Data Mining

How to Move From Describing to Predicting

Provided by Magenic

Category Data Mining

Type White Paper

Length 4

Publish Date May 31 2017

Date posted September 13 2017


Algorithms that predict heart attack risk. Smart cars. Facial and voice recognition tools for identifying criminals. Software that uncovers difficult-to-identify malware. Stock market fluctuation predictors. Making complex legal documents understandable to an eighth-grade reading level.
The exponential growth of data across the world combined with technological advancement has already led to these and other extraordinary uses of Artificial Intelligence (AI) and Machine Learning. Forrester Research identified AI as one of the top 15 emerging technologies that will change the world between 2017 and 2022. And now that technology visionaries are able to “train” computer software to “learn” from the data it receives –effectively simulating human cognition – the future will likely introduce applications we can barely imagine at this point in history.
“Even now, scientists do not really understand how the brain works, but there is nothing supernatural about it,” notes a recent article in The Economist. “That implies building something similar inside a machine should be possible in principle.”
One only has to look at investment spreadsheets from major players such as Google, Baidu, Microsoft, Uber, Facebook, Apple, IBM, Skype and Salesforce to foresee the continued impact on the business world. In fact, worldwide revenues for the big data and business analytics driven by AI are slated to grow from $130.1 billion in 2016 to $203 billion in 2020 – a compound annual growth rate of 11.7 percent.
Forrester goes so far as to predict that by 2020, every business worldwide will become either a “digital predator” or “digital prey,” with the former harnessing technology tools such as machine learning to create new sources of value for customers and the latter unable to evolve “until it’s too late.”

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