All of us have been in a hospital for one or another reason and are aware of the medical procedures that follow during initial check up and diagnosis. Sometimes it gets a lot tedious to get so many tests done before a diagnosis is made and even some of the diseases reach a stage where they become incurable before their clinical diagnosis, like cancers.
While deep learning racks up the likes among the big data crowd, a potentially bigger phenomenon is the emergence of extremely simple machine learning models that do not require sophisticated technical and mathematical skills, or what machine learning expert Ted Dunning calls "cheesy and cheap machine learning," or simply "cheap learning."
BlackRock Inc., the $5 trillion money manager, announced last month that it would be overhauling its actively managed equities business, increasingly betting on computers rather than humans to make investment decisions. This move sent shudders through the financial services industry that has long relied on people to help others with their asset allocation decisions. For all industry observers, it is the next nail in the coffin of actively managed accounts, as technology disrupts the age old financial services business model. The question that corporate consultants must now ask is: "Are we next?
The overarching term "artificial intelligence (AI)" is a hub with many spokes. One of the most exciting of these from a business perspective is machine learning. As I explained in my first blog in the series, at its most basic, machine learning involves'teaching' a computer to learn and change when given a vast amount of data. The computer is not necessarily explicitly programmed for these changes, but instead learns to spot patterns and make connections. Therefore, the machine learns (get it?)
Artificial intelligence (AI) is a budding field. Nowadays, many businesses are trying to figure out how to use it to their advantage. Indeed, AI can help reduce operational costs, improve efficiency, generate revenue, and enhance customer experiences. In an article for the Register, Danny Bradbury says that there's a chasm between many current AI deployments and a mature approach with sensible business benefits, and companies need to know how to get from here to there.