The impact that technology has had in our lives goes without saying. One can barely imagine a day without faintly using technology to complete tasks at work or to simply unwind at home. It is even harder to remember how one navigated through daily tasks back in the day - millennials will not relate to this - before technology became so omnipresent in every aspect of our lives. The impact is nothing short of revolutionary, causing a complete paradigm shift that has made technology not just a means to simplify daily tasks, but the definitive means to get things done.
Our reliance on technology has given it that sort of power that is readily and generously given up. The role of technology is slowly shifting from having us program the machines, to the machines being completely automated and responding autonomously when exposed to new data. Hence the rise of machine learning, with companies from tech giants such as Google and Facebook to start-ups vigorously adopting it.
The appeal of machine learning essentially stems from the gargantuan amounts of data that companies find themselves dealing with. Machine learning allows the processing of this data to be cheaper and more powerful. Moreover, the storage of data becomes more cost effective and easier to manage. As such, the benefits go beyond functionality, and into the much loved territory of cost savings, which companies relentlessly strive for.
Analytics thought leader Thomas H. Davenport wrote in The Wall Street Journal that with rapidly evolving and increasing volumes of data, "... you need fast-moving modeling streams to keep up. Humans can typically create one or two good models a week; machine learning can create thousands of models a week."
Not to be confused with data mining, machine learning does not analyze old data to uncover patterns. Machine learning is used to reproduce known patterns and knowledge, instantly assimilate those to other data, and then automatically apply those results to decision making and actions that shape and realign corporate strategies.
"There were two stages to the information age," says University of Washington computer scientist Pedro Domingos. "One stage is where we had to program computers, and the second stage, which is now beginning, is where computers can program themselves by looking at data."
It is precisely this new age that is revolutionary. Even with the advancement of technology, human intervention was typically mandatory, even if minimal. Google's self-driving car shook that status quo, since it demonstrated how machine learning allows algorithms to learn through experience, and do things we don't know how to create roadmaps for.
"Nobody actually knows how to program a car to drive," Domingos says. "We know how to drive, but we can't even explain it to ourselves. The Google car learned by driving millions of miles, and observing people driving."
The takeover of machine learning is expected to keep growing at an exponential rate. It is inevitable that the next wave of start-ups will be predominantly focused on expanding the boundless potential of machine learning, and eventually replacing the conventional models of how algorithms overthrew human minds.