Maybe the secret to the complicated relationship between humans and machines comes down to a simple instruction: We all just need to get along. The conversation has been a hot one since the dawn of the Industrial Revolution: Would machines take our jobs, rob us of our creativity and intuition and in the process turn us into drones that are more machine-like than human? But when everyone simmers down, it becomes clear that this is not an either-or situation, that in fact taking the best of both -- the best of humans and the best of the machines -- can lead to accomplishments that neither could achieve on its own. The truth is that in the end, machines amplify human potential. “When you are building this whole man and machine model, the machine needs to be a companion,” says Ashutosh Garg, BloomReach’s chief technical officer. “Machines need to be an assistant. You don’t want to be competing with a machine.” Garg, a former IBM researcher and Google scientist, says that machines and humans are good at different things -- meaning the potential for partnership abounds. A machine, for instance, is good at remembering where your keys are; you are not. A machine can churn through massive amounts of data and come up with a result. You would be overwhelmed by massive amounts of data. But you are good at generalizing based on past experience. Machines aren’t. You’ve got intuition; machines don’t. The differences between humans and machines mean that each has a role. For instance, Garg says, think about innovative ideas and the evolution of them. “As a machine, you can come up with an algorithm that will make your iPhone better,” he explains. “But a machine cannot come up with an iPhone. The machine is going from the iPhone 4 to the iPhone 5.” In other words, innovation takes a creative spark; a very human creative spark. But a machine is able to amplify that creativity. A machine can in essence survey millions of iPhone users and track the performance of millions of iPhones. It can tell human engineers, designers and marketers what apps iPhone users favor and what apps they are ignoring. It can tell the humans at what times and for what purposes people are using their iPhones. Are consumers primarily using their phones to listen to music? The humans are going to want to work on improving the next version’s speakers and acoustics. Have iPhones become consumers’ go-to cameras? Maybe it’s time for designers and engineers to improve upon the lens and flash. Is the iPhone battery dying at dinnertime instead of bedtime? Time for a better battery. Are users complaining about the weight of the phone? Machines can process millions of comments and figure that out. Then the humans can figure out how to make the phone lighter. Or think of the human and machine partnership in launching a marketing campaign, Garg says. An experienced marketer, for example, might rely on intuition and experience to conclude that blue sweaters are going to be a big seller in the coming season. “Machines cannot come up with that. The amount of data they would require to come up with that is practically non-existent,” he says. “Anytime you are trying to do an innovative thing, out of the box, trying to set a trend, you don’t have the data for that. You have your wild intuition.” But if you’re building a results-oriented marketing campaign to sell those blue sweaters, turning to a data-infused, intelligent machine makes a lot of sense. “If you’re doing certain things, machines can help you improve things -- a lot,” Garg says. “Machines can tell you the likelihood of this campaign performing, of having conversions, what the cost is.” A human marketer can figure out what kind of Web page makes sense for the campaign -- a theme page, a splash page, a collection concept page. A machine can quickly process a ton of product data -- what are consumers searching for, what do they buy as a result, what other products do they view and purchase as a result of their initial search -- and determine exactly which products should go on the page. The machine can not only come up with the answer faster, its answer is more reliable than one arrived at by a human, who has no chance of processing the same amount of data and who is left to rely on partial data and artistic hunches. “Optimization is a tedious task. Machines are very good at that, churning through a bunch of data,” Garg says. “Machines are great optimizers and humans are great innovators.” It would seem the surest path to peace and prosperity when it comes to the co-existence of humans and machines, then, is for everyone to keep that squarely in mind. Photo of Ashutosh Garg by BloomReach; photo of Lego robots by Nick Royer and factory worker by Kheel Center published under Creative Commons license. Mike Cassidy is BloomReach’s storyteller. Contact him at email@example.com ; follow him on Twitter at @mikecassidy.