Video: The Impact of Machine Learning on Work Is Bigger Than You Think
A new phase of technology-enhanced work is upon us.
Artificial Intelligence and Business Strategy
In collaboration withBCG
On May 23, 2017, the MIT Sloan School of Management hosted the 14th annual CIO Symposium: “The CIO Adventure: Now, Next and… Beyond.” The one-day event brought senior IT executives together to discuss key technologies, including IoT, AI, blockchain, Big Data, DevOps, cloud computing, and cybersecurity. The main idea was to help prepare these tech leaders for challenges they face, including shepherding ongoing digital transformations, building a digital organization, and managing IT talent.
This series highlights insightful sessions from the event.
Despite much hype about artificial intelligence, we’re actually underestimating what’s coming, said MIT researcher Andrew McAfee in a fireside chat with his long-time MIT collaborator, Erik Brynjolfsson. This discussion, moderated by former MIT Technology Review editor in chief Jason Pontin, offers insights about the impact of technology-based innovations on business and society from two of the most influential thinkers in this area.
The MIT collaborators contrasted the effects of machines on work today with the effects of automation on labor during the Industrial Revolution. Whereas automation during the Industrial Revolution augmented physical work, allowing humans to be much more productive, machine learning today is augmenting knowledge workers, enabling new problems to be solved.
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The business impact of machine learning is coming in waves. Brynjolfsson and McAfee spoke about the first wave of machine learning, during which humans codified their knowledge by learning to code and training technology to learn, thereby increasing their own efficiency. We’ve now entered a second wave where machines are learning and reaching insights on their own.
According to McAfee, we’re still underestimating what’s coming. He supports this claim by referencing the recent triumph of AlphaGo over a top-ranked player of Go, a game that humans have studied for 3,000 years. Speech recognition, a particularly troublesome activity for machines in the past, has made tremendous progress, Brynjolfsson observes, citing the achievement of sub-5% error rates by some technologies. Together with other recent machine-learning achievements, we are beginning to glimpse technological capabilities beyond the automation of routine tasks; these capabilities may transform the need for high levels of employment, raising questions about humanity’s role in the economy.