UCF College of Engineering and Computer Science artificial intelligence researcher and associate professor Kenneth Stanley, Ph.D., provides a thought-provoking and contrarian perspective on how “greatness” is achieved.  In the soon-to-be-released book Why Greatness Cannot Be Planned: The Myth of the Objective, Stanley and co-author Joel Lehman, Ph.D., explore the provocative concept that setting specific goals and measurable objectives can have a detrimental impact on achieving a significant accomplishment. Rather, the authors argue, greatness occurs as the result of intelligent serendipitous discovery.

“The book explores the radical idea that our most notable successes and greatest achievements are not the result of having these specific achievements as the original objective,” said

Stanley. “On the contrary, society’s greatest achievements evolve from ideas and activities that appear totally foreign and disconnected to the end result. It’s what we refer to as the objective paradox.”

Anticipated to be released in mid-May, the book is based on years of artificial intelligence research conducted by Stanley and Lehman, a UCF graduate and recent post-doctoral fellow at the University of Texas at Austin. The authors explain that there is currently an over-emphasis on goal-setting and adhering to measurable objectives. Entire industries and institutions—including education, science funding and medical research—are heavily influenced by this philosophy. The problem, Stanley and Lehman explain, is that the scientific evidence and historical data suggest this doesn’t work.

“There is an increased opportunity for greatness when there is no static objective but there is flexibility to discover and redirect,” said Stanley.  “Oftentimes, a great success is achieved through a serendipitous discovery. The interesting aspect, however, is that the discoverer is typically prepared and motivated to take advantage of the discovery and the new direction it provides. It’s what we refer to as intelligent serendipitous discovery.”

Stanley points to rock ‘n’ roll, the computer, and microwave oven as examples of this intelligent kind of serendipitous discovery.  “Elvis didn’t start out with the goal of playing a key role in the creation of rock ‘n’ roll.  He was just following a path that felt natural and took advantage of the new direction it provided—and the rest is history.”

The idea for the book took root while Stanley was observing the interactions and results of his Picbreeder effort at UCF. Stanley’s Evolutionary Complexity Research Group at UCF created Picbreeder as an online platform that allows users to “breed” pictures by selecting “parents” to produce evolving “offspring.”

“Our observation of users engaged with Picbreeder actually inspired the main theme of the book, which is that sometimes achievement is more effective if you do not have an explicit objective,” said Stanley. “The key observation was that the most impressive images the users bred on Picbreeder were almost always not their objective. This insight struck us as very deep. We realized the implication of allowing great achievements to happen serendipitously, rather than as a result of chasing objectives.”

Why Greatness Cannot Be Planned: The Myth of the Objective, is available for pre-order immediately at Amazon and will soon be available for shipment. Visit amazon.com for more details.

Stanley has more than 80 peer-reviewed published articles, 10 of which have won best paper awards. His research focuses on abstracting the essential properties of natural evolution that made it possible to discover astronomically complex structures such as the human brain and is, in part, an approach to artificial intelligence. He is regularly invited to speak at venues across the world.  Lehman, who will begin as an assistant professor at the IT University of Copenhagen later this year, also has numerous published articles and is a frequent presenter. His research with Stanley led to a new artificial intelligence algorithm called “novelty search,” which is included in the book. This algorithm searches only for new behaviors such as robot behaviors, but with no other objective, and often finds useful or interesting behaviors.