This post is based on ideas from my latest book, The Agile Consumer. To learn more or purchase the book, you can read more on my website.

Let’s continue our 3-part exploration of the next generation of consumers and the makings of our agile future.

We are years away from the Terminator-style scenario where cyborgs, or human-machine hybrids, are roaming the earth. But that doesn’t mean that we’re that far away from the majority of humanity relying heavily on machines to do more than display information and serve as always-on search engines for us.

After all, computers have the advantage of being able to analyze and process huge amounts of data without getting fatigued. This means that machines can do more than take orders from us while dependent on our intelligence, creativity, and insights. It means that they can think creatively, learn for themselves, and augment human knowledge and insight in ways we previously thought possible only in science fiction.

In the artificial intelligence (AI) world, recent high-profile events like DeepMind’s AlphaGo AI beating Go match champion Ke Ji, (widely considered to be the highly complex game’s best human player) show just how far AI and machine learning have come. 

Artificial intelligence and machine learning

What does it mean when human intelligence and creativity and computers’ ability to crunch massive amounts of data are augmented by an intelligence that is creative, and infinitely scalable?

These technologies allow for exponential growth, choice and opportunity—whether using rules-based AI or machine learning, 

What human-computer integration means for the agile consumer

In the late 1950s, Army Specialist William D. Mellin was featured in a newspaper article about Army mathematicians and their work with computers. He is widely credited with coining the term “garbage in, garbage out,” or GIGO, which essentially means that if you give a computer a bad request or provide bad data as a starting point, you’re likely to get a useless response.

In 1957, computers took up entire rooms, required hours (if not days) to program, and were only capable of a fraction of what they are today. Carefully using punch cards and constructing the perfect query would get you what would now be considered a very simple, often mathematical response.

Better and more complex integration between people and machines means that computers will move beyond being simple order takers. If machines are dependent on humans to determine the right questions, they can only provide the best answer possible to that query. But what if the solution needed is something else entirely?

Artificial intelligence means that GIGO will be less prevalent once the systems are better able to assess when that first “garbage” exist. Computers will better understand and adapt enough to take “garbage” questions and turn them into something which will yield the intended result, and eventually determine when the premise of the question or query is faulty. 

While we’re still a ways off from this in reality, a very simple current example of this is how a Google search query with a misspelled word is often corrected by the search engine. “Did you mean…” and a word with the correct spelling is often displayed. Or how about the way that Gmail will offer to finish your sentences? While both of these sometimes provide comically bad (or worse, offensive) results, their accuracy continues to improve. 

Taking this many steps further, think about the time and effort it takes to solve complex financial or health care problems. How much time and money does starting down a well-intentioned, but incorrect path cost organizations? 

Despite the fact that some of these missteps have brought about happy accidents of their own, the promise of AI is that we will get better answers more quickly to the problems we are hoping to solve.

This post is based on ideas from my latest book, The Agile Consumer. To learn more or purchase the book, you can read more on my website.