In algorithms we trust


In previous posts, I pondered the evolution of problem solving that is being data-driven by our increasing reliance on algorithms, which some mistrust as a signal that we’re shifting from human to artificial intelligence (AI).

Would you like to play a game?

“Slowly but surely,” John MacCormick explained in his book Nine Algorithms that Changed the Future, “AI has been chipping away at the collection of thought processes that might be defined as uniquely human. For years many believe that the intuition and insight of human chess champions would beat any computer program, which must necessarily rely on a deterministic set of rules rather than intuition. Yet this apparent stumbling block for AI was convincingly eradicated in 1997, when IBM’s Deep Blue computer beat world champion Gary Kasparov.”

Another AI stumbling block was hurdled in 2011, when IBM’s Watson won Jeopardy! by defeating Brad Rutter and Ken Jennings, two of the television quiz show’s most celebrated champions. “I for one welcome our new computer overlords,” Jennings joked afterward. In his TED talk video Watson, Jeopardy and me, the obsolete know-it-all, Jennings talked about how it felt to have a computer literally beat him at his own game, and also made his case for the value of good old-fashioned human knowledge.

Everyday algorithms

“Meanwhile,” MacCormick continued, “the success stories of AI were gradually creeping into the lives of ordinary people too. Automated telephone systems, servicing customers through speech recognition, became the norm. Computer-controlled opponents in video games began to exhibit human-like strategies, even including personality traits and foibles. Online services such as Amazon and Netflix began to recommend items based on automatically inferred individual preferences, often with surprisingly pleasing results.”

While many of us, myself included, lament the ubiquity of automated customer service telephone systems, we enjoy other examples of speech recognition facilitated by AI advancements, such as voice-activated smartphone commands and speech to text translation (which I used to dictate portions of this post).

Who still uses a travel agent?

In fact, the progress of AI is altering our perception of many tasks. “Consider a task,” MacCormick explained, “that once indisputably required the intelligent input of humans, who would actually be paid for their expertise: planning the itinerary of a multi-step plane trip. In 1990, a good human travel agent could make a huge difference in finding a convenient and low-cost itinerary. By 2010, however, this task was performed better by computers than humans. Exactly how computers achieve this would be an interesting story in itself, as they do use several fascinating algorithms for planning itineraries. But even more important is the effect of the systems on our perception of the task. I would argue that by 2010, the task of planning an itinerary was perceived as purely mechanistic by a significant majority of humans—in stark contrast to the perception 20 years earlier.”

In algorithms we trust

As MacCormick concluded, whether or not you consider the algorithms of advancing AI to be truly intelligent, you can expect to see a lot more of them in the years ahead. From something as trivial as answering trivia questions to something as important as making lung cancer treatment decisions (which, in 2013, became the first commercial application of IBM Watson’s software system), it is in algorithms we trust more and more of our tasks.


About Author

Jim Harris

Blogger-in-Chief at Obsessive-Compulsive Data Quality (OCDQ)

Jim Harris is a recognized data quality thought leader with 25 years of enterprise data management industry experience. Jim is an independent consultant, speaker, and freelance writer. Jim is the Blogger-in-Chief at Obsessive-Compulsive Data Quality, an independent blog offering a vendor-neutral perspective on data quality and its related disciplines, including data governance, master data management, and business intelligence.

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