Erik Brynjolfsson Discusses What Machine Learning Can Do

By TAP Staff Blogger

Posted on January 12, 2018


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Digital computers have transformed work in almost every sector of the economy over the past several decades. We are now at the beginning of an even larger and more rapid transformation due to recent advances in machine learning (ML), which is capable of accelerating the pace of automation itself. However, although it is clear that ML is a “general purpose technology,” like the steam engine and electricity, which spawns a plethora of additional innovations and capabilities, there is no widely shared agreement on the tasks where ML systems excel, and thus little agreement on the specific expected impacts on the workforce and on the economy more broadly.
(What Can Machine Learning Do? Workforce Implications,” by Erik Brynjolfsson and Tom Mitchell, Science, December 2017)

 

Across diverse industries, chief information officers are exploring ways that artificial intelligence (AI) and machine learning (ML) can automate repetitive tasks and augment human workers within their companies. (See “CIOs Aim to Make AI Useful, Hire the Right People to Manage It in 2018,” Wall Street Journal, December 29, 2017.) But efforts are ongoing to determine the tasks most appropriate for AI integration.

 

What job functions are suitable for machine learning? Which tasks are good candidates for automation? MIT’s Erik Brynjolfsson has been exploring these questions for a number of years. Professor Brynjolfsson is the author and co-author of several books on the economic implications of AI; and his research examines the effects of information technologies on business strategy, productivity and performance, digital commerce, and intangible assets.

 

Professor Brynjolfsson was recently interviewed by the Wall Street Journal to discuss the key take-aways from the above-quoted Science article. In “To Scale AI, Rethink Business Processes: MIT’s Brynjolfsson,” Professor Brynjolfsson discusses how companies can determine which tasks are best suited to take advantage of machine learning.

 

Below are a few excerpts from “To Scale AI, Rethink Business Processes: MIT’s Brynjolfsson.”

 

What’s keeping AI from ramping up more quickly?

 

It’s not because the technology is lagging. It really has to do with the organizational side, the culture, and the co-invention of business processes that takes a lot longer. CIOs know this. Say you’re trying to implement an ERP system. You don’t just flip the switch a couple of weeks after you buy the software. It takes years. It’s the same thing with a lot of AI applications. You have to reinvent your business processes, and that process redesign is really where the time-consuming work happens.

 

It’s not just buying some AI and popping it in. It’s rethinking your processes and reorganizing your business so that you decide who has responsibilities.

 

What might that [AI integrated in a workplace] look like in practice?

 

Suppose you are in a health-care company and you have radiologists looking at medical images. Machines are now able to do that extremely well to detect cancer. At the same time, the humans do a lot of other tasks. They talk to the other doctors, they look at other lab reports, they interface and communicate with patients, and recommend treatments. So all those other parts of the job still need to be done and they aren’t done by the machine learning, so you need to have a way that the machine learning part can communicate its recommendation to the human doctor, and the human doctor pulls in all the other information and communicates with the patient. It’s a re-engineering of that role.

 

Read the full article: To Scale AI, Rethink Business Processes: MIT’s Brynjolfsson.

 

Read more of Professor Brynjolfsson’s work on the economic implications of AI:

 


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