AI and the productivity paradox – Irving Wladawsky-Berger

MIT Sloan Visiting Lecturer Irving Wladawsky-Berger

MIT Sloan Visiting Lecturer Irving Wladawsky-Berger

From The Wall Street Journal

Artificial intelligence is now applied to tasks that not long ago were viewed as the exclusive domain of humans, matching or surpassing human level performance. But, at the same time, productivity growth has significantly declined over the past decade, and income has continued to stagnate for the majority of Americans. This puzzling contradiction is addressed in “Artificial Intelligences and the Modern Productivity Paradox,” a working paper recently published by the National Bureau of Economic Research.

As the paper’s authors, MIT professor Erik Brynjolfsson, MIT PhD candidate Daniel Rock and University of Chicago professor Chad Syverson, note: “Aggregate labor productivity growth in the U.S. averaged only 1.3% per year from 2005 to 2016, less than half of the 2.8% annual growth rate sustained from 1995 to 2004… What’s more, real median income has stagnated since the late 1990s and non-economic measures of well-being, like life expectancy, have fallen for some groups.”

After considering four potential explanations, the NBER paper concluded that there’s actually no productivity paradox. Given the proper context, there are no inherent inconsistencies between having both transformative technological advances and lagging productivity. Over the past two centuries we’ve learned that there’s generally a significant time lag between the broad acceptance of new technology-based paradigms and the ensuing economic transformation and institutional recomposition. Even after reaching a tipping point of market acceptance, it takes considerable time, often decades, for the new technologies and business models to be widely embraced by companies and industries across the economy, and only then will their benefits follow, including productivity growth. The paper argues that we’re precisely in such an in-between period.

Let me briefly describe the four potential explanations explored in the paper: false hopes, mismeasurements, concentrated distribution, and implementation and restructuring lags.

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Artificial intelligence and the future of work – Thomas Kochan

MIT Sloan Professor Thomas Kochan

MIT Sloan Professor Thomas Kochan

From InfoTechnology

Artificial intelligence is quickly coming of age and there remain lingering questions about how we will manage this change.

AI will eliminate some jobs, there’s no question, but it will also create some new ones. So the first question we will face as business people, workers and citizens is about balance: are we going to create more jobs than we eliminate or not?

The second and much more fundamental question is: how are we going to proactively manage our AI investments so we can use AI to create new jobs or career opportunities for the future? And how will we make sure those jobs reach out to various sectors of our society increasing our overall wealth and well being and not overly increasing the inequities that already exist in our society.

I believe if we think about it strategically and if we engage more people in the design of AI systems, we’ll be able to make this transition successfully. It will require a proactive strategy. The American public and people all over the world have been shown the negative consequences of not being proactive—take global trade for example. The benefits of global trade have not been widely shared and we are now witnessing the effects of the anger and frustrations this has produced in the movement to more extreme politics and the deeper social divisions laid bare by recent events. We can’t make the same mistake about the future developments of technology.

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The business of artificial intelligence – Erik Brynjolfsson and Andrew McAfee

Professor of Information Technology, Director, The MIT Initiative on the Digital Economy

Director of the MIT Initiative on the Digital Economy, Erik Brynjolfsson

Co-Director of the MIT Initiative on the Digital Economy, Andrew McAfee

From Harvard Business Review

For more than 250 years the fundamental drivers of economic growth have been technological innovations. The most important of these are what economists call general-purpose technologies — a category that includes the steam engine, electricity, and the internal combustion engine. Each one catalyzed waves of complementary innovations and opportunities. The internal combustion engine, for example, gave rise to cars, trucks, airplanes, chain saws, and lawnmowers, along with big-box retailers, shopping centers, cross-docking warehouses, new supply chains, and, when you think about it, suburbs. Companies as diverse as Walmart, UPS, and Uber found ways to leverage the technology to create profitable new business models.

The most important general-purpose technology of our era is artificial intelligence, particularly machine learning (ML) — that is, the machine’s ability to keep improving its performance without humans having to explain exactly how to accomplish all the tasks it’s given. Within just the past few years machine learning has become far more effective and widely available. We can now build systems that learn how to perform tasks on their own.

Why is this such a big deal? Two reasons. First, we humans know more than we can tell: We can’t explain exactly how we’re able to do a lot of things — from recognizing a face to making a smart move in the ancient Asian strategy game of Go. Prior to ML, this inability to articulate our own knowledge meant that we couldn’t automate many tasks. Now we can.

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Artificial intelligence, predictive analytics: How MBA campus recruiting is changing – Jean Ann Schulte

MIT Sloan Director of Employer Relations & Recruiting Services Jean Ann Schulte

From Business Because

Traditionally, recruiting is an on-campus, very structured, time-intensive, and expensive process. Students and employers learn about each other through a series of organized activities like mixers, presentations, coffee chats, treks, interview prep sessions, invite-only dinners, and interviews.

While the largest, most prominent companies continue to host a full schedule of events at their preferred schools, new approaches are emerging.

Employers seek to cut the cost and time required to hire, while increasing the predictability of a new hire’s success. Students have less time and more employment options. Given the rising demand for talent, they expect a more personalized approach and put greater emphasis on cultural fit.

Enter Artificial Intelligence (AI) and predictive analytics. Together, they automate much of the process of sourcing and engaging qualified, interested candidates. It’s a hot field—the number of VC-backed startups focused on the hiring process and employment has increased six-fold in 10 years, with more than 100 companies entering the space in each of the past three years.

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Artificial Intelligence Will Soon Shop For You, But Is That A Good Thing? – Renée Richardson Gosline

MIT Sloan Prof. Renée Richardson Gosline

From WBUR’s Cognoscenti

We’ve all had bad department store shopping experiences. The aggressively cheerful salesperson. The unforgiving glare of the dressing room. The overstuffed racks of garments where none of the sizes fit, and the ones that do, don’t come in your favorite color.

The advent of online shopping has helped consumers gain more control over their shopping experiences. But digital purchases are often a gamble, too. You scroll through endless webpages to find the perfect boots only to discover your size is on back order for two months. And the items you purchase frequently disappoint: The jacket that looked so elegant on the website’s model looks awkward on your frame.

Retail prognosticators claim that artificial intelligence and other new technologies will offer shoppers salvation. In the not-so-distant future, armies of robots using retina recognition software (à la “Minority Report”) will tailor their sales pitches to your preferences and price point. Voice-activated assistants and digital mannequins will help you to find just the right fit. Shopping from home will be a breeze too: Virtual reality headsets will allow you to “try on” clothes and sample items ranging from a tube of lipstick to a tennis racket. Two-day shipping? How antiquated. In the future, your package will arrive via drones in less than two hours. It may sound like science fiction but, in fact, many stores are testing these innovations and have plans to roll them out to customers.

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