Machine learning is based on algorithms that can learn from data without relying on rules-based programming. It came into its own as a scientific discipline in the late 1990s as steady advances in digitisation and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so. The unmanageable volume and complexity of the big data that the world is now swimming in have increased the potential of machine learning—and the need for it.People learn from experience - and now machines do so, too.
Back in 1967, McKinsey published an article by Peter Drucker named "The manager and the moron". In it, it states that "the computer makes no decisions; it only carries orders. It's a total moron, and therein lies its strength. It forces us to think, to set the criteria. The stupider the tool, the brighter the master has to be - and this is the dumbest tool we have ever had".
Half a century later, this statement couldn't seem further away from the truth. Progress has been striking: computers now can process human speech and answer questions, make recommendations and performs a series of actions. They can even recognise objects and patterns in images. Computers are replacing skilled workers in a large variety of fields - ranging from medicine and architecture to aviation and the law. In fact, a recent study carried out by Oxford University concluded that over 47% of all U.S. jobs are susceptible to computerisation!
So, should we be racing with or against intelligent machines?
"Worth reading Superintelligence by Bostrom. We need to be super careful with AI. Potentially more dangerous than nukes."
— Elon Musk (@elonmusk) August 3, 2014
Image source: Uni Hamburg
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