minimal human intervention.

IDC forecasts that spending on Machine Learning will grow from $12 billion in 2017 to $57.6 billion by 2021. What’s more, Machine Learning patents grew at a 34 percent CAGR between 2013 and 2017, making it the third-fastest growing category of all patents granted.

One survey, commissioned by MemSQL in cooperation with O’Reilly Media, on the adoption of Machine Learning in the workplace, discovered that 61 percent of respondents indicated Machine Learning as their companies’ most significant data initiative for the following year, and 74 percent of all respondents considered Machine Learning to be a game changer.

“We really are living in this era of Machine Learning, which is the dominant AI technology, and probably will be for some time to come. Executives who I talk to today are a lot more aware of previous waves of disruption, and they’re more keenly aware of the possibility that it’s going to happen again. They’ve internalized Andy Grove’s advice that only the paranoid survive,” said Andrew McAfee, Principal Research Scientist at the Massachusetts Institute of Technology (MIT) and a co-director of the MIT Initiative on the Digital Economy.

Already, Machine Learning is helping doctors with diagnosis, making marketing efforts more personal, getting better at spotting potential cases of fraud across many different fields, translating obscure legalese in contracts into plain language, predicting the movement of the stock market, and eliminating false alarms at stadiums, concerts, and other venues, just to give a few examples.

Machine Learning is also very effective when used to help companies analyze massive quantities of data in order to find patterns that are not immediately obvious to humans. Splunk is particularly popular in this regard, and it has already been applied a number of times to improve IT operations and gain visibility into the performance and availability of infrastructure and applications.

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