Defining Intelligence

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The dictionary definition of “artificial intelligence” is the capability of a machine to imitate intelligent human behavior.

Determining the meaning of “intelligence,” is the greater challenge.

While we all agree that intelligence has something to do with knowledge and the ability to reason, human intelligence seems to go beyond that to include consciousness or self-awareness, wisdom, emotion, sympathy, intuition and creativity.

Plus human intelligence comes in many forms. While some people are highly intelligent in the field of mathematics, others excel in art, music, politics, business, medicine, law, linguistics and so on. Some people may excel in academics, whereas others are skilled in trades or have a higher level of emotional competence.

Without a reliable standard for measuring human intelligence, it’s very difficult to point to a computer and say that it’s behaving intelligently.

Computers are certainly very good at performing certain tasks and may do so much better and faster than humans, but does that make them intelligent? For example, computers have been able to beat humans in chess for decades. IBM Watson beat some of the best champions in the game show Jeopardy. Google’s DeepMind has beaten the best players in the 2500-year-old Chinese game called “Go” — a game so complex that there are thought to be more possible configurations of the board than there are atoms in the universe. Yet none of these computers understands the purpose of a game or has a reason to play.

As impressive as these accomplishments are, they are still just a product of a computer’s special talent for pattern matching — extracting information from its database that enables it to answer a question or perform a task.

Pattern Matching

This seems to be intelligent behavior only because a computer is excellent at that particular task. However, we rarely attribute human characteristics to other machines, such as boats that can “swim” faster or hydraulic jacks that are “stronger” and can easily lift a car above a mechanic’s head.

In many ways a game is a perfect environment for a computer. It has set rules with a certain number of possibilities that can be stored in a database. When IBM’s Watson played Jeopardy all it needed to do was use natural language processing (NLP) to understand the question, buzz in faster than the other contestants and apply pattern matching to find the correct answer in its database.

Early AI developers knew that computers had the potential to excel in a world of set rules and possibilities. Only a few years after the premier AI conference, developers had their first version of a chess program. The program could match an opponent’s move with thousands of possible counter moves and play out thousands of games to determine the potential ramifications of making a move before deciding which piece to move and where to move it, and it could do so in a matter of seconds.

Artificial intelligence is always more impressive when computers are on their home turf — when the rules are clear and the possibilities limited. Organizations benefit most from AI are those that work within a well-defined space with set rules, so it’s no surprise that organizations like Google fully embrace AI. Google’s entire business involves pattern matching — matching users’ questions with a massive database of answers. AI experts often refer to this as good old-fashioned artificial intelligence (GOFAI).

If you’re thinking about incorporating AI in your business, consider what computers are really good at — pattern matching. Do you have a lot of pattern matching in your organization? Does a lot of your work have set rules and possibilities?

It’ll be this work that will be the first to benefit from AI.

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