Irving Wladawsky-Berger

Cryptocurrencies and Blockchains - How Are They Doing After 10 Years?

Bitcoin was created about a decade ago, with the release of the original design paper, Bitcoin: A Peer-to-Peer Electronic Cash System, under the pen name of Satoshi Nakamoto.  Bitcoin aimed to become a decentralized cryptocurrency and digital payment system that would enable the secure exchange of digital money without the need for central government authorities or financial intermediaries.  It introduced the blockchain, - which most everyone agrees is a truly brilliant architecture built on decades-old fundamental research in cryptography, distributed data, distributed computing, game theory and other advanced technologies.

How are they doing after 10 years?  The September 1 issue of The Economist includes a special report on cryptocurrencies and blockchains, with nine articles on the subject. The report’s overview article doesn’t mince words in articulating its overall assessment: “Bitcoin and other cryptocurrencies are useless.  For blockchains, the jury is still out.”

“Bitcoin has been a failure as a means of payment, but thrilling for speculators,” it further added.  Its price has been subject to wild fluctuations over the past two years.  It rose from around $600 in September, 2016, to $6,000 in September, 2017, reaching a peak of over $19,000 in December, 2017, before falling to around $7,000 earlier this year.  It’s been hovering between $6,500 and $7,500 for the past few months.  Other cryptocurrencies have experienced similar price fluctuations.  “That has made a few people very rich (just 100 accounts own 19% of all existing bitcoin), encouraged others to play for quick gains and left some nursing substantial losses.”

Solow Paradox 2.0: The Lagging Impact of Technology Advances on Productivity Growth

Fewer workers is one of the key reasons for our stagnant economic growth.  Over the coming decades, the labor force is expected to shrink in most parts of the world as fertility rates continue to decline, especially in advanced and emerging economies.  The US labor force is growing very slowly while fertility rates keep hitting record lows.

Our aging economies are thus dependent on productivity gains to drive long-term economic growth, future prosperity and higher standards of living.  Which is why few topics have generated as much concern among economists and policy makers as the sharp decline in productivity growth in the US and other advanced economies over the past decade, - despite accelerating technology advances.

“Labor productivity growth is near historic lows in the United States and much of Western Europe,” noted Solving the Productivity Puzzle, a report published earlier this year by the McKinsey Global Institute (MGI).  The report analyzed the productivity-growth declines across a sample of seven countries, - France, Germany, Italy, Spain, Sweden, the United Kingdom, and the United States, - which represent about 65% of the GDP of advanced economies.  Their average productivity growth in the 5 years between 2000 and 2004 was 2.4%.  A decade later, - in the 5 year period between 2010 and 2014, - their average productivity growth had declined to .5%.  While starting to pick up recently, productivity growth remains at or below 1 percent in many of the countries in the study, still quite low relative to historical levels.

Social Physics - Reinventing Analytics to Better Predict Human Behaviors

Social physics first emerged over 200 years ago as an attempt to understand society and human behavior using laws similar to those of the physical sciences.  But, it wasn’t until the past two decades that we finally had enough data, powerful computers and sophisticated mathematical algorithms to develop quantitative theories of human social interactions.  We were now able to reliably predict how large groups of people make decisions by analyzing how information and ideas flow from person to person.

“The engine that drives social physics is big data: the newly ubiquitous digital data now available about all aspects of human life,” wrote MIT professor Alex (Sandy) Pentland in his 2014 book Social Physics: How Good Ideas Spread - The Lessons from a New Science.  “Social physics functions by analyzing patterns of human experience and idea exchange within the digital bread crumbs we all leave behind as we move through the world, - call records, credit card transactions, and GPS location fixes, among others.  These data tell the story of everyday life by recording what each of us has chosen to do… Who we actually are is more accurately determined by where we spend our time and which things we buy, not just by what we say we do.”

The book was the result of over a decade’s worth of research in the Human Dynamics group at MIT’s Media Lab.  Pentland, along with his graduate students and research associates, partnered with a variety of companies to obtain and analyze real-world data, properly anonymized to protect the privacy of the individuals whose behavior was reflected in the data.  They eventually discovered that all event-data representing human activity contain a special set of social activity patterns regardless of what the data is about.  These patterns are common across all human activities and demographics, and can be used to detect emerging behavioral trends before they can be observed by any other technique. 

Deep Learning: Is it Approaching a Wall?

Deep learning and related machine learning advances have played a central role in AI’s recent achievements, giving computers the ability to be trained by ingesting and analyzing large amounts of data instead of being explicitly programmed.  In just the past two years, Google’s deep-learning-based AlphaGo defeated the world’s top Go players, surprising most AI experts who thought that it would take another 5 to 10 years to achieve such a milestone.  Similarly, when Google switched to its new deep learning AI system in late 2016, it achieved an overnight improvement in the quality of its machine translations roughly equal to the total gains that the previous program had accrued over its 10 year lifetime.

As is typically the case with major technology achievements, - e.g., the dot-com bubble, - deep learning has quickly climbed to the top of Gartner’s hype cycle, where all the excitement and publicity accompanying new, promising technologies often leads to inflated expectations, followed by disillusionment if the technology fails to deliver.  AI may be particularly prone to such hype cycles, as the notion of machines achieving or surpassing human levels of intelligence leads to feelings of wonder as well as fear.  Over the past several decades, AI has gone through a few such hype cycles, including the so-called AI winter in the 1980s that nearly killed the field.

In a recent article, Deep Learning: A Critical Appraisal, author and NYU professor Gary Marcus offers a serious assessment of deep learning.  He argues that, despite its considerable achievements over the past 5 years, deep learning may well be approaching a wall, an opinion apparently shared by University of Toronto professor Geoffrey Hinton, the so-called Godfather of Deep Learning.