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Machine learning, a method of data analysis that automates analytical model building, is at the top of the “hype curve” for emerging technologies and is one of the top 10 strategic technology trends for 2016, according to Gartner. It’s safe to say that most of us are already interacting with machine learning applications on a daily basis whether it’s Apple’s Siri, Facebook’s face detection, Netflix personalized recommendations, or iOS’s autocorrect. And from a business perspective, basically any organization that gathers data with the intention of acting on it can benefit from machine learning.
But what can we learn from this growing trend? What can we do with such a widely applicable technology and how can we maximize its potential? What techniques are making it happen? Will its functionality evolve to threaten large numbers of white collar jobs? What problems does it solve and how does it affect businesses, the Internet of Things, cloud, and cybersecurity? And where are investors placing their bets in the evolving machine learning landscape?
One area where machine learning is having great impact is cybersecurity and fraud detection (at this year’s RSA Conference, it was hard to find a vendor that wasn’t talking about machine learning). One of the key challenges in cybersecurity is searching for the proverbial needle in a haystack – finding the unusual malicious activity amongst the vast amounts of legitimate activity. This problem plays to the strengths of machine learning in being able to identify patterns and anomalies in large data sets. But can machine learning consistently outperform expert hackers? What happens when you try to remove human expertise from the equation?
For many people machine learning has become synonymous with artificial intelligence (AI). AI is defined as the ability of machines to simulate the intelligence of humans – perform the tasks that are commonly thought to require human intelligence. It’s probably most accurate to describe machine learning as a subset of AI, but machine learning (and its subset, deep learning) is where much of the exciting recent advances have taken place. For example, Google’s AlphaGo system recently became the first machine to beat a professional player in Go, an achievement some thought wouldn’t happen for decades.
At Ascent, we have a deep interest in machine learning beyond its intriguing potential. Rapidminer, an open source predictive analytics platform and one of our portfolio companies, offers machine learning as one of its core capabilities. Another company in our portfolio, Sidecar, leverages machine learning to help retailers get the most from their advertising dollars.
Machine learning is a rich topic. We’ll be delving into it at our next Ascent B2B IT Forum on June 21, at the Microsoft New England Research and Development Center in Cambridge, MA. Register here.