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June 2, 2016

AI: Everywhere and Nowhere (Part 1)

Summary:

While beating the best human at Go is impressive, the hoopla surrounding AI and games perpetuates the confusion about AI’s ultimate mission.

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This is part 1 of a three-part series.

Artificial Intelligence (AI) is all the rage in the popular press. Even if you are an alien who just landed on Earth from a planet far away, it was impossible to miss the headlines that AlphaGo—the AI program developed by Google—beat the world champion of the game Go, Lee Sedol, 4-1.

Why is there such excitement about this AI program beating the human champion? What is in fact AI, or artificial intelligence? What does this all mean to our businesses or each one of us? To be even more melodramatic – what does this mean for humanity?

AI Defined (Really?)

Since the term was first coined in 1956, AI has suffered from shifting definitions.

The term “artificial intelligence” was first used at the second Dartmouth conference organized by John McCarthy, one of the founding fathers of AI.  Most definitions of AI revolve around “simulation of intelligent behavior by computers.” However, one of the most popular AI textbooks took AI to another level.

In “Artificial Intelligence: A Modern Approach,” Stuart Russell and Peter Norvig define AI as the designing and building of intelligent agents that receive percepts from the environment and take actions that affect that environment. This view of AI brings together a number of distinct subfields of computer vision, speech processing, natural language understanding, reasoning, knowledge representation, learning and robotics with the aim of achieving an outcome by the machine.

As AI has evolved, it has also splintered. As soon as any subfield of AI is well understood, it gets renamed, and whatever is still to be discovered gets branded as AI. For example, handwriting recognition or voice recognition was once considered AI. However, with the availability of commercial systems that can recognize written text or recognize human speech, these areas are no longer considered AI. As a result, any precise definition of AI is fraught with the danger that the definition becomes obsolete as technology advances take place.

See also: Seriously? Artificial Intelligence?

Given the difficulty of defining “intelligence” and hence “artificial intelligence,” the field of AI has resorted to beating humans in games where the humans exhibit a lot of thinking, learning or physical activity. As a result, over the past couple of decades, we have seen AI beat the best humans in chess, Jeopardy and now Go.

There are also games like soccer where a group of robots train to beat the soccer world champions one day. While beating the best humans at their own game—thinking and learning–is a laudable goal, gaming situations differ from a majority of our day-to-day activity in significant ways.

First, these games have a prescribed set of rules and well-defined and certain outcomes (e.g., win, loss or tie). Second, these games are closed-loop systems where the effect of the actions is limited to participants within the system. Third, the AI can be trained with multiple failures (e.g., losing the game) with no real consequences to participants outside the system.

Needless to say, these situations are not very common outside of the games, and the hoopla surrounding AI and games perpetuates the confusion about AI’s ultimate mission. While it is great to see that what was once considered close to impossible just two years back – beating the world champion of Go – has now been achieved, the implications of this achievement for the broader application of AI needs to be kept in perspective. It is one more feather in the cap of “deep learning,” the mechanism that AlphaGo used to beat Lee Sedol. However, the excitement of the win needs to be tempered by the daunting and challenging situations that AI software still needs to operate under.

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About the Author

Anand Rao is a principal in PwC’s advisory practice. He leads the insurance analytics practice, is the innovation lead for the U.S. firm’s analytics group and is the co-lead for the Global Project Blue, Future of Insurance research. Before joining PwC, Rao was with Mitchell Madison Group in London.

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