Aug 24, 2018 - 2 minutes read time
To gain any groundbreaking insights into marketing, consumer psychology, popular art, or even a mobile app’s user interface, an innovator is still necessary to synthesize human experience, newspaper articles, and stories about how people have reacted, conversed and complained.
Extracting hidden and ambiguous meanings in the social realm requires not just big data, which are often broad and shallow and devoid of emotional meanings, but also small data—the thick and deep information about individuals, for example, about how a sickly child at the hospital is nervously waiting for an MRI scan.
Although machine learning can be extremely powerful. Big data concern themselves with correlation, not causation. Under the hood of IBM Watson, for instance, an algorithm establishes many statistically significant relationships but never quite explains why they are so. The way we assess the performance of computer systems can also be misleading. The notion of machine intelligence, strangely enough, remains ill-defined.
Scientists tend to evaluate a computer’s performance based on tasks that are difficult for humans, such as playing chess, Go, or Jeopardy! These tasks are unnatural and difficult for humans because they require deliberation and planning—the type of cognitive work that our evolutionary history has endowed us with only most recently. These are not the sort of tasks that human intelligence has evolved for—making very quick decisions using limited sensory data with limited calculation power. Consequently, a computer can play chess far better than it can recognize the face of its opponent.
Precisely because people are more agile and dexterous than the most advanced robots in identifying objects and understanding nuances, the results are always better when computers and humans work together, rather than when either one does it alone, whether it is playing chess or solving other real-world problems.
As I explain in, you can try pushing a machine toward the bleeding edge, but it still won’t beat an average machine that works alongside a human. The future is merging machine capability and human consciousness. Each doing their best work and managerial creativity is the human advantage.
Therefore, successful businesses must automate as much as possible and simultaneously unleash the full potential of human creativity.
Outlast your competition and thrive in an ever-changing world
In Leap, Howard Yu, LEGO professor of strategy and innovation at IMD, explains how companies can prosper, not just survive. Leap identifies five fundamental principles that allow companies to stay successful in the face of such competition.
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