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Kalsi

Founder at Continuity 1
Building technology platform that create scalable market-ready products for leaders and entrepreneurs.
Kalsi
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“A breakthrough in Machine Learning would be worth ten Microsofts.” – Bill Gates

Machine Learning has been defined, by Arthur Samuel, as “A Field of study that gives computers the ability to learn without being explicitly programmed.” In essence, the approach draws upon the tremendous computing power at the disposal of today’s “machines” to compare vast amounts of data and iteratively improve decision making from instance to instance as more and more data gets available, and analysed. Clearly data is not in short supply today – there are more than enough scarily large numbers floating around to drive home that point adequately. This availability of data and a desire to leverage it is driving the market for Machine Learning northwards. BCC Research estimated that by 2019 this would reach $ 15.3 Billion, growing at close to 20% annually on average. This is a good time to check where we stand now – are there enough Machine Learning success stories out there to make the case that the Machine Learning age is upon us or at least, is waiting its turn in the wings?

We have written in the past, how the last holiday shopping season saw Machine Learning clearly make its presence felt among retailers and shoppers alike. 

If taking over our holidays was not enough, Machine Learning is now making a determined bid for our workplace as well. Several enterprise grade software products and solutions are now being built around a solid Machine Learning core. “More-than-CRM” products like Gainsight and Lattice are already leveraging Machine Learning to give predictive insights about sales opportunities and deals at risk of going bad. Products in the HR & Talent Management space like Entelo, are helping enterprises predict their talent needs better and to manage and engage their employees better based on system-driven insights. Finance is a space that has long been data-driven and to that extent lends itself well to the practice of Machine Learning – helping products like Anaplan move beyond providing backwards-looking reports to forward-looking projections and predictions that can aid better planning and eventually more top and bottom line.

So the workplace and our holidays are under the spell of Machine Learning – what about our life in general. Considering how much time we spend online how about we turn an eye to the inner working of the web for signs of Machine Learning? Enough has been written about how the Googles and Bings of the world are leveraging Machine Learning to refine search results, weed out fake or spam sites and, of course, also serve up more relevant and better-targeted ads to users. Even setting that kind of high tech voodoo aside for a moment, are there more everyday signs for us to see? Sure enough, the fingerprints are everywhere in the online world. Consider that bane of our existence, spam email. Have you noticed how mail systems are getting better at showing us stuff that is relevant? That guided trip to the “Promotions” folder in Gmail (or “Junk” elsewhere) is solidly based on Machine Learning. The systems have their own algorithms but as you mark mails that slip through that dragnet as spam these “machines” learn and the get even more effective. Machine Learning -1, spammers – 0!

In a sign of how attached, we are becoming to Machine Learning, we are turning to it in sickness as well as in health. The data-intensive Healthcare technology space is the next big frontier Machine Learning seems to be setting out to conquer. In a sign of the times, tech worthy Vinod Khosla said, “Doctors can be replaced by software – 80% of them. I’d much rather have a good machine learning system diagnose my disease than a median or an average doctor.” Machine Learning is coming to the party in everything from pattern and image recognition to improve the accuracy of diagnoses, to better targeting of preventive care based on data-driven insights of at-risk patients of depression, to helping healthcare providers improve clinical outcomes as well as hospital operations and financial performance. Innovative Startups, as well as Electronic Medical Records HealthTech giants alike, are looking to incorporate Machine Learning into their products in ever-increasing numbers. Another area in Healthcare feeling the Machine Learning TLC is Pharma. Pharma companies are increasingly turning to Machine Learning to analyse the results of drug trials prior to release and real world evidence post release. The objective is drugs that work as intended, for those they are targeted at. Machine Learning seems to be in the pink of health and we are all the better for it.

That’s a pretty solid case already and it doesn’t even include mention of everyone’s favourite self-driven car! It’s fair to say that the fuel in the autonomous vehicles of today and the future will be Machine Learning. So let’s recap, Machine Learning seems to be following us at work and off it, in sickness and in health, on the road and while online – pretty comprehensive acceptance it would seem. If we were advising Bill Gates, this may be the time to recommend getting into the Machine Learning game!     

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