In this report, members of the Technology Policy Program of the Mercatus Center comment on the appropriate policy framework for artificial intelligence (AI) technologies at this nascent stage of their development and to make the case for prudence, patience, and a continuing embrace of "permissionless innovation." Permissionless innovation refers to the idea that "experimentation with new technologies and business models should generally be permitted by default. Unless a compelling case can be made that a new invention will bring serious harm to society, innovation should be allowed to continue unabated and problems, if they develop at all, can be addressed later." Policymakers may be tempted to preemptively restrict AI technologies out of an abundance of caution for the perceived risks these new innovations might seem to pose. However, an examination of the history of US technology policy demonstrates that these concerns can be adequately addressed without quashing a potentially revolutionary new industry.
I believe the three ways in which AI can enhance human innovation -- namely, creating space for innovation, generating novel patterns and democratizing creativity -- have the potential for real impact on the business world. However, a few qualifying words are due before we end the discussion. True human creativity and innovation are only possible when there is a solid basis to build on. Moreover, the more robust the substrate on which you build, the more ingenious the innovation will be. The more educated and trained a person is, the more they can create.
Machine learning is a branch of artificial intelligence that enables machines to learn on their own, without much human supervision, drinking deeply from the well of Big Data. Computers essentially write and follow their own programs based on the statistical relationships they discover in unstructured data--and are roiling industries ranging from credit cards to automobiles in the process. "It's speed and the ability to learn from data that gives machine learning the power to provide tremendous insights in ways that humans could never do on their own or with basic business-intelligence tools," says Mike Tuchen, CEO of Talend, a Los Altos, California-based big-data integration firm. Or machine learning "can use algorithms to mine historical data for outcomes that are different than with traditional simulation." At financial companies, for instance, machine learning can assess insider-trading activities and identify potential fraudulent activities that could trip a regulatory investigation.
Today, many businesses have entered the "innovation imperative" -- an executive mandate for the company to launch the next wave of innovation in their industry, all while maintaining and improving business with their current customers. I work on a team at Epsilon tasked with corporate innovation within our agency services discipline. Across the agency, our innovation team is charged with leveraging research and data in all its shapes, sizes and types to figure out what makes consumers tick so we can help our clients connect with them through smarter marketing and more effective creative. For example, we've recently introduced machine learning as a new accelerant to our data-infused creative process. Innovation really is key for any company seeking to proactively thwart disruption to their business model, operations or products.