Interest in the possibilities afforded by algorithms and big data continues to blossom as early adopters gain benefits from AI systems that automate decisions as varied as making customer recommendations, screening job applicants, detecting fraud, and optimizing logistical routes.1 But when AI applications fail, they can do so quite spectacularly.2 Consider the recent example of Australia's "robodebt" scandal.3 In 2015, the Australian government established its Income Compliance Program, with the goal of clawing back unemployment and disability benefits that had been made inappropriately to recipients. It set out to identify overpayments by analyzing discrepancies between the annual income that individuals reported and the income assessed by the Australian Tax Office.

A schism is emerging in the scientific enterprise. On the one side is the human mind, the source of every story, theory and explanation that our species holds dear. On the other stand the machines, whose algorithms possess astonishing predictive power but whose inner workings remain radically opaque to human observers. As we humans strive to understand the fundamental nature of the world, our machines churn out measurable, practical predictions that seem to extend beyond the limits of thought. While understanding might satisfy our curiosity, with its narratives about cause and effect, prediction satisfies our desires, mapping these mechanisms on to reality. We now face a choice about which kind of knowledge matters more – as well as the question of whether one stands in the way of scientific progress. Until recently, understanding and prediction were allies against ignorance.

Just as the industrial age was heralded by a new type of industrial firm, so too the age of artificial intelligence is defined by the emergence of a new kind of company, says Marco Iansiti, Harvard Business School professor and co-director of the school's Digital Initiative. In the recently released book, "Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World," he and co-author Karim R. Lakhani paint a picture of the digital firms that dominate this disruptive era, marked by both outsized risks and rewards. AI enables the massive, scale, scope, and learning potential that opens up a wealth of new opportunities for the digital enterprise, Iansiti says, but the possible downsides – from privacy concerns to algorithmic bias to wealth inequality – are equally enormous. That's why the age of AI demands a new kind of leadership, Iansiti says. Some time-honored assumptions simply no longer apply.

It seems algorithms rule the world, now scientists think they have identified the algorithm responsible for human intelligence, if true then it could revolutionise artificial intelligence Little might you realise but as you read this article good chances are your brain is running its "n 2ⁱ-1" algorithm. And who said you couldn't do maths!? Scientists in the USA now believe that our brains have a basic algorithm that enable us to not just recognise a meal, or words on a page, for example, but also the intelligence to ponder their broader implications. "A relatively simple mathematical logic underlies our complex brain computations," said Dr. Joe Tsien, a neuroscientist at the Medical College of Georgia at Augusta University. Tsien is talking about his Theory of Connectivity, a fundamental principle for how our billions of neurons assemble and align not just to acquire knowledge, but to generalise and draw conclusions from it. Scientists pull images from peoples minds using AI and fMRI "Intelligence is really about dealing with uncertainty and infinite possibilities," said Tsien, "it appears to be enabled when a group of similar neurons form a variety of cliques to handle each basic like recognising food, shelter, friends and foes. Groups of cliques then cluster into functional connectivity motifs, or FCMs, to handle every possibility in each of these basics like extrapolating that rice is part of an important food group that might be a good side dish for your meal. The more complex the thought, the more cliques join in."

Algorithm

A few years ago, a young man named Kyle Behm took a leave from his studies at Vanderbilt University in Nashville, Tennessee. He was suffering from bipolar disorder and needed time to get treatment. A year and a half later, Kyle was healthy enough to return to his studies at a different university. Around that time, he learned from a friend about a part-time job. It was just a minimum-wage job at a Kroger supermarket, but it seemed like a sure thing. His friend, who was leaving the job, could vouch for him. For a high-achieving student like Kyle, the application looked like a formality. But Kyle didn't get called in for an interview. When he inquired, his friend explained to him that he had been "red-lighted" by the personality test he'd taken when he applied for the job. The test was part of an employee selection program developed by Kronos, a workforce management company based outside Boston.