Cyborg BI
by Paul Sonderegger
Forget about agile BI or self-service BI. Think bigger: Cyborg BI.
In his recent Wired story “The Cyborg Advantage,” Clive Thompson writes about a “freestyle” chess tournament where any kind of entrant was allowed – human, machine or a combination of both. The winner was neither a grandmaster nor a supercomputer. It was a couple of amateur players using PCs and common chess apps. It seems that regular players assisted by regular computers beats both brilliance or brute force alone.
The combination teams were originally the idea of Garry Kasparov, who has the genuine distinction
of being a world champion chess player and the dubious one of being the first to lose to a computer. Several years after his loss to IBM’s Deep Blue, he wondered what would happen if people and computers were to cooperate rather than compete. He called this new kind of play Advanced Chess.
This combination sounds an awful lot like our Google-, Facebook-, Twitter-infused lives. And Thompson points out the critical implication: In our information-rich world, the key to success is knowing how and when to turn to information technology for help. This may sound like the obvious man-machine relationship, but it wasn’t always this way.
For most of the twentieth century, the obvious relationship was exactly the opposite. Since mechanization was widely accepted as key to economic prosperity, people had to adapt to the ways of machines. The 1933 Chicago World’s Fair refined this idea down to a pithy slogan: Science Finds – Industry Applies – Man Conforms. At the time, this seemed self-evident. After all, in the previous hundred years, new machines revolutionized transportation, manufacturing, agriculture, even kitchen appliances, transforming the way people lived and worked.
One of the clearest examples of the World’s Fair slogan in action is the military’s use of computing power in early antiaircraft guns. In the 1940s, Sperry, a defense contractor, experimented with rudimentary computers to help gunners solve the very difficult problem of how to hit one moving object, a plane, with another, a very big bullet. The computations were complex and so was the machinery. The person operating the gun was “the glue that held integrated systems together.” But despite that importance, the gunners were considered “human servo-mechanisms”. They were literally cogs in a machine.
So, how did we go from a world where people adapt to machines to save them from their frailties, to one where things work the other way around? Two big shifts changed the way we think about the man-machine relationship. The first is from awe of machines to ownership. The second is from information scarcity to super-abundance.
Early computers, like the ENIAC, built in the 1940s, were massive things, filling entire rooms. They were enormous, intimidating and secret. The iPhone in your pocket is over 1,000 times more powerful. And if you get tired of it or break it, you can get a new one this afternoon. Our everyday computers do not awe us, they serve us. And what they serve us is an inexhaustible supply of information. The ENIAC just crunched numbers. But our phones give us immediate access to everything from President Lincoln’s personal correspondence to pictures of our cousin’s roommate’s friend’s pottery class. It is but one tap between the sublime and the ridiculous.
These changes matter because an ever-increasing percentage of the workforce is essentially paid to think and make decisions. In a 2005 study, McKinsey, a consultancy, classified US jobs into three categories – transformational (turning raw materials into intermediate or finished goods), transactional (interacting with others in ways that can be automated through rules), and tacit (complex interactions that defy automation). It found that 41% of the US workforce were employed in tacit jobs and that between 1998 and 2005 this category of work grew “two and a half times faster than the number of transactional jobs and three times faster than employment in the entire national economy.” For more and more of us, our jobs require that we exploit information in greater volume and variety than ever before, and we’re dependent on our technology tools to do it. We’re not just playing advanced chess, we’re living it.
Here’s where the outcome of the freestyle tournament holds out hope. We don’t all have to become grandmasters. As Kasparov noted, “Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.” Managers and front-line employees with the right tools and a knack for using them can gain insights even a CEO with the same tools and lack of local knowledge cannot. The key principles for designing these tools are already known. But they’re scattered across disparate, specialized disciplines like cognitive linguistics, interaction design, and behavioral economics. In future posts, we’ll pull them together and apply them.
Paul Sonderegger
