Las puertas que abre la tecnología del blockchain – Christian Catalini and Cathy Barrera

MIT Sloan Professor Christian Catalini

MIT Sloan Professor Christian Catalini

From La Nacion

Si se otorgaran premios a la terminología comercial más de moda, sin duda la cadena de bloques o blockchain sería candidata. Después de todo, es una de las tecnologías más promocionadas en Silicon Valley y más allá.

Y, sin embargo, pese a tanto revuelo, la promesa y el potencial de la cadena de bloques -tecnología en la que se basan las criptomonedas, como el bitcoin- no se comprenden del todo. Hasta la fecha, solo se hicieron unos pocos estudios sobre el tema y, según una encuesta hecha el año pasado por Deloitte, casi el 40% de los altos ejecutivos afirma tener escaso o ningún conocimiento del modo en que funciona la cadena de bloques.

En un nivel básico, la tecnología de cadena de bloques permite que una red de computadoras llegue, a intervalos regulares, a un consenso sobre el estado verdadero de un registro descentralizado. Ese registro contiene diversos tipos de datos compartidos, como registros de transacciones, atributos de transacciones, credenciales u otra información. Read More »

Imagine If Robo Advisers Could Do Emotions– Andrew Lo

MIT Sloan Professor Andrew Lo

MIT Sloan Professor Andrew Lo

From the Wall Street Journal

At a conference last year, I was approached by an audience member after my talk. He thanked me for my observation that it’s unrealistic to expect investors to do nothing in the face of a sharp market-wide selloff, and that pulling out of the market can sometimes be the right thing to do. In fact, this savvy attendee converted all of his equity holdings to cash by the end of October 2008.

He then asked me for some advice: “Is it safe to get back in now?” Seven years after he moved his money into cash, he’s still waiting for just the right time to reinvest; meanwhile, the S&P 500 earned an annualized return of 14% during this period.

Investing is an emotional process. Managing these emotions is probably the greatest open challenge of financial technology. Investing is much more complicated than other chores like driving, which is why driverless cars are already more successful than even the best robo advisers.

Despite the enthusiasm of tech-savvy millennials—the generation of investors now in their 20s and 30s who are just as happy interacting with an app as with warm-blooded humans—robo advisers don’t take into account the limits of human cognition; they don’t make allowances for emotional reactions like fear and greed; and they can’t eliminate blind spots. Robo advisers don’t do emotion. When the stock market roils, investors freak out. They need comfort and encouragement. During last August’s stock-market rout, Vanguard Group told The Wall Street Journal it was “besieged” with calls from jittery investors and had to pull volunteers from across the company to handle the call volume.

But what if a robo adviser could identify the precise moment you freak out and encourage you not to sell by giving you historical context that calms your nerves? Better yet, what if this digital adviser could actively manage the risk of your portfolio so you don’t freak out at all?

Imagine if, like your car’s cruise control, you can set a level of risk that you’re comfortable with and your robo adviser will apply the brakes when you’re going downhill and step on the gas when you’re going uphill so as to maintain that level of risk. And if you do decide to temporarily take over by stepping on the brakes, the robo adviser will remind you from time to time that you need to step on the gas if you want to reach your destination in the time you’ve allotted. Instead of artificial intelligence, we should first conquer artificial emotion—by constructing algorithms that accurately capture human behavior, we can build countermeasures to protect us from ourselves.

Robo advisers have great potential but the technology is still immature; they’re the rotary phones to today’s iPhone.

Marvin Minsky, the recently deceased founding father of artificial intelligence, summarized the ultimate goal of his field by saying that he didn’t just want to build a computer that he could be proud of, he wanted to build a computer that could be proud of him. Wouldn’t it be grand if we built a robo adviser that could be proud of our portfolio?

See the post at  WSJ “The Experts” 

Andrew W. Lo is the Charles E. and Susan T. Harris Professor at MIT Sloan School of Management, director of the MIT Laboratory for Financial Engineering, principal investigator at MIT Computer Science and Artificial Intelligence Laboratory, and chief investment strategist at AlphaSimplex Group.

 

 

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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Beer’s role in innovation – Joe Hadzima

Joe Hadzima,
MIT Sloan Senior Lecturer

From Huffington Post

Many great—or seemingly great—ideas come to fruition during the course of drinking a beer. When you’re out with the guys (or girls), one or two cold ones could have you rhapsodizing about how you’re going to change the world. This is most likely when self-lowering toilet seats, automatic pet petters, and self-twirling ice cream cones were all dreamed into existence.

As great as these and other inventions are, we’re not sure beer had any role in their creation. But has beer had a role in actual innovation?

Self-driving cars are all the rage in the news lately, with Google and Uber fighting it out over patents and racing to the front of the line for consumer release. While they were focused on cars for the everyday driver, the first self-driving truck delivered 50,000 cans of Budweiser 120 miles in Colorado.

That’s right. The first self-driven truck was used to deliver beer.

Budweiser has come a long way since the days of the horse and cart, right? In the first days of beer delivery, customers only had access because their drink of choice was brought daily by horse and wagon.

You’re probably familiar with the Clydesdales, still often used in Budweiser commercials to tug at heartstrings. These horses were bred by farmers along the banks of the River Clyde in Lanarkshire, Scotland. The Great Flemish Horse was the forerunner of the Clydesdale, which was bred to pull loads of more than one ton at a walking speed of five miles per hour. While that kind of pulling power was amazing during those days, it was still slow and expensive. Each hitch horse needed 20 to 25 quarts of whole grains, minerals and vitamins, 50 to 60 pounds of hay, and 30 gallons of water per day.

Is it any wonder that Anheuser Busch was the exclusive US licensee of the Rudolph Diesel patents? One might assume Ford or the railroad would have been first on board with the development of diesel powered trucks, but it was actually beer.

Knowing how much was needed to keep those magnificent horses healthy and hardy, it seems diesel was a logical next step. This is a classic example of early adopter customers driving a new technology.

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