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Saturday, November 21, 2015

How machine learning will affect your business

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Lukas Biewald, co-founder and CEO of CrowdFlower reports, "Machine learning techniques may have been used for years, but recently there has been an explosion in their applications. In fact, in a recent Q3 earnings call, Google CEO Sundar Pichai said “Machine learning is a core, transformative way by which we’re re-thinking how we’re doing everything.” And they’re far from the only business making that claim." 

Photo: Computerworl

In the past, successful use of machine learning algorithms required bespoke algorithms and huge R&D budgets, but all that is changing. IBM Watson, Microsoft Azure, Amazon and Alibaba all launched turnkey cloud based machine learning SaaS solutions in 2015. At the same time startups like Idibon, MetaMind, Dato and MonkeyLearn have built machine learning products that companies can take advantage of.

Gartner already puts machine learning at the top of its hype curve, and no: machine learning won’t replace all of your employees with computers or suddenly double your revenue. But that doesn’t mean that it can’t give every business a competitive advantage. There are plenty of business processes that can significantly benefit from machine learning.

So how does machine learning change the way businesses operate? 

1. Bigger upfront costs
First thing’s first: Machine learning needs training data and training data costs money. Especially training data labelled by humans.

Let me explain. To make machine learning work for business, the algorithm needs to see lots and lots of examples of what it’s supposed to be doing. If you want an algorithm to tell you if a sales lead is good, you need to show it lots and lots of examples of good sales leads and bad sales leads. If you want an algorithm to tag your support tickets you need to show it many examples of support tickets. If you localize your algorithm to a new language you probably need to collect lots of examples in that language.

In some instances, a company may have those training sets in house. For example, a bunch of disqualified or qualified leads. But say you haven’t labelled each of your support tickets as they’ve come in over the year. You’d need to have people -- either in-house or en masse via a data enrichment platform -- label those tickets. The machine will then look at those judgments and start finding connections and patterns it can learn from. 

Source: Computerworld

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