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MIT CDB Assistant Professor, Renee Gosline, Describes Digital Marketing's Newest Influencers

MIT CDB Video: Sinan Aral on Social Commerce

At the recent MIT CDB conference on Big Data, professor and social networking expert, Sinan Aral, discusses peer influence and how it can impact product marketing strategies and the online economy.

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The same robotic technologies that enable laboratory droids to bake cookies and construct small buildings have the potential to dramatically automate manufacturing processes in the next decade.


Robots are already changing lives and accelerating productivity much as computing did in the last few decades, according to MIT professor and researcher, Daniela Rus. The next wave of exponential growth and advancements in robotic fabrication also will have a huge impact on the digital economy. “We will soon get to an age where it is as easy to have your own robots as it is to print on paper today,” she said at an October IDE seminar.  Rus, Professor of Electrical Engineering and Computer Science as well as Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL), discussed and demonstrated the results of her most recent work.A1-MIT-robots-rus_0.jpg


Based on a 2014 McKinsey report advanced robotics, the Internet of Things and autonomous cars are among the top 12 disruptive technologies with a total potential economic impact between $14 trillion and $33 trillion a year in 2025. (See infographics here). Advanced robotics alone could generate from $1.7 to $4.5 trillion, according to McKinsey. The estimates are  "based on an in-depth analysis of key potential applications and the value they could create in a number of ways, including the consumer surplus that arises from better products, lower prices, a cleaner environment, and better health."


Rus said despite huge progress, the high cost of robotic design and production, as well as limitations in communications and physical dexterity still have to be overcome before bots can reach their full economic potential. Toward that end, her group is building and deploying easily designed robots that can perform complex, multi-step tasks. They can also interact with humans and follow instructions with new levels of precision and accuracy. For example, a team of bots in the lab have built a log cabin by identifying parts and language sequences, then dividing the tasks among themselves. Once at work, the robots analyze instructions and adjust their processes to accommodate their droid co-workers. They communicate with each other and can ask their human partners for specific help if they get stuck with a task as well.


bakebot-2.jpgIn another example, an Iron Chef bot baked cookies by “reading” a series directions, mixing the ingredients and popping the pan into the oven. Executing such seemingly simple tasks required more than a year’s work in computational and lab development at MIT and cost about $500,000 to produce. The group is also testing a series of  simple, self-assembling "origami" style robots. The results of all of these efforts clearly “stretch the boundaries of what robots can do” now and indicate how much more they can achieve in the very near-term, Rus said—especially when commercial developers stake their claim.

“The state of robot production today is similar to where programming was before the invention of compilers,” she said. And with the rapid pace of advancements, the proliferation of low-cost, high function robots is just a blink away.

I had a chance to present my research with Joohee Oh about the value of free goods at the recent Techonomy conference in Tuscon. We now to have the full video and transcript available to read and view. As I wrote about here and as Andy McAfee described, I’m convinced that we are in the midst of a major technological revolution that is not being fully reflected in official government statistics, most notably, the GDP and productivity numbers


According to official government data, there has bee zero growth in the information sector. But we have all seen an explosion of new digital products and services, from Wikipedia and Facebook, to Youtube and GPS mapping. The official data say this sector is the same size as what it was in the 1960s! Now how can that be? Obviously, there’s some major measurement problems in the way we keep our statistics, and that’s a real concern because, as the saying goes, you can’t manage what you don’t measure. So we need to come up with a better way of measuring things.  That’s what we’ve been working at the MIT Center for Digital Business.


Here are a few highlights from my talk:


      • We start with the fact that many digital goods are delivered for free. In some cases, they're funded through advertising. In many other cases, users just contribute their time; they develop content and make it available. And maybe there’s a little bit of contributions or advertising that pays for the bandwidth, but those costs are relatively minimal. We know that there’s just been an explosion of the availability of these goods because you can count the number of bits produced; you can count the number of Wikipedia articles produced, for example--and those have grown ten-fold since 2004.
      • Counting bits is a start, but what we really want to know is not just the number of bits but the actual value of information goods. This is where the bug in the GDP measurement occurs; because GDP measures only the total amount spent on goods and services, not their value. So what happens if the price is zero? Well, zero times any quantity is still zero. So you could have an enormous of explosion of bits or articles or whatever else and the statisticians still see it as a big fat zero contribution for our GDP
      • Traditional metrics are really missing what’s going on in this information economy because so much of the digital economy is a free economy. We found a number of other ways to go about measuring it. One is to look at the time that people spent, and that is something that we do. If you just look at the dollars, you’re going to get a sense that actually the economy is stagnant or even shrinking. But we calculate the demand curve for information goods based on the time spent. This is a very different approach than the traditional GDP accounting, but it’s one that we think better captures the real value that goods and services are producing in the economy.
      • After you do the math and plug in the numbers, we estimate that the annual welfare gain from all these free goods is about $300 billion. Now, that’s the average over the past ten years or so. And that works out to about $1,400 per person.


We also came up with a way to calibrate the value of this time which can you hear more about if you watch the full video. I will continue to write about our findings as the research unfolds.

There are many metrics and data available that quantify the use of digital goods and services. We know, for example, how many billions songs are downloaded and how much revenue that garners. We can tell how many articles there are on Wikipedia and how many hours people spend on Facebook.


To date, there are primarily three ways to quantify the impact digitization is having: We can look at the contribution of the transactions to the GDP; we can look at an IT company’s stock values, or we can track consumer spending and prices as indicators of consumer surplus created.



But none of these approaches measure the real value of digital services when the services are free, according to our latest research at MIT’s Center for Digital Business. For example, a money-only model may be missing 95% of the value consumers derive online. Finding that new metric is the focus of my team’s latest research. Free goods and services on the Internet have exploded in the past decade, and the average American now spends more than 32 hours a month online.


Once they have an Internet connection, they don't spend money to use Wikipedia, Facebook or Youtube, so the value of these services isn't properly reflected in the GDP statistics.

This gap is a problem we have been grappling with for some time, including in a Sloan Management Review article a few years ago.


At the recent annual meeting we offered a possible solution to the measurement problem: Consumers pay with time, not just money, and where they choose to spend their limited time and attention online is a form of voting. Increasingly, the digital economy is the 'attention economy.' Our research is calculating a demand curve for time and estimating how much consumers implicitly value free goods based on use of their time, not on dollars, spent on the Internet.


One preliminary finding shows that free goods added the equivalent of $139 billion in value to the economy in 2010-- more than 1% of the GDP and equal to $647 per person. The findings may have an impact of calculations such as the GDP and other economic metrics.