by Dan Woods
When it comes to the creative processes inherent in predictive modeling, it is time for a new paradigm, one in which the user and machine learning work in tandem to achieve better results than could be achieved working separately. Nutonian’s vision for this is machine intelligence.
What’s important to understand is how collaboration between people and machine intelligence powers statistical creativity. However, this new paradigm first requires unlearning some of the patterns established by early forms of artificial intelligence.
Consider a game of chess. A chess master has a great memory and can evaluate a lot of positions, but that’s child’s play compared to Deep Blue. This chess-playing computer is known for being the first piece of artificial intelligence to win both a chess game and a chess match against a reigning master. Deep Blue can evaluate every possible move it can take at each turn, considering 200 million positions every second.
While Deep Blue’s power to play great chess is an awesome achievement, we need to put it into context. Deep Blue’s wins were the culmination of 12 years of development towards an extremely specialized task, and its potential moves were reliant on a static list of previous games. The computer can’t invent new moves that weren’t already in its database, and it would have to start back from square one if the rules of chess ever changed.
Imagine instead that the chess master and Deep Blue were on the same side of the table, working together. What if the two could communicate? Collaboratively creating and vetting potential strategies – one using his hard-earned expertise to handle new information and uncommon situations and the other using its vast database to discover optimal strategies and provide a sounding board? Wouldn’t this combination be more powerful?
The machine intelligence paradigm puts man and machine learning on the same team as equal partners. While Nutonian’s Eureqa automatically generates potential solutions through a powerful evolutionary search process, it communicates how it arrived at its results and flexibly accommodates outside guidance. This transparency allows anyone to incorporate their expertise into the system and seed the next round of discovery.
Nutonian believes that the best results happen when the user and the machines work together as partners in the process of invention. This productive partnership between man and machine heralds the golden age of analytics.
ABOUT THE AUTHOR
Dan Woods is CTO and founder of CITO Research. He has written more than 20 books about the strategic intersection of business and technology. Dan writes about data science, cloud computing, mobility, and IT management in articles, books, and blogs, as well as in his popular column on Forbes.com.