Irving Wladawsky-Berger

The MIT Intelligence Quest

How does human intelligence work, in biological as well as in engineering terms? And how can we use such an understanding of human intelligence to build wiser and more useful machines?  On February 1, MIT launched the Intelligence Quest (MIT IQ), - an initiative aimed at addressing these big questions by advancing the science and engineering of both human and machine intelligence.  MIT IQ aims “to discover the foundations of human intelligence and drive the development of technological tools that can positively influence virtually every aspect of society.”

MIT has been deeply involved in artificial intelligence since the field’s inception in the 1950s.  MIT professors  John McCarthy and Marvin Minsky were among the founders and most prominent leaders of the new discipline.  AI was one of the most exciting areas in computer sciences in the 1960s and 1970s.  Many of the AI leaders in those days were convinced that a machine as intelligent as a human being would be developed within a couple of decades.  They were trying to do so by somehow programming the machines to exhibit intelligent behavior, even though to this day we have little idea what intelligence is about, let alone how to translate intelligence into a set of instructions to be executed by a machine.  Eventually, all these early AI approaches met with disappointment and were abandoned in the 1980s.  After years of unfulfilled promises, a so called AI winter of reduced interest and funding set in that nearly killed the field.

AI was reborn in the 1990s when it adopted a more applied, engineering-oriented paradigm.  The new AI paradigm enabled computers to acquire intelligent capabilities by ingesting and analyzing large amounts of data using powerful computers and sophisticated algorithms.  Instead of trying to explicitly program intelligence, this new approach was based on feeding lots and lots of data to the machine, and then letting the algorithms discover patterns and extract insights from all that data.

Such a data-driven, machine learning approach produced something akin to intelligence or knowledge.  Moreover, unlike the explicit programming-based approaches, the statistical-based ones scaled very nicely.  The more data you had, the more powerful the supercomputers, the more sophisticated the algorithms, the better the results.  Machine learning and related advances like deep learning, have played a major role in AI’s recent achievements. 

The Vital Role of Culture in Business

The January-February issue of the Harvard Business Review spotlights The Culture Factor, with five articles on the subject.  “Culture is the tacit social order of an organization: It shapes attitudes and behaviors in wide-ranging and durable ways,” notes the issue’s lead article.  “Cultural norms define what is encouraged, discouraged, accepted, or rejected within a group.  When properly aligned with personal values, drives, and needs, culture can unleash tremendous amounts of energy toward a shared purpose and foster an organization’s capacity to thrive.”

In Who Says Elephants Can't Dance?, - his excellent chronicle of the IBM transformation he led as Chairman and CEO from 1993 to 2002, - Lou Gerstner wrote: “I came to see in my time at IBM that culture isn’t just one aspect of the game - it is the game.  In the end, an organization is nothing more than the collective capacity of its people to create value.  Vision, strategy, marketing, financial management - any management system, in fact - can set you on the right path and can carry you for a while.  But no enterprise - whether in business, government, education, health care or any area of human endeavor - will succeed over the long haul if those elements aren't part of its DNA.” 

The Complex Relationship Between Science and the Public

More than ever, scientific and technological innovations touch every aspect of our lives and influence the choices we make in a wide variety of areas.  A 2015 study by the Pew Research Center revealed that most Americans have a positive view of science and of the accomplishments of scientists.  But, despite broadly similar views about the place of science in the country, there were large differences between the general public and scientist across a number of important issues, including genetically modified foods, vaccine safety and climate change.

Two years ago, the American Academy of Arts and Sciences launched The Public Face of Science, an initiative whose objective is to better understand the complex relationship between scientists and the public in America.  A few weeks ago, the Academy released Perceptions of Science in America, the first in a series of reports from the Public Face of Science initiative.  The report aims to analyze the public’s perception of scientists, explore how opinions vary among different populations, and shed light on how public opinions are shaped.  Its analysis is based on survey data from multiple sources, including the Pew Research Center, the National Science Board Science and Engineering Indicators, the National Opinion Research Center, Research!America and ScienceCounts.

What do we mean by science?  “The term science encompasses a range of disciplines in the physical, social, and life sciences, along with applied fields, such as engineering and medicine,” notes the report. “Science can be defined as ‘the use of evidence to construct testable explanations and predictions of natural, as well as the knowledge generated through this process.’”  But, one must keep in mind that the word science is interpreted differently by different individuals, - e.g., medical treatments, technological advances, fundamental research.  “When interpreting the available survey data, it is important to consider the range of responses a question might prompt.”

Blockchain Killer-Apps

A killer app is an IT application whose value is simple to explain, its use is fairly intuitive, and the application turns out to be so useful that it helps propel the success of a product or service.  Spreadsheets and word processing were major factors in the 1980s adoption of personal computers, for example.  A decade later, e-mail, and the World Wide Web played crucial roles in the mainstream success of the Internet.  While there are a number of strong candidates for smartphone killer-apps, navigation and music are among the most widely used.

Blockchain has been in the news lately, but beyond knowing that it has something to do with payments and digital currencies, most people don’t know what blockchain is or why they should care.  A major part of the reason is that we still don’t have the kind of easy-to-explain blockchain killer-apps that propelled the Internet forward.

Blockchain has yet to cross the chasm from technology enthusiasts and visionaries to the wider marketplace that’s more interested in business value and applications.  There’s considerable research on blockchain technologies, platforms and applications as well as market experimentation in a number of industries - roughly where the Internet was in the mid-late 1980s: full of promise but still confined to a niche audience.

In addition, outside of digital currencies, -a somewhat exotic topic of interest to a relatively small audience, - blockchain applications are primarily aimed at institutions.  And, given that blockchain is all about the creation, exchange and management of valuable assets, its applications are significantly more complex to understand and explain than Internet applications.

The management of information is quite different from the management of transactions.  The latter, especially for transactions dealing with valuable or sensitive assets, requires deep contractual negotiations among companies and jurisdictional negotiations among governments.  Moreover, since blockchain is inherently multi-institutional in nature, its applications involve close collaboration among companies, governments and other entities.