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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Young doctors struggle to learn robotic surgery, so they are practicing in the shadows – Matt Beane

Matt Beane
Research Affiliate in
Management Science

From The Conversation

Artificial intelligence and robotics spell massive changes to the world of work. These technologies can automate new tasks, and we are making more of them, faster, better and cheaper than ever before.

Surgery was early to the robotics party: Over a third of U.S. hospitals have at least one surgical robot. Such robots have been in widespread use by a growing variety of surgical disciplines, including urology and gynecology, for over a decade. That means the technology has been around for least two generations of surgeons and surgical staff.

I studied robotic surgery for over two years to understand how surgeons are adapting. I observed hundreds of robotic and “traditional” procedures at five hospitals and interviewed surgeons and surgical trainees at another 13 hospitals around the country. I found that robotic surgery disrupted approved approaches surgical training. Only a minority of residents found effective alternatives.

Like the surgeons I studied, we’re all going to have to adapt to AI and robotics. Old hands and new recruits will have to learn new ways to do their jobs, whether in construction, lawyering, retail, finance, warfare or childcare – no one is immune. How will we do this? And what will happen when we try?

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IOT’S people problem – Peter Hirst

MIT Sloan Associate Dean of Executive Education Peter Hirst

MIT Sloan Associate Dean of Executive Education Peter Hirst

From Insight

This spring, I participated in three major IoT-focused events and came away with mixed feelings about the state of the industry. The first was the Internet of Things World in Santa Clara, California. The conference tends to focus on more technological aspects of IoT and draws thousands of attendees. A few days later, I flew to London to take part in the Internet of Things World Forum (IoTWF), an invitation-only event that caters to the C-suite audience. The third was a board meeting and strategy workshop for the Internet of Things Talent Consortium, a spinoff from the IoTWF, of which MIT Sloan Executive Education is a founding member. The three events were highly educational, thought provoking and inspirational in their own right, but all shared a common theme—we, the people, are the main barrier to faster and wider IoT adoption. Moreover, it’s the very nature of humanity, our habits and idiosyncrasies that seem to be stalling the robots’ march toward making our lives wonderfully better—or toward total world domination, depending on how you look at it. More seriously, here are some themes that emerged in my mind once the conference excitement wore off.

Stop Resisting Change
People are creatures of habit. We are comfortable with what we know. It’s not laziness—it’s an evolutionary trick we’ve developed to survive as a species. New is scary and we tend to resist it, willfully or subconsciously, but this resistance can hinder progress. For example, as we heard from the main stage at IoT World Forum in London, GE— who is one of the earliest and relatively successful entrants into IoT—sees organizational inertia as one of the biggest problems in digital transformation. Culture clashes between different kinds of businesses within an organization are dragging down the entire enterprise. Traditional engineers and digital engineers are not speaking the same language. Business leaders are not yet adept in the ways of leading required to drive a digitally-enabled transformation. At IoT World, I was on a panel discussing the future leadership needs around IoT. The panel featured professionals in different industries from education to talent recruitment to defense. And what everybody was saying is that when you look at executives, the need to have agility and resilience is just as great as the need for people who can understand both the technology and the business. In the IoT era, leaders need to have awareness of all sorts of business-environment issues, as well as other important concerns, such as privacy and cyber security, regulation and public policy. Whether it’s strictly IoT or not, you could see that in examples like Uber or Airbnb, as they’ve run into public policy, regulatory and other kinds of situations. I think it’s fairly self-evident that it would be hard for those businesses to succeed without having leaders who are able to take on those kinds of aspects as well.

I’ve touched upon this subject previously, and hearing from companies like GE only reinforced my impression that there is an immediate need to not only train the workers who will work on the IoT implementation, but also to educate leaders on how to lead the transformation. In light of this, the IoT Talent Consortium is redoubling its efforts to help organizations understand and analyze that question. The group sees itself as a community where digital-transformation pioneers and people who believe that they need to or want to go through this kind of journey can share experiences and identify successful practices.

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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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