Blockchain’s potential for environmental applications – Irving Wladawsky-Berger

MIT Sloan Visiting Lecturer Irving Wladawsky-Berger

MIT Sloan Visiting Lecturer Irving Wladawsky-Berger

From The Wall Street Journal

The World Economic Forum in mid-September released a report examining how blockchain technologies could be harnessed to address serious environmental issues, better manage our shared global environment and help drive sustainable growth and value creation. The report outlined some of the world’s most-pressing environmental challenges and highlighted eight blockchain-based game changers that could lead to transformative solutions to these pressing problems.

“The majority of the world’s current environmental problems can be traced back to industrialization, particularly since the ‘great acceleration’ in global economic activity since the 1950s,” notes the report. “While this delivered impressive gains in human progress and prosperity, it has also led to unintended consequences… research from many Earth-system scientists suggests that life on land could now be entering a period of unprecedented environmental systems change.”

True, blockchain is still in its early stages of development and deployment. Its capabilities have been often oversold, as is the case with just about all promising technologies. But, as the WEF report argues, if blockchain one days lives up to its promise, it could “transform how society operates, becoming one of the most significant innovations since the creation of the internet. The opportunity to harness this innovation to help tackle environmental challenges is equally significant.”

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Voices in AI – Episode 72: A Conversation with Irving Wladawsky-Berger

MIT Sloan Visiting Lecturer Irving Wladawsky-Berger

MIT Sloan Visiting Lecturer Irving Wladawsky-Berger

From GigaOm

Episode 72 of Voices in AI features host Byron Reese and Irving Wladawsky-Berger discuss the complexity of the human brain, the possibility of AGI and its origins, the implications of AI in weapons, and where else AI has and could take us. Irving has a PhD in Physics from the University of Chicago, is a research affiliate with the MIT Sloan School of Management, he is a guest columnist for the Wall Street Journal and CIO Journal, he is an agent professor of the Imperial College of London, and he is a fellow for the Center for Global Enterprise.

Here is the podcast transcript:

Byron Reese: This is Voices in AI, brought to you by GigaOm, and I’m Byron Reese. Today our guest is Irving Wladawsky-Berger. He is a bunch of things. He is a research affiliate with the MIT Sloan School of Management. He is a guest columnist for the Wall Street Journaland CIO Journal. He is an adjunct professor of the Imperial College of London. He is a fellow for the Center for Global Enterprise, and I think a whole lot more things. Welcome to the show, Irving.

Irving Wladawsky-Berger: Byron it’s a pleasure to be here with you.

So, that’s a lot of things you do. What do you spend most of your time doing?

Well, I spend most of my time these days either in MIT-oriented activities or writing my weekly columns, [which] take quite a bit of time. So, those two are a combination, and then, of course, doing activities like this – talking to you about AI and related topics.

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In an era of tech innovation, whispers of declining research productivity – Irving Wladawsky-Berger

MIT Sloan Visiting Lecturer Irving Wladawsky-Berger

MIT Sloan Visiting Lecturer Irving Wladawsky-Berger

From The Wall Street Journal

Given the pace of technological change, we tend to think of our age as the most innovative ever. But over the past several years, a number of economists have argued that increasing R&D efforts are yielding decreasing returns.

Are Ideas Getting Harder to Find?, a recent paper by economists Nicholas Bloom, Charles Jones and Michael Webb from Stanford and John Van Reenen from MIT, shows that, across a wide range of industries, research efforts are rising substantially while research productivity is declining sharply.

Moore’s Law, the empirical observation that the number of transistors in a computer chip doubles approximately every two years, illustrates these trends. The paper points out that the number of researchers required to double chip density today is 18 times larger than those required in the early 1970s. In the case of Moore’s Law, research productivity has been declining at a rate of about 6.8% per year.

The authors conducted a similar in-depth analysis in the agricultural and pharmaceutical industries. For agricultural yields, research effort went up by a factor of two between 1970 and 2007, while research productivity declined by a factor of 4 over the same period, at an annual rate of 3.7 %. For pharmaceuticals, research efforts went up by a factor of 9 between 1970 and 2014 while research productivity declined by a factor of 5, an annual rate of 3.5%.

 

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Tech innovators open the digital economy to job seekers, financially underserved – Irving Wladawsky-Berger

MIT Sloan Visiting Lecturer Irving Wladawsky-Berger

MIT Sloan Visiting Lecturer Irving Wladawsky-Berger

From The Wall Street Journal

The future of work is a prime interest of the MIT Initiative on the Digital Economy, started in 2013 by researchers Erik Brynjolfsson and Andy McAfee. To help come up with answers to questions about the impact of automation on jobs and the effects of digital innovation, the group launched the MIT Inclusive Innovation Challenge last year, inviting organizations around the world to compete in the realm of improving the economic opportunities of middle- and base-level workers.

 More than $1 million in prizes went to winners of the 2017 competition in Boston last month in four categories: Job creation and income growth, skills development and matching, technology access, and financial inclusion. Awards were funded with support from Google.org, The Joyce Foundation, software firm ISN, and ISN President and CEO Joseph Eastin.

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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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