When Brian Uzzi spoke to the crowd of more than 200 students and visitors to The Pavilion, he had one goal in mind: Make machine learning approachable – even interesting – to the layman.
Uzzi, an artificial intelligence expert who spoke at Thursday’s C Spire Tech Experience, said machine learning can benefit the everyday person.
Brian Uzzi, Northwestern University professor, explains the use and necessity of machine learning in everyday life. (Photo by: Marlee Crawford)
“You don’t have to learn how to do machine learning. You have to know where it’s valuable,” Uzzi said.
Uzzi, a professor at Northwestern University, spoke about three multi-million dollar companies that own relatively little: Amazon, Uber and Facebook. These industries primarily rely upon machine learning to recommend new friends, new purchases and to coordinate Uber drivers, but they don’t produce content, have stocks from which a user can purchase or own any cars.
Despite the benefits that can come from using machine learning, Uzzi said many businesses and people are wary of it. This, he said, is partially because of the competitive mentality many bring to innovative technology.
Uzzi told the folktale of John Henry. In Henry’s time, the story was simple. Man versus machine: The machine may have had more strength, but a human’s brain was far superior. But now, that’s changing, Uzzi said.
“Machines have begun to challenge us in the places we thought we couldn’t be challenged,” Uzzi said.
Instead of a dichotomy of man versus the machine, Uzzi proposed that society should start thinking of how it can work in collaboration with technology.
“The idea is not to think about machines the way John Henry thought about them – as a competitor – but as a collaborator.”
Perhaps the closest example of machine learning – and the fear that surrounds us – was Pepper, a highly intelligent, humanoid robot that can detect emotions and speak in sentences.
Upstairs, Pepper spoke in full sentences, gestured and asked questions. Some people hung back and watched the robot interact with others instead of talking to her directly.
According to Uzzi, this wariness is common.
Many who don’t understand machine learning are less likely to trust it. Uzzi said we have a tendency to think of artificial intelligence as the Terminator, not C3PO.
That tendency doesn’t just exist among technology novices, however. Uzzi said in an experiment with a chess-playing computer partner and a grand master chess player, the grand master wouldn’t take the advice of the computer – even when the recommendations would have ensured a win.
“We don’t want to believe that there’s something out there that can make us better when we’ve invested so much in becoming the best.”
Among those who do embrace machine learning, Uzzi said there is a common question: Where should people interested in technology go? Where are the major hubs for innovation? Uzzi’s answer was simple: everywhere.
“There are a lot of places that could be in the running for a great technology hub,” Uzzi said. “That means if you map the technology hubs today, they don’t have to be the technology hubs of tomorrow. A very capable place like Mississippi should try to get in the game.”
