Winning the Race With Ever-Smarter Machines

  • Erik Brynjolfsson and Andrew McAfee
  • December 21, 2011

Rapid advances in information technology are yielding applications that can do anything from answering game show questions to driving cars. But to gain true leverage from these ever-improving technologies, companies need new processes and business models.

In 2011, an IBM supercomputer called Watson beat human champions in the Jeopardy! game show.

Image courtesy of IBM.

In the past few years, progress in information technology — in computer hardware, software and networks — has been so rapid and so surprising that many present-day organizations, institutions, policies and mind-sets are not keeping up. We used to be pretty confident that we knew the relative strengths and weaknesses of computers vis-à-vis humans. But computers have started making inroads in some unexpected areas — and this has significant implications for managers and organizations.

A clear illustration of the dramatic increase in computing power comes from comparing a book published in 2004 with an announcement made in 2010. The book is The New Division of Labor by economists Frank Levy and Richard Murnane, and it’s a thoroughly researched description of the comparative capabilities of computers and human workers.

In its second chapter, titled “Why People Still Matter,” the authors present a spectrum of information-processing tasks. At one end are straightforward applications of existing rules. These tasks, such as performing arithmetic, can be easily automated, since computers are good at following rules.

At the other end of the complexity spectrum are pattern recognition tasks where the rules can’t be inferred. The New Division of Labor gives driving in traffic as an example of this type of task, and asserts that it is not automatable:

The … truck driver is processing a constant stream of [visual, aural and tactile] information from his environment. … [T]o program this behavior we could begin with a video camera and other sensors to capture the sensory input. But executing a left turn against oncoming traffic involves so many factors that it is hard to imagine discovering the set of rules that can replicate a driver’s behavior. …

Articulating [human] knowledge and embedding it in software for all but highly structured situations are at present enormously difficult tasks. … Computers cannot easily substitute for humans in [jobs like truck driving].1

The results of the first DARPA Grand Challenge, held in 2004, supported Levy and Murnane’s conclusion. The challenge was to build a driverless vehicle that could navigate a 142-mile route through the Mojave Desert. The “winning” team made it less than eight miles before failing.