Irving Wladawsky-Berger

The Public Face of Science - Implications of the Covid Crisis

The Public Face of Science was launched four years ago by the American Academy of Arts and Sciences to better understand the complex relationship between scientists and the American public.  The initiative has now published three reports.

The first, Perceptions of Science in America, was published in 2018.  It found that a majority of Americans have expressed a great deal of confidence in the scientific community, a confidence which has remained stable over the past thirty years.  But, it also found that confidence in science varied based on demographics, including age, race, educational attainment, regional location, political affiliation and other characteristics.  The report recommended additional research to better understand why certain topics were particularly controversial, especially climate change, vaccine safety, and genetically modified foods.

It was followed by Encountering Science in America, which was published in 2019.  This second report explored the diverse and expanding range of opportunities for people to learn about science outside the classroom, including visits to science centers and museums, general news sources, online information, social media, and entertainment.  It concluded that such a complex landscape calls for a multifaceted approach to the public face of science, and recommended additional research to better understand how effective communications and engagements shape the public’s interest in their understanding and support of science.

Priorities for the Future, the third and final report, was published in August of 2020.  While the report doesn’t directly address the implications of Covid-19 to the Public Face of Science initiative, it does so in an accompanying document which highlights the critical role played by science in ensuring the well-being of individuals and society during the pandemic.  “[T]he experience with COVID-19 reinforces the need for continuing thoughtful work to address public access to reliable scientific content and to enhance the public’s capacity to identify and reject misinformation and disinformation (intentionally false information).”

AI Technologies Are Fundamentally Changing How Work Gets Done

After decades of promise and hype, artificial intelligence is finally becoming one of the most important technologies of our era.  AI technologies, like machine learning, are clearly having a major impact on the very nature of work.  But, how can we best quantify their impact on the actual evolution of jobs?

The best approach for exploring the relationship between technology and jobs is to look at the individual tasks that comprise a job.  Most jobs involve a number of tasks.  Some of these tasks are relatively routine and based on well-understood rules, while others require judgement, social skills and other human capabilities.  The more routine and rules-based the task, the more amenable it is to automation. 

However just because some of its component tasks have been automated, does not imply that the whole job has disappeared.  To the contrary, automating the more routine parts of a job will often increase the productivity and quality of workers by complementing their skills with technologies and tools, thus enabling them to focus on those aspect of the job that most need their attention.  While automation  substitutes for labor, automation also complements labor, increasing productivity and other economic outputs in ways that often raise the demand for and earnings of workers.

“The emergence of artificial intelligence (AI) and machine learning (ML) poses a new set of opportunities - and challenges - for work and workers,” said The Future of Work: How New Technologies Are Transforming Tasks, a research report released last Fall by the MIT-IBM Watson AI Lab.  “The tasks that can be done by machine learning are much broader in scope than previous generations of technology have made possible.  The expanded scope will change the value employers place on tasks, and the types of skills most in demand.”

After Years of Promise and Hype, Is AI Once More Failing to Deliver?

The June 13 issue of The Economist included an in-depth look at the limits of AI, with seven articles on the subject.  “There is no question that AI - or, to be precise, machine learning, one of its sub-fields - has made much progress,” notes The Economist in the issue’s overview article.  “Computers have become dramatically better at many things they previously struggled with…  Yet lately doubts have been creeping in about whether today’s AI technology is really as world-changing as it seems.  It is running up against limits of one kind or another, and has failed to deliver on some of its proponents’ more grandiose promises.”

Transformative technologies, - remember the dot-com bubble, - are prone to hype cycles, when all the excitement and publicity accompanying their early achievements often lead to inflated expectations, followed by disillusionment if the technology fails to deliver.  But AI is in a class by itself, as the notion of machines achieving or surpassing human levels of intelligence has led to feelings of both wonder and fear over the past several decades.

The article reminds us that AI has gone through two such major hype cycles since the field began in the mid-1950s.  Early achievements, - like beating humans at checkers and proving logic theorems, - led researchers to conclude that machines would achieve human-level intelligence within a couple of decades.  This early optimism collapsed leading to the first so-called AI winter from 1974-1980.  The field was revived in the 1980s with the advent of commercial expert systems and Japan’s Fifth Generation project, but it didn’t last long, leading to the second AI winter from 1987-1993.

May the Network Force Be With You

May the Force Be With You has become part of popular culture since the words were first uttered in the 1977 Star Wars film.  As we know, The Force is a source of power for those who, like the Jedi, feel its flow.  “The network force is similar,” wrote Silicon Valley network expert and entrepreneur James Currier in Your Life is Driven by Network Effects.  “You don’t always see it, but it is exerting itself on you.  It wants something from you. Your network force proactively guides you down a path.”

“When you start to see that dialogue between you and your network, the push-pull, you see it everywhere.  The chaos of the world diminishes a bit and becomes more understandable and predictable. And you understand more why things are the way they are and why they stay that way.  Hopefully, it will also give you insights as to where you can push to change things that should be changed, not just about you, but about your company, your city, and your world.”

Over the past few decades, network forces have been influencing many aspects of business, the economy, and our social interactions.  The Internet’s universal reach and connectivity have led to increasingly powerful network effects, such as, for example, platform economies.

The Social Network Is the Computer

“Human beings have always been a social species,” writes MIT professor Sinan Aral in the opening paragraph of The Hype Machine, his recently published book.  “We’ve been communicating, cooperating, and coordinating with one another since we were hunting and gathering.”  Our increasingly complex social interactions have been the critical factor in the exponential increase of human cranial capacity over the past few million years.  “But today something is different.  Over the last decade, we’ve doused our kindling fire of human interaction with high-­octane gasoline.  We’ve constructed an expansive, multifaceted machine that spans the globe and conducts the flow of information, opinions, and behaviors through society.”

Professor Aral has been studying social media since its beginning in the early 2000s, when it was driven by the idealistic vision of connecting the world and providing free access to information.  The Hype Machine is what he now calls our global social media ecosystem.  This ecosystem has been designed to stimulate and manipulate the human psyche, “to draw us in and persuade us to change how we shop, vote, and exercise, and even who we love.”  His book nicely explains how the social media industrial complex works, how it affects us, and what we can do to help achieve its original vision while avoiding its later perils.  Let me discuss a few of its key points.

The AI Factory: A New Kind of Digital Operating Model

“Whether you’re leading a digital start-up or working to revamp a traditional enterprise, it’s essential to understand the revolutionary impact AI has on operations, strategy, and competition,” wrote Harvard professors Marco Iansiti and Karim Lakhani in “Competing in the Age of AI”, a recently published article in the Harvard Business Review (HBR).  Earlier this year, they also published a book of the same title, which expands on the ideas in the article and illustrates them with a number of concrete use cases.

The age of AI is being ushered by the emergence of a new kind of digital firm.  Rather than just relying on traditional business processes operated by its workers, these firms are leveraging software and data-driven algorithms to eliminate traditional constraints and transform the rules of competition.  Managers and engineers are responsible for the design of the new AI-based operational systems, but the system then runs the operations pretty much on its own.

“At the core of the new firm is a decision factory - what we call the AI factory,” note the authors.  “[T]he AI factory treats decision-making as a science.  Analytics systematically convert internal and external data into predictions, insights, and choices, which in turn guide and automate operational workflows…  As digital networks and algorithms are woven into the fabric of firms, industries begin to function differently and the lines between them blur.”

The Industrial Revolution transformed the economy by developing a scalable, repeatable approach to manufacturing.  The AI factory is now driving another fundamental transformation by industrializing the data gathering, decision making, and overall digital operations of 21st century firms.

Reconnecting Society and Reopening the Economy

A few months ago, MIT Connection Science held it’s annual Sponsors’ Meeting online.  The virtual meeting included a number of very interesting presentations.  Given its continuing importance, I want to focus my discussion on “Reconnecting Society After a Pandemic,” a talk given by visiting professor Esteban Moro. 

Moro started his presentation by showing a slide from this article.  The slide illustrates that if we had done nothing, or just relied on soft mitigation and herd immunity to contain the spread of the Covid-19 virus, the number of cases and deaths would have gone up very rapidly, - overwhelming healthcare systems around the world.  Instead, many national and state governments adopted two distinct sets of actions, the hammer and the dance.

The hammer is the somewhat draconian social distancing policies aimed at containing the spread of the virus, especially in regions like the New York metro area where the number of cases were escalating at a very rapid pace.  The dance starts once the spread of the virus has been brought under reasonable control, - albeit at the price of large economic costs and societal disruption.  The dance is the longer term effort to keep the virus contained until there’s an effective vaccine while reopening the economy and bringing life as close to normal as possible.

The Transformational Impact of Blockchain-based Supply Chains: Nine Real-World Use Cases

In May 2016, Don Tapscott published Blockchain Revolution, his 16th book, this one co-written with son Alex.  Don, whom I’ve known for over 25 years, has an uncanny knack for identifying The Next Big Thing and explaining it to everybody else through his books, articles, and speeches.  Don and Alex have each developed a follow-on blockchain book.  Earlier this year, Alex published Financial Services Revolution, while a few weeks ago Don published Supply Chain Revolution, which I will now discuss.

Supply Chain Revolution consists of nine chapters, edited with a foreword by Don.  He opens his foreword by noting, “When we first started working on this book, the coronavirus (COVID-19) hadn’t yet made headlines…  As I’ve become fond of saying, ‘If you’ve got it, a supply chain brought it to you.’  I suppose that, during these times, I could add, ‘If you can’t get it, a supply chain let you down.’”

Each chapter details how blockchain is revolutionizing a different supply chain, from general use cases like global trade to more specific ones like food traceability and manufacturing.  Each was written by some of the world’s top experts on these blockchain applications.

For a few years now, I have argued that supply chain applications will likely emerge as the earliest blockchain killer apps.  Given that blockchain first came to light in 2008 as the architecture underpinning bitcoin and other cryptocurrencies, many thought that its killer-apps would be in the financial services industry.  That might well be the case in the future, but given its underlying complexity, transforming the global financial ecosystem will take considerable time and investment.  Supply chain infrastructures and processes are significantly less complex than those in financial services, healthcare, and other industries, which is why several supply chain use cases have already been brought to market over the past few years.

The Long-Term Impact of Early-Career Setbacks

Fail fast, fail often is one of Silicon Valley’s best known mantras.  Is there something to this belief that if you’re not failing fast and often you may be stuck in a stagnating endeavor?  After all, social scientists have their own Matthew Effect, - i.e., the rich get richer and the famous get more famous, - as evidenced in our age of rising economic inequality and superstar dynamics.

In early March, - just before Covid-19 brought normal life to a screeching halt, - I attended a very interesting seminar by Northwestern professor Dashun Wang.  Over the past several years, professor Wang, along with various collaborators, has been investigating the impact of early-career setbacks on the future careers of scientists.

“Setbacks are an integral part of a scientific career, yet little is known about their long-term effects,” notes a recent article he co-authored in Nature Communications.  “Despite the ubiquitous nature of failures, it remains unclear if a setback in an early career may augment or hamper an individual’s future career impact.”  The Matthew Effect would suggest that early-career success is an important determinant of a successful scientific career.  On the other hand, early setbacks could well be a marker for future achievement, teaching young scientists, entrepreneurs, artists, etc, valuable lessons that they wouldn’t have otherwise learned, i.e., what doesn’t kill you makes you stronger.  Which is the most likely scenario?

Could AI Reshape the Global Balance of Power?

The National Security Commission on Artificial Intelligence (NSCAI) was established by the US Congress in Fiscal Year 2019 to “advance the development of artificial intelligence, machine learning, and associated technologies by the United States to comprehensively address the national security and defense needs of the United States.”  A few months ago, the 15 member Commission, - led by former Google CEO Eric Schmidt  and former deputy secretary of defense Robert Work, - released its interim report. The final report is due to be released in March, 2021.  It remains to be seen how the Covid-19 pandemic will impact the final report’s recommendations and release date.

“The convergence of the artificial intelligence revolution and the reemergence of great power competition… threaten the United States’ role as the world’s engine of innovation and American military superiority,” said the interim report in its opening statements.  “How the United States adopts AI will have profound ramifications for our immediate security, economic well-being, and position in the world…  We are concerned that America’s role as the world’s leading innovator is threatened… We know strategic competitors are investing in research and application.  It is only reasonable to conclude that AI-enabled capabilities could be used to threaten our critical infrastructure, amplify disinformation campaigns, and wage war.”

Like the Internet, - let’s remember that ARPANET was started in the late 1960s by the US Department of Defense, - AI is usable for military and civilian purposes.  But, - also like the Internet, - AI is a general purpose technology that can be used for a wide variety of applications, many of them beneficial, but others potentially threatening.

Scenario Planning - Formulating Strategy in the Face of High Uncertainty

“How can we formulate strategy in the face of uncertainty?,” asks Learning from the Future, a recent Harvard Business Review article by Peter Scoblic, in its opening sentence.  Answering this question has never felt more urgent as leaders prepare for the future.  “Even before the Covid-19 crisis, rapid technological change, growing economic interdependence, and mounting political instability had conspired to make the future increasingly murky.”  Uncertainty was already all-encompassing.

“And then the pandemic hit.”

When the present resembles the past, we can calculate the probability of future outcomes by analyzing past historical data.  That’s how insurance companies accurately predict the life expectancy of different people based on age, gender, general health, behavioral traits like smoking and drinking, etc.  But the situation is very different when the present doesn’t resemble the past.  “Uncertainty stems from our inability to compare the present to anything we’ve previously experienced.  When situations lack analogies to the past, we have trouble envisioning how they will play out in the future.”

Military strategists first developed scenario planning in the 1950s to help them envision the impact of a nuclear war between the US and the Soviet Union, - something that was clearly way beyond anyone’s experience.  The object of scenario planning is to assemble all known facts, trends, and other information and use them to create a number of alternative stories rich in detail, typically three or four, about plausible future outcomes along with the development paths and key events that led to such outcomes.  Altogether, the scenarios should span the scope of possible futures, although the actual future will likely include events from each scenario.

The Post-Pandemic Enterprise: What Will It Be Like?

In the near term, nations have been mostly focused on surviving the massive disruptions caused by Covid-19.  But, what about the longer term?  What will the post-pandemic new normal be like for societies and economies?  More specifically, how will the pandemic impact the world of business?  How can enterprises leverage the crisis to accelerate their journey toward what most everyone agrees will be an even faster changing future?  As Rahm Emanuel famously said in a 2008 WSJ interview: “You never want a serious crisis to go to waste.”

“The future is not what we thought it would be only a few short months ago,” said a recent McKinsey article, From thinking about the next normal to making it work.  “As businesses step into the post-coronavirus future, they need to find a balance between what worked before and what needs to happen to succeed in the next normal.”  The article offers 21 concrete recommendations for succeeding in the next normal, organized into seven major areas.  Let me summarize a few of these recommendations.

Building a More Resilient, Data-Driven Economy

“With each major crisis, be it war, pandemic, or major new technology, there has been a need to reinvent the relationships between individuals, businesses, and government,” wrote MIT professor Alex “Sandy Pentland in the first chapter of Building the New Economy.  Pentland cites two major historical examples.  In the early 20th century, the rise of mass manufacturing led to the creation of regulations governing working conditions and pay, health rules for mass-produced foods, and laws to prevent monopolies that stifled competition.  Then in mid-century, the decades following WWII saw greater access to higher education, racial and gender progress, and increased support for scientific research and medical advances.

“Today we face two simultaneous disruptions: one is the COVID-19 pandemic and resulting economic shock, and the second is the rise of pervasive digital data, crypto systems, and artificial intelligence (AI).”  The two disruptions are highly interrelated.  Digital technologies have been deployed to fight the global pandemic as well as helping us stay connected as we practice social distancing.

But, these same technologies are also increasing the already high levels of disinformation and fraud, threatening privacy rights with well-meaning public health technology measures, and likely to impact employment as institutions accelerate their embrace of digitization, AI and automation.  “These problems highlight the need to reinvent the ways data and AI are used in all of societies’ civic and government systems, both to guarantee that future pandemics can be handled better and to reinvigorate the economy but also to spread the economic benefits throughout society.”

High resilience in the face of an uncertain, changing environment is the essence of evolution and natural selection.  Building a new post-pandemic economy requires the creation of more agile, resilient systems and institutions to help us better adapt to an unpredictable future and withstand turbulent events like Covid-19.  In a resilient economy, power and decision-making should be distributed among a diverse set of stakeholders rather than concentrated in a few hands.

An Increasingly Digital “New Normal”

About a dozen years ago, I attended a conference where I was interviewed on stage by then WSJ technology columnist Walter Mossberg.  He started out by asking me about IBM’s near-death experience in the early 1990s, and the subsequent technical and cultural transformation that made it possible for IBM to reinvent itself and survive, while many other IT companies didn’t make it.  In particular, we discussed whether a successful company transformation must be preceded by the kind of major crisis IBM went through.  When it comes to humans, the ancient Greeks certainly thought so, as reflected in their tragedies.

Transformative change is very difficult for established companies.  Over the years, they’ve amassed valuable assets and extensive organizations.  Already consumed with managing their existing operations, they may look at transformative change as more of a threat or distraction than an opportunity, - unless they’re in dire straits and have no other choice, as was the case with IBM.

A serious crisis is an opportunity for companies to focus on the actions needed to survive in a fast changing environment.  As Rahm Emanuel famously said in a 2008 interview with the WSJ: “You never want a serious crisis to go to waste.”  In that spirit, how should companies take advantage of the Covid-19 crisis, - arguably the biggest shock the world has experienced since WWII, - to accelerate their journey toward what most everyone agrees will be an increasingly digital future?

Blockchain and Public Health Solutions

The Blockchain Research Institute (BRI) is a global think tank dedicated to the strategic implications of blockchain technologies to business, government and society.  On March 26, I participated in a virtual roundtable convened by the BRI to discuss the potential use of blockchain technologies for public health solutions.  The roundtable’s findings and recommendations were released in early April in Blockchain Solutions in Pandemics: A Call for Innovation and Transformation in Public Health.

The report identified five key areas where blockchain could be deployed to fight Covid-19 as well as future pandemics: identity, health records and shared data; just-in-time supply chains; sustaining the economy; a rapid response registry for medical professionals; and incentives models to reward responsible behavior.  In each of these areas, the report presented uses cases where blockchain is already being deployed, and recommended a number of blockchain related public health measures that will help better prepare for future pandemics.  Given the broad scope of the report, I will focus my attention on one area in particular: identity, health records and shared data.

Measuring the Effectiveness of Social Distancing Policies

One of the most interesting projects in the MIT Connection Science group I’m affiliated with is The Atlas of Inequality.  The project, - led by visiting professor Esteban Moro and professor Alex ‘Sandy’ Pentland - uses aggregated anonymous geolocation data from digital devices to estimate where different groups of people in US cities spend their time.  Over time, the project will be expanded to cities around the world.  The data show the significant income inequality among people in those cities, not just by neighborhoods as you’d expect, but also in the restaurants, stores and other places beyond their neighborhoods which they visit every day.

A couple of weeks ago, the Atlas research group started to apply their geolocation data and methods to analyze the effectiveness of the social distancing policies adopted in the New York metropolitan area in response to the Covid-19 pandemic.  These policies include school closures, bans on non-essential gatherings, limiting restaurants to take-out orders, and strict stay-in-place measures.   There’s no way to empirically measure the impact of these social distancing measures in real time on the spread of Covid-19.  You can only measure their impact retrospectively, or simulate what might happen in the future based on past data.

However, you can empirically address a key set of important question: how have these social distancing policies changed mobility and social behavior?; how does social distancing behavior vary across the New York metropolitan area?; how does the behavior vary across the variety of demographic groups in the NY metro area?; and overall, how well are people following these social distance measures?

The 2019 Artificial Intelligence Index

AI has emerged as the defining technology of our era, as transformative over time as the steam engine, electricity, and the Internet.  While still in its early stages, AI is already reshaping the economy, society, our personal lives and the very nature of work, with much more to come as the technology and applications mature, including the scientific research necessary to anticipate and fight future pandemics.  But, what’s the current state of AI and where is it heading?

To help address these important questions, the annual Artificial Intelligence Index was created under the auspices of the Stanford Institute for Human-Centered Artificial Intelligence, - guided by a Steering Committee of experts from academia and industry, and in collaboration with a number of sponsoring partners and data contributors.  The AI Index aims to “provide unbiased, rigorous, and comprehensive data for policymakers, researchers, journalists, executives, and the general public to develop a deeper understanding of the complex field of AI.”

Now in its third year, the 2019 AI Index Report was released this past December.  So was the associated 2019 AI Index Data, which includes three times as many data sets as the 2018 edition.  The report  comprises nine chapters.  Let me summarize a few of its highlights.

The Coming Era of Decision Machines

“Artificial intelligence (AI) is the pursuit of machines that are able to act purposefully to make decisions towards the pursuit of goals,” wrote Harvard professor David Parkes in A Responsibility to Judge Carefully in the Era of Prediction Decision Machines, an essay recently published as part of Harvard’s Digital Initiative.  “Machines need to be able to predict to decide, but decision making requires much more.  Decision making requires bringing together and reconciling multiple points of view.  Decision making requires leadership in advocating and explaining a path forward.  Decision making requires dialogue.”

In April, 2017 I attended a seminar by University of Toronto professor Avi Goldfarb on the economic value of AI.  Goldfarb explained that the best way to assess the impact of a new radical technology is to look at how the technology reduces the cost of a widely used function.  For example, computers are essentially powerful calculators whose cost of digital operations have dramatically decreased over the past several decades.  Over the years, we’ve learned to define all kinds of tasks in terms of digital operations, e.g., financial transactions, word processing, photography.  Similarly, the Internet has drastically reduced the cost of communications and of access to all kinds of information, - including text, pictures, music and videos.

Viewed through this lens, the AI revolution can be viewed as reducing the cost of predictions.  Prediction means anticipating what is likely to happen in the future.  Over the past decade, increasingly powerful and inexpensive computers, advanced machine learning algorithms, and the explosive growth of big data have enabled us to extract insights from all that data and turn them into valuable predictions.

Given the widespread role of predictions in business, government and everyday life, AI is already having a major impact on many human activities.  As was previously the case with arithmetic, communications and access to information, - we will be able to use predictions in all kinds of new applications.  Over time, we’ll discover that lots of tasks can be reframed as prediction problems.

Can Democracy and Free Markets Survive in the Coming Age of AI?

Can technology plan economies and destroy democracy? is the provocative title of an essay in the end-of-2019 issue of The Economist.  “The success of market- and semi-market-based economies… has made the notion of a planned economy seem like a thing of the past.”  But, given all the data that can now be gathered by billions of smartphones and 10s of billions of sensors, - with many more to come, - could our increasingly intelligent and powerful AI systems “replace the autonomous choices on which the market is based?… And if technology can outperform the invisible hand in the economy, might it be able to do the same at the ballot box when it comes to politics?” 

As we’ve already seen, AI applications “can provide not just data on what people want and will tolerate, but also the means to manipulate those desires.  When such means are available to actors within or outside a state the struggle to gain, or for that matter retain, a democracy which reflects a genuine popular will might become even harder than it is.”  Could this all mean that free-market economics and liberal democracy will become obsolete over the coming century?

These questions were often raised and repeatedly tested throughout the 20th century, as various governments attempted to redesign their economies and societies in accord with what were believed to be scientific laws.  But, most such schemes, especially those carried out by authoritarian states, ended up as complete failures.  And some went tragically awry, - like Stalin’s collectivization of agriculture in the Soviet Union and Mao’s Great Leap Forward in China, - both of which brought death and disruption to millions.

The State of AI Adoption - High Performers Show the Way

For the past few years, the McKinsey Global Institute has been conducting a yearly survey to assess the state of AI adoption.  Its 2017 survey of over 3,000 AI-aware executive found that outside the technology sector, AI adoption was at an early, often experimental stage.  Only 20% of respondents used any AI-related technology in a core part to their business.  A common theme throughout the report was that the same players who were leaders in the earlier waves of digitization and analytics were the early leaders in the AI wave.

The McKinsey 2018 survey garnered responses from over 2,000 participants from a wide range of company sizes across 10 industry sectors.  Overall, the 2018 survey found that while the business world had begun to adopt AI, few companies had in place the foundational building blocks that would help them to generate value from AI at scale.  30% were conducting AI pilots.  Nearly half, 47%, had embedded at least one AI capability in their standard business processes, but only 21% reported using AI across multiple business functions.  AI investments were still quite small, with almost 60% of respondents saying that less that one-tenth of their digital budgets went toward AI.

The most recent Global AI Survey was published in November of 2019.  McKinsey garnered responses from over 2,300 participants, nearly 80% of whose companies were using AI in some capacity.  Overall,  the survey showed that “Most companies report measurable benefits from AI where it has been deployed; however, much work remains to scale impact, manage risks, and retrain the workforce.”  AI is becoming more mainstream, with 58% of respondents using AI in at least one function, - a nearly 25% increase over the previous year; and 30% reported using AI across multiple business units, - a nearly 50% increase.

One of the survey’s major findings is that a small share of high performers, around 3% of companies using AI, are already achieving outsize business results.   High performing companies are much further along in scaling the use of AI in their organizations, with an average of 11 different use cases compared an average of 3 among the other companies.

Conceptualizing AI in Human Terms is Misleading and Potentially Harmful

“We speak of machines that think, learn, and infer.  The name of the discipline itself - artificial intelligence - practically dares us to compare our human modes of reasoning with the behavior of algorithms,” writes Oxford doctoral candidate David Watson in his recently published article The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence.  “Despite the temptation to fall back on anthropomorphic tropes when discussing AI, however, I conclude that such rhetoric is at best misleading and at worst downright dangerous.  The impulse to humanize algorithms is an obstacle to properly conceptualizing the ethical challenges posed by emerging technologies.”

What do we mean by intelligence?  In 1994 the Wall Street Journal published a definition which reflected the consensus of 52 leading academic researchers in fields associated with intelligence: “Intelligence is a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience.  It is not merely book learning, a narrow academic skill, or test-taking smarts.  Rather, it reflects a broader and deeper capability for comprehending our surroundings - ‘catching on,’ ‘making sense’ of things, or ‘figuring out’ what to do.”  This is a very good definition of general intelligence, - the kind of intelligence that’s long been measured in IQ tests, and that, for the foreseeable future, only humans have.

On the other hand, specialized intelligence, - the ability to effectively address well-defined, specific goals in a given environment, - is the kind of task-oriented intelligence that’s part of many human jobs.  In the past several years, our increasingly smart machines have become quite proficient at handling a variety of such specialized intelligent tasks.  AI technologies are approaching or surpassing human levels of performance in vision, speech recognition, language translation, the early detection and diagnosis of various forms of cancer, and other capabilities that were once viewed as the exclusive domain of humans.

Why Some AI Efforts Succeed While Many Fail

Winning with AI, - a 2019 report based on a survey jointly conducted by the MIT Sloan Management Review and the Boston Consulting Group, - found that 90% of respondents agree that AI represents a business opportunity for their company.  The global survey attracted over 2,500 respondents from 29 industries and 97 countries, and conducted interviews with 17 executives leaders of AI initiatives in large organizations.

The report classified the total survey population into four subgroups based on their understanding of AI tools and concepts and their levels of adoption of AI applications: Pioneers (20%) are leading-edge organizations that both understand and have widely adopted AI; Investigators (30%) understand AI but have not deployed applications beyond the pilot stage; Experimenters (18%) are learning by doing, conducting pilots without a deep understanding of AI; and Passives (32%) have not adopted AI and have little understanding of the technology.

“Many AI initiatives fail,” was the report’s overriding finding.  “Seven out of 10 companies surveyed report minimal or no impact from AI so far.  Among the 90% of companies that have made at least some investment in AI, fewer than 2 out of 5 report obtaining any business gains from AI in the past three years. This number improves to 3 out of 5 when we include companies that have made significant investments in AI.  Even so, this means 40% of organizations making significant investments do not report business gains from AI.”

Innovation and National Security in the 21st Century

“Countries that can harness the current wave of innovation, mitigate its potential disruptions, and capitalize on its transformative power will gain economic and military advantages over potential rivals,” was the top finding of the Innovation and National Security Task Force which was commissioned by the Council of Foreign Relations (CFR) to assess the current state of US technological innovation.  The Task Force noted that leadership in innovation, research and technology since World War II has made the US the most secure and economically prosperous nation on earth.

“Today, this leadership position is at risk,” the Task Force warned.  Federal support and funding for R&D has stagnated over the past two decades.  “Washington has failed to maintain adequate levels of public support and funding for basic science.  Federal investment in R&D as a percentage of GDP peaked at 1.86 percent in 1964 but has declined from a little over 1 percent in 1990 to 0.66 percent in 2016.”

The US has successfully responded to technological competition in the past.  Sputnik was a tipping point in the space race with the Soviet Union during the cold war, leading to, among other things, a significant increase in the number of graduate student fellowships in STEM disciplines, of which I was personally a beneficiary.  Another prominent example is the strong economic competition from Japan in the 1980s, which the US fended off a decade later with our leadership in the Internet and other major digital innovations.

The Internet of Things is Changing the World

“How the world will change as computers spread into everyday objects,” is the title of the lead article in a comprehensive review of the Internet of Things (IoT) in a recent issue of The Economist.  Like many technological advances, IoT has been long in coming.  Ubiquitous or Pervasive Computing dates back to the 1990s, when neither the necessary low-cost devices and wireless networks were anywhere near ready.  “But the transformation is about to go into overdrive, notes The Economist.  “One forecast is that by 2035 the world will have a trillion connected computers, built into everything from food packaging to bridges and clothes.”

IoT promises to bring many benefits, including a whole new generation of innovative products.  We’ve long thought of products as physical entities built with a combination of mechanical and electrical components, - e.g., appliances, cars, agricultural machines, industrial equipment, - some quite simple and some highly complex.  IoT has been ushering a whole new class of smart connected products.  In addition to mechanical and electrical components, these new products use digital components like microprocessors, sensors, data storage, software, and connectivity in a wide variety of ways.

As the world’s digital and physical infrastructures converge, digital technologies are being designed right into all kinds of consumer and industrial products.  Internet-connected smart doorbells, for example, include motion sensors and video cameras that notify a home owner when someone arrives at the door.  Using a smartphone app, the home owner can watch and talk to the visitor, while a video of their interaction is saved for an added level of security.

“The magic of computers is that they provide in a machine an ability - to calculate, to process information, to decide - that used to be the sole preserve of biological brains,” writes The Economist.  “The IoT foresees a world in which this magic becomes ubiquitous.  Countless tiny chips will be woven into buildings, cities, clothes and human bodies, all linked by the internet… Over the past century electricity has allowed consumers and businesses at least in the rich world, access to a fundamental, universally useful good - energy - when and where they needed it.  The IoT aims to do for information what electricity did for energy.”

The State of Financial Health Around the World

The Gallup Global Financial Health Study was published in May of 2018.  The study is based on a survey of over 15,000 people in 10 different countries.  Conducted by Gallup in partnership with the MetLife Foundation and the Rockefeller Philanthropy Advisors, the study aimed to develop two key financial measures: how secure do people feel around the world, and how much control and influence they believe they have over their financial situations.

“The degree to which people perceive they are in control of and can influence their financial situation is an important and complementary dimension under the umbrella of financial health,” said the study.  While researchers have developed valuable measures of financial inclusion and financial literacy, data on financial security and control have never existed before.  Such financial measures should help organizations and countries around the world make evidence-based decisions on how to improve the ability of people to lead more financially healthy and secure lives.

The countries in the survey spanned the gamut from highly advanced to developing economies.  The ten countries, and their approximate GDP per capita in $1000s, are: the US ($58), UK ($43), Japan ($42), South Korea ($37), Greece ($37), Chile ($27), Colombia ($24), Vietnam ($14), Bangladesh ($6.3), and Kenya ($3.6).

The study defines financial control as “the extent to which people perceive they are in control of and can influence their financial situation - an important and complementary dimension within the umbrella of financial health.”  Financial control is composed of four key variables: locus of control -  having power over events in one’s life; resilience - ability to come up with solutions to small financial shocks; self-efficacy - extent of influence over future outcomes; and autonomy - having decision-making power.

Can AI Help Develop and Execute a Competitive Business Strategy?

Strategy For and With AI, a recent article in the MIT Sloan Management Review by David Kiron and Michael Schrage, argues that while formulating a comprehensive strategy for the use of AI technologies is absolutely necessary, it’s not sufficient.  “Creating strategy with AI matters as much - or even more - in terms of exploring and exploiting strategic opportunity.”  The article is based on a survey of over 3,000 executives, managers and analysts from companies in over 100 countries and 20 industries, as well as on interviews with executives and academics.

AI is expected to be the biggest commercial opportunity for companies and industries over the next couple of decades.  Two 2018 reports, one by PwC and one by McKinsey, estimated that AI has the potential to incrementally add around $13-$15 trillion to global economic output by 2030.  The growth will come from productivity gains, - e,g, the continuing automation of routine tasks, and AI-based tools to augment human capabilities, - and from increasingly sophisticated, AI-enhanced products, services, and system-wide applications.

Machine learning advances, like deep learning, 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.  Machine learning methods for solving problems, - subtle adjustments to the numerical weights that interconnect huge number of artificial neurons, - are radically different and complement the way humans solve problems.  But, while being a very powerful tool, machine learning, and AI in general, lack all-important human qualities like common sense and empathy, so their decisions and suggestions should be carefully reviewed by humans.  People and technology must closely collaborate, with each playing the particular role they are best at.

Every day we can read about the latest AI advances from research labs, startups and large companies.  AI technologies are approaching or surpassing human levels of performance in vision, speech recognition, language translation, playing championship-level Go, and the early detection and diagnosis of various forms of cancer.  But, can AI help address broad, open-ended and ambiguous problems like developing and executing a competitive business strategy?

The Increasing Demand for Hybrid, “T-Shaped” Workers

A recent article in The Atlantic used the USS Gabrielle Giffords to illustrate the important changes taking place in the US Navy, - and in the world of work in general.  After discussing various features of its advanced design, the article noted that the ship’s most futuristic aspect is its crew.  “It was designed to operate with a mere 40 souls on board - one-fifth the number aboard comparably sized legacy ships and a far cry from the 350 aboard a World War II destroyer.  The small size of the crew means that each sailor must be like the ship itself: a jack of many trades and not, as 240 years of tradition have prescribed, a master of just one,” noted the article.  Due to increasingly sophisticated technologies and the high cost of personnel, the USS Gabrielle Giffords is among a new class of Navy ships that’s turned away from specialists in favor of so-called hybrid sailors who are able to handle multiple tasks and rapidly acquire new skills.

The replacement of highly specialized workers with problem-solving generalists who are able to handle and quickly learn new tasks has been taking place across a wide variety of  occupations and industries.  Increased productivity and lower costs, i.e., doing more with less, are some of the reasons for this change.  But, the increased complexity of products, applications and systems, and the unanticipated problems that often follow are other important reasons.

The concept of hybrid or T-shaped workers was first introduced around 25 years ago as a metaphor to describe the kind of individuals that the Navy and many other organizations are looking for.  The vertical stroke in the letter T represents a depth of skills and expertise in one or more specific fields; the horizontal bar implies broad multidisciplinary and social skills, as well as the ability to collaborate with experts across disciplines to jointly solve complex problems.

Impact of AI on Government - Opportunities and Challenges

A few weeks ago I wrote about Leveraging AI to Transform Government based on a 2018 report by the Partnership for Public Service and the IBM Center for the Business of Government.  The report illustrates how government is using AI by describing four use cases being pursued by different agencies.  In 2019, the Partnership and IBM collaborated on two additional reports, Assessing the Impact of AI on the Work of Government, and Building Trust, Managing Risks.

This first report is focused on the impact of AI on the federal workforce, based on an analysis of the jobs and tasks most susceptible to automation, as well as on roundtables and interviews with over 40 AI experts.  The overriding conclusion is that, over time, AI will have a deep impact on how government works.  More than 80 different federal occupations, employing over 130,000 workers, are likely be significantly transformed, including 30,000 in the Treasury Department, 16,000 in Defense, and 5,000 in Transportation, as well as a variety of occupations in independent agencies like the Securities and Exchange Commission and the Government Publishing Office.

In particular, AI will have a major impact in three key areas: the transformation of the federal workday, the potential for personalized customer service and the increased importance of technical and data skills.  Let me summarize the findings and recommendations in each of these areas.

Leveraging AI to Transform Government

“AI has the potential to help address many of our country’s pervasive problems and advance our safety, health and well-being,” said The Future Has Begun, a report on the impact of AI on government by the Partnership for Public Service and the IBM Center for the Business of Government.  “That promise has great potential to transform government.”

 “The most challenging problems AI may help us solve - from fighting terrorists to serving vulnerable populations - will involve government.  More immediately, though not less consequentially, AI will change the way public servants do their jobs.  Because of the impact AI could have on how governments ensure our safety and further our well-being, government leaders are likely to play a significant role in dictating future directions for AI.”

To illustrate how government is using AI, the report is focused on four concrete use cases being pursued by different agencies.  In-depth interviews were conducted with 14 people from 10 organizations, including the various government agencies involved, as well as their collaborators in non profits and universities.  Let me briefly summarize each of these four case studies.

How to Support the Widespread Adoption of AI

“Artificial intelligence is reshaping business - though not at the blistering pace many assume,” notes Building the AI-Powered Organization, an article in Harvard Business Review by McKinsey partners Tim Fountaine, Brian McCarthy, and Tamim Saleh in its opening sentence.  A number of studies agree that AI is the biggest commercial opportunity for companies and industries over the next 10-15 years.  Two recent reports by McKinsey and PwC respectively concluded that AI has the potential to incrementally increase global economic output between now and 2030 by an additional $13 to $15 trillion.

“Yet, despite the promise of AI, many organizations’ efforts with it are falling short,” adds the HBR article.  Surveys with thousands of executives, showed that most firms are only using AI in ad hoc pilots or applying it to a single business process.  Only 8% of firms are engaged in practices that support widespread adoption.

Why the slow progress?  “At the highest level, it’s a reflection of a failure to rewire the organization.  In our surveys and our work with hundreds of clients, we’ve seen that AI initiatives face formidable cultural and organizational barriers…  at most businesses that aren’t born digital, traditional mindsets and ways of working run counter to those needed for AI.”

To support the widespread adoption of AI, companies must make three fundamental shifts.

The State of AI in the Enterprise

A few months ago, Babson College professor Tom Davenport gave a talk on the state of AI in the enterprise at the annual conference of MIT’s Initiative on the Digital Economy.  His talk was based on two recent US surveys conducted by Deloitte, the first one in 2017 followed by a second in 2018.  Davenport was a co-author of both reports.

The 2017 survey was focused on the responses of 250 US executives who were leading the applications of AI in their companies.  The larger 2018 survey reached out to 1,100 IT (46%) and line-of-business (54%) executives from US-based companies (64% at the C-level) and 10 different industries.  All of these respondents were early AI adopters compared with their counterparts in an average company, - 90% were directly involved in their company’s AI projects, and 75% said that they had an excellent understanding of AI.

Davenport started his talk by summarizing the key findings in the Deloitte surveys:

• 20-30% of enterprises are early adopters, having implemented at least one AI prototype or production application;
  
• Many projects are in pilots but some are already in production;
• Relatively simple low hanging fruit projects prevail over more ambitious and complex moon shots;
  
• Only 24% cited “reducing headcount through automation” as one of their top AI priorities;
• The great majority of respondents believe that AI leads to moderate or substantial changes in job roles and skills;
• Implementation, integration, data issues and talent top the list of challenges faced by early adopters;
  
• Further AI growth is inevitable.

Using Agile Processes to Develop AI-Based Solutions

In the waterfall development model, a project is broken down into a set of sequential phases, - e.g., conception, analysis, design, construction, testing, deployment, maintenance, - each dependent on the completion of the previous phase.  Such a model is most appropriate for complex engineering projects, like bridges, airplanes and computer hardware.  It’s also quite applicable for mission critical software and infrastructure projects where security, availability, scalability and other ilities are paramount.

But, a serial, orderly approach isn’t appropriate for projects where time-to-market, usability, and flexibility are the highest priorities.  For such projects, an agile development approach is much more suitable.  Key to the agile approach is the involvement of the product’s target end-users early in the process to make sure that the product under development is responsive to their requirements. 

Agile development is evolutionary in nature, based on the early availability of experimental prototypes, user feedback, continuous improvements, and rapid responses to technology, market and user requirements.  It’s not surprising that agile software development took off in the 1990s, when the explosive rise of the Internet brought millions and millions of users online, lots of new user-oriented applications, and short time-to-market cycles in response to fierce competition.

Our increasingly sophisticated smart machines bring with them a whole new set of development challenges.  These AI-based systems are generally software-intensive, user-centric, and data-driven.  Their complexity goes way up the smarter we want the systems to be.  The behavior of an AI system based on machine learning depends on the information we use to train its algorithms rather than on the precise set of software instructions that tell the computer what to do.

AI systems are designed to sense, respond and adapt to a changing environment.  Their development feels sort of like bringing up and sending a teen-ager into the world.  Given that we want them to be able to handle unanticipated situations, they must have a fair degree of flexibility and autonomy.  But, it’s hard to be sure that the system (or the teen-ager) will always behave the way we’d like them to.

From “Buy What We Have” to “Connected Customer Strategies”

Adapting to the ongoing digitization of the economy is one of the key challenges faced by just about every company around the world.  While there are many ways a company can leverage digital technologies to differentiate itself from competitors, two are particularly prominent: streamline operations to develop higher quality products at competitive prices, and provide a superior, cost-effective customer experience through digital channels

The explosive growth of mobile devices means that a company can be engaged with its customers whether they are at home, at work, or shopping in a store.  But, digital customers can be fickle and hard to satisfy.  A successful business will try to make each of its clients feel special by understanding and addressing their unique requirements, and by providing excellent customer service and quickly resolving problems when they come up.  This is hard, which is also why it may very well be the most important way for a business to stand out from its competitors.

“Thanks to new technologies that enable frequent, low-friction, customized digital interactions, companies today are building much deeper ties with customers than ever before,” said a recent Harvard Business Review (HBR) article, The Age of Continuous Connection by Nicolaj Siggelkow and Christian Terwiesch, in its opening paragraph.  “Instead of waiting for customers to come to them, firms are addressing customers’ needs the moment they arise - and sometimes even earlier.  It’s a win-win: Through what we call connected strategies, customers get a dramatically improved experience, and companies boost operational efficiencies and lower costs.”

Most companies engage with their customer fleetingly, using what the authors call the buy what we have model.  In it, companies provide high-quality offerings at competitive prices, but it’s up to customers to identify their needs and seek out the appropriate products or services to meet them.  The key problem with the traditional buy what we have journey is that company and customer are poorly connected throughout.

The Business Value of Augmented Reality

I recently heard a very interesting presentation at the 2019 MIT CIO Symposium, - Why Companies Need an Augmented Reality Strategy, - by Harvard professor Michael Porter and James Heppelmann, CEO of PTC, an industrial software company.  The talk was based on their HBR article A Manager’s Guide to Augmented Reality.

We generally think of products as physical entities built with a combination of mechanical and electrical components, - e.g., appliances, cars, agricultural machines, industrial equipment, - some quite simple and some highly complex.  But, in a 2014 HBR article, Porter and Heppelmann introduced the notion of smart connected products (SCPs), a whole new class of products that, in addition to mechanical and electrical components, use digital components like microprocessors, sensors, data storage, software, and connectivity in a wide variety of ways.  Smart connected products are radically reshaping companies and exposing them to new competitive opportunities and threats, while altering industry boundaries and creating entirely new industries.

Through its first five decades, IT helped companies become significantly more productive, by automating many of their processes, reengineering their overall operations, and reaching out beyond the boundaries of the enterprise.  But, their actual products were largely unaffected.  This is all now changing with the rise of the Internet of Things (IoT), cloud computing, Big Data, AI, and other innovations whose combined impact is driving the new era of smart connected products.

As the world’s digital and physical infrastructures converge, IT is now an integral part of the products themselves. Digital technologies are designed right into the products.  Massive amounts of product-usage data can now be gathered, stored and analyzed by a variety of applications in the product’s cloud counterpart.  The cloud might also include a digital twin of each specific physical product, that is, a highly realistic 3-D digital model that monitors the real-time status of the digital twin’s physical counterpart, - e.g., a jet engine, elevator or turbine, - and helps make important decisions such as the need for preventive maintenance to prevent a product failure.

Is the Digital Revolution At Last Shaking Up Banking?

Digital transformations have generally followed three distinct stages.  First comes the use of IT to improve the productivity and quality of back- and front-end processes.  Distribution is next, leveraging the universal reach and connectivity of the Internet for online transactions and access to information. Finally, the transformation reaches a tipping point when technologies radically change the business model and user experience, leading to a fundamental disruption of the industry.

While such a digital journey is ultimately inevitable, the pace varies widely across industries.  The IT industry has been the most disrupted, - often by its own digital creations in a kind of sorcerer’s apprentice scenario.  Over my long career, I’ve seen many once powerful IT companies done in by technology and market changes, and either disappear altogether or become shadows of their former selves.

Beyond IT, few industries have felt the impact of digital forces like media.  In less than two decades, the global recorded music industry has lost over half its revenues, while the drop in newspaper advertising revenue in the US has been even steeper.  Retail has also undergone major changes with the rise of e-commerce, as has telecommunications with the transition to mobile phones.  How about banking, which has long been a leading user of information technologies?

The May 4 issue of The Economist addressed this question in a special report on Banking and Technology, with several articles on the subject.  “Over the past two decades people across the world have seen digital services transform the economy and their lives,” said its lead article.  “Taxis, films, novels, noodles, doctors and dog-walkers can all be summoned with a tap of a screen.  Giant firms in retailing, carmaking and the media have been humbled by new competitors.  Yet one industry has withstood the tumult: banking.”

But, adds The Economist, “technology is at last shaking up banking.”  Two major forces are driving this shake-up: the dramatic growth of digital payments in China and other Asian countries; and the rise of mobile banking in the West, especially among young digital natives.

The Economic Value of Digital Identity

Identity plays a major role in our everyday life.  It’s the key that determines the particular transactions in which we can rightfully participate as well as the information we’re entitled to access.  Think about going to an office, getting on a plane, logging on to a website or making an online purchase.  We generally don’t pay much attention to the management of our identity credentials unless something goes seriously wrong.

For much of history, our identity systems have been based on face-to-face interactions and on physical documents and processes.  But, the transition to a digital economy requires radically different identity systems.  In a world that’s increasingly governed by digital transactions and data, our existing methods for managing security and privacy are proving inadequate.  Data breaches, large-scale fraud, and identity theft have become more common.  In addition, a significant portion of the world’s population lacks the credentials needed to participate in the digital economy.  Our existing methods for managing digital identities are far from adequate.

A few weeks ago, the McKinsey Global Institute released Digital Identification: A key to inclusive growth, a comprehensive report at over 100 pages that examined the state of digital identities around the world.  The report analyzed the value creation potential of good digital IDs for individuals and institutions, as well as the potential risks and challenges.  McKinsey’s analysis was informed by nearly 100 concrete uses of digital IDs in seven countries: Brazil, China, Ethiopia, India, Nigeria, the UK and the US.

The Puzzling Personal Productivity Paradox

The past 10-15 years have seen a number of technology advances, from smartphones to machine learning.  Yet, despite these impressive advances, for most of this period economies around the world have been stuck in an era of slow productivity growth.  Opinions abound, but in the end, there’s no consensus on the causes of this apparent productivity paradox, on how long the slowdown will likely last, or on what to do about it.

Some economists have argued that secular stagnation is the reason behind this unusual situation, caused primarily by a reluctance of companies to invest and of consumers to spend.  Others feel that aging populations around the world, accompanied by slow or declining labor forces are major factors.  A few contend that there’s been a fundamental decline in innovation and productivity over the past few decades, compared to the period between 1870 and 1970.

Yet others argue that there have always been time lags between technology advances and their impact on economic growth.  While technologies may advance rapidly, humans and institutions change slowly.  And, the more transformative the technologies, - as is currently the case, - the longer it takes for their impact to be felt across economies and societies.

But, companies and industries are not the only ones trying to adjust to our fast changing digital economy.  Individuals are also experiencing their own version of the productivity paradox.  Personal computers, along with applications like word processing and spreadsheets introduced a whole new category of personal productivity tools in the 1980s. A decade later, the Internet and World Wide Web enabled people to easily communicate with each other, and gave them access to all kinds of information and applications.

A Novel Method for Measuring the Value of Free Digital Services

Gross domestic product (GDP) is the basic measure of a country’s overall economic output, based on the market value of all the goods and services the country produces.  Most measures of economic performance used by government officials to inform their policies and decisions are based on GDP figures.  But, concerns have long been raised about the adequacy of GDP-based measurements given the major changes that’ve taken place in economies around the world over the past several decades.

GDP is essentially a measure of production.  While suitable when economies were dominated by the production of physical goods, GDP does not adequately capture the growing share of services and increasingly complex solutions that characterize advanced economies.  Nor does it reflect important economic activity beyond production, such as income, consumption and living standards.

In 2008, a Commission on the Measurement of Economic Performance and Social Progress led by Nobel-prize winning economists Joseph Stiglitz and Amartya Sen was convened to look at the limits of GDP as an indicator of economic performance and progress.  “What we measure affects what we do; and if our measurements are flawed, decisions may be distorted.” noted its report.  The Commission recommended complementing classical measures of GDP and economic production with additional measurements that captured people’s well being, as well as factoring in measurements to help reflect the evolution of the economy into the future.

Over the past decade, a new set of concerns have arisen with the rise of the Internet-based digital economy.  How do you measure the value of the increasing amounts of free information goods available over the Internet, including Wikipedia articles, Google maps, Facebook interactions, smartphone apps and You Tube videos?

AI and the Evolution of History

Several weeks ago, I wrote about the event I attended on February 28 to celebrate the launch of MIT’s new Schwarzman College of Computing, - MIT’s strategic response to the rise of artificial intelligence, - a technology that will reshape “geopolitics, our economy, our daily lives and the very definition of work” in the decades to come.  The all-day celebration featured talks and panels on a wide variety of topics, some focused on innovative applications of AI technologies, others on the challenging issues raised by these powerful technologies.

A few weeks later I wrote about one of these challenging issues, the impact of AI on our social interactions, based on the talk by MIT professor Sherry Turkle on Rethinking Friction in Digital Culture, and a related article by Yale professor Nicholas Christakis on How AI Will Rewire US.

I now want to discuss the interview conducted by NY Times columnist Thomas Friedman with former US Secretary of State Dr. Henry Kissinger at the February 28 event.  The interview, - which can be seen in this video, - was based on a June, 2018 article by Dr. Kissinger in The Atlantic, - How the Enlightenment Ends: “Philosophically, intellectually - in every way - human society is unprepared for the rise of artificial intelligence.”

The Third Wave of the Digital Economy - Promises and Challenges

“The next wave of digital innovation is coming.  Countries can welcome it, prepare for it, and ride it to new heights of innovation and prosperity, or they can ignore the changing tide and miss the wave,” writes Robert Atkinson in The Task Ahead of Us.  Atkinson is founder and president of the Information Technology and Innovation Foundation (ITIF), a think tank focused on science and technology policy.

We’re now entering the third wave of the digital economy, says Atkinson.  The first was based on personal computing, the Internet, Web 1.0, and e-commerce.  The second brought us Web 2.0, big data, smartphones and cloud computing.  The emerging third wave promises to be significantly more connected, - including higher bandwidth and a wide variety of devices; more automated, - with more work being done by machines while integrating the physical and digital worlds; and more intelligent, - leveraging huge volumes of data and advanced algorithms to help us understand and deal with our increasingly complex world.

“Building and adopting the new connected, automated, and intelligent technology system will lead to enormous benefits globally, not least of which will be robust rates of productivity growth and improvements in living standards.  Moreover, these technologies will help address pressing global challenges related to the environment, public health, and transportation, among others.”

We’re in the early stages of this third wave.  5G, IoT, robotics, AI and other promising technologies are being embraced by early marketplace adopters, but their full-scale impact is still 5 to 10 years away.  We’re in a period not unlike the late 1980s, when it was clear that IT was on the brink of a major transition, but the Internet revolution didn’t arrive until the mid-1990s.

The Top Ten Emerging Technologies of 2018

For the past several years, the World Economic Forum (WEF) has published an annual list of the Top Ten Emerging Technologies that would be potentially disruptive over the next three to five years while also providing significant benefits to economies and societies.  Starting in 2017, the Top Ten list has been chosen in collaboration with Scientific American and published in an issue of the magazine.

The Top Ten Emerging Technologies 2018 was released in September, 2018.  The final list was selected by a steering group of experts from an initial list with more than 50 submissions.  Here are the 10 technologies comprising the 2018 list, along with the reasons cited in the report for their selection:

What Might Work Look Like in 2030?

A number of recent studies have taken a close look at the the future of work over the next 10 to 15 years.  For example, a December, 2017 report by McKinsey examined which jobs will likely be displaced by automation through 2030, as well as which jobs are likely to be created over the same period, based on data from 46 countries.

McKinsey’s overall conclusion was that a growing technology-based economy will create a significant number of new occupations which will more than offset declines in occupations displaced by automation.  However, “while there may be enough work to maintain full employment to 2030 under most scenarios, the transitions will be very challenging - matching or even exceeding the scale of shifts out of agriculture and manufacturing we have seen in the past.”

Another report, The Changing Nature of Work, - published by the World Bank in October, 2018, - concluded that our problem is not that there won’t be enough work in the future.  The key problem is that, in many countries, the workforce isn’t prepared for a fast unfolding future. Neglecting investments in human capital, - i.e., the sum total of a population’s health, skills, knowledge, and experience, - will dramatically weaken a country’s competitiveness.

Let me now discuss Eight Futures of Work Scenarios and their Implications, a white paper published in January, 2018 by the World Economic Forum (WEF) in collaboration with the Boston Consulting Group (BCG).  The paper considered eight distinct scenarios on what work might be like by 2030, based on different combinations of three of the most impactful and uncertain variables affecting the future of work: the rate of technological change; the evolution of learning in the workforce; and the magnitude of talent mobility across geographies.

Impact of an Aging Population on Economic Growth and Automation

The world has been stuck in an era of slow economic growth over the past decade.  Top economists and policy makers have proposed a number of explanations for the economic slowdown, but in the end, there’s no consensus on the reasons, on how long the slowdown will likely last, or on what to do about it.

Economic growth has two main components, productivity growth and the growth of the labor force.  One potential theory for our stagnant economic growth is demographics, namely, the rapidly declining population and labor force growth around the world.

The current world’s population is 7.7 billion.  The UN Population Division estimates that global population will reach 9.8 billion with a growth rate of 0.5% in 2050, and will peak around 2100 at roughly 11.2 billion with a growth rate of 0.1%.  Other projections estimate that the population will grow slower, peak at 9.4 billion around 2070 and then decline to around 9 billion in 2100.  Some project that after reaching a peak of 9 billion, the world’s population will decline back to today’s levels, - around 7 billion, - by 2100.

Japan and a few other countries are already experiencing population declines.  The US population is currently around 330 million, and is projected to reach 390 million in 2050 and 450 million in 2100.  While the US birthrate hit a historic low in 2017, the population has continued to grow due to immigration.  The US growth rate is 0.71% including immigration and 0.43% without.

The global labor force grew at an average of 1.8% per year between 1960 and 2005, but since then it’s been growing at just 1.1% per year.  The labor force is still growing in some developing countries like India, Nigeria and the Philippines, but it’s already shrinking in China, Japan and Germany.  In the US, the labor force is growing very slowly, - 0.5 percent per year over the past decade, compared with 1.7 percent from 1960 to 2005.  Given the continuing decline in fertility rates in most parts of the world, the global labor force is expected to slip further in the coming decades.

The New, Interdisciplinary, MIT Schwarzman College of Computing

A few months ago, MIT president Rafael Reif announced the launch of the Stephen A. Schwarzman College of Computing in an e-mail to the MIT community.  “This new College is our strategic response to a global phenomenon – the ubiquity of computing and the rise of AI…” wrote President Reif.  “To state the obvious, AI in particular is reshaping geopolitics, our economy, our daily lives and the very definition of work.  It is rapidly enabling new research in every discipline and new solutions to daunting problems.  At the same time, it is creating ethical strains and human consequences our society is not yet equipped to control or withstand.”

The new College of Computing is made possible by a $350 million foundational gift from Stephen Schwarzman, - chairman, CEO and co-founder of Blackstone, one of the world’s leading investment firms.  The gift is part of a  $1 billion commitment to reshape MIT to help it better address the continuing advances in technology in our 21st century digital economy, as well as their many applications , - both the exciting opportunities and the difficult challenges.

The College is envisioned as an interdisciplinary hub across all five of MIT’s existing schools: Engineering; Science; Humanities, Arts, and Social Sciences; Architecture and Planning; and the Sloan School of Management.  This MIT News announcement succinctly summarized the key objectives of the new College:

• reorient MIT to bring the power of computing and AI to all fields of study at MIT, allowing the future of computing and AI to be shaped by insights from all other disciplines

• create 50 new faculty positions that will be located both within the College and jointly with other departments across MIT - nearly doubling MIT’s academic capability in computing and AI

• give MIT’s five schools a shared structure for collaborative education, research, and innovation in computing and AI

• educate students in every discipline to responsibly use and develop AI and computing technologies to help make a better world; and

• transform education and research in public policy and ethical considerations relevant to computing and AI.

Despite All the Bad News that Surround Us… Is the World Actually Getting Better?

The World is Getting Quietly, Relentlessly Better is the title of a recent WSJ article by its chief economics commentator Greg Ip.  “If you spent 2018 mainlining misery about global warming, inequality, toxic politics or other anxieties, I’m here to break your addiction with some good news: The world got better last year, and it is going to get even better this year,” writes Ip in his opening paragraph.  “Poverty around the world is plummeting; half the world is now middle class; and illiteracy, disease and deadly violence are receding.  These things don’t make headlines because they are gradual, relentless and unsurprising.”

Ip’s article is mostly based on data insights from Our World in Data, a comprehensive website that quantifies the evolution of our global living conditions over the past few centuries.  Our World in Data (OWID) is an initiative of the Oxford Martin Programme on Global Development, led by economist Max Roser at the University of Oxford.

OWID analyzes longitudinal data on how the world has been changing over decades and centuries, as well as explaining the causes and consequences of those changes in its accompanying articles. It makes extensive use of visual aids, including interactive graphs and maps, to present its analyses and insights.  Its content is organized into 15 main sections:  population, health, food, energy, environment, technology, growth & inequality, work & life, public sector, global connections, war & peace, politics, violence & rights, education, media and culture, -  each of which further includes a number of subsections.

Let me summarize just a few of the key trends in the OWID website.

The Current State of AI Adoption

AI is seemingly everywhere.  In the past few years, the necessary ingredients have finally come together to propel AI beyond early adopters to a broader marketplace: powerful, inexpensive computer technologies; advanced algorithms; and huge amounts of data on almost any subject.  Newspapers and magazines are full of articles about the latest advances in machine learning and related AI technologies.

Two recent reports concluded that, over the next few decades, AI will be the biggest commercial opportunity for companies and nations.  AI advances have the potential to increase global GDP by up to 14% between now and 2030, the equivalent of an additional $14-15 trillion contribution to the world’s economy, and an annual average contribution to productivity growth of about 1.2 percent.

Over time, AI could become a transformative, general purpose technology like the steam engine, electricity, and the Internet. AI marketplace adoption will likely follow a typical S curve pattern, - that is, a relatively slow start in the early years, followed by a steep acceleration as the technology matures and firms learn how to best leverage AI for business value.

To get a better sense of the current state of AI adoption, McKinsey recently conducted a global online survey on the topic, garnering responses from over 2,000 participants across 10 industry sectors, 8 business functions and a wide range of regions and company sizes.  The survey asked about their progress in deploying nine major AI capabilities, including machine learning, computer vision, natural language text and speech processing, and robotic process automation.

The Rise of Global, Superstar Firms, Sectors and Cities

In the 1990s, the Internet was supposed to usher a much more open, decentralized, democratic economy and society.  Startups with innovative business models where now able to reach customers anywhere, anytime.  Companies, from the largest to the smallest, could now transact with anyone around the world.  Vertically integrated firms became virtual enterprises, increasingly relying on supply chain partners for many of the functions once done in-house.  Experts noted that large firms were no longer necessary and would in fact be at a disadvantage in the emerging digital economy when competing against agile, innovative smaller companies.

Some even predicted that the Internet would lead to the decline of cities.  People could now work and shop from home; be in touch with their friends over e-mail, video calls, and text messaging; and get access to all kinds of information and entertainment online.  Why would anyone choose to live in a crowded, expensive, crime-prone urban area when they could lead a more relaxing, affordable life in an outer suburb or small town?

But, as we well know, it hasn’t quite worked out as expected.  Instead, we’ve seen the rise of the global superstar company, the unicorn startup, and the winner-take-all city.  As it’s turned out, the Internet’s universal reach and connectivity has led to increasingly powerful network effects and to the rise of platform economies.

Beyond Machine Learning: Capturing Cause-and-Effect Relationships

Artificial intelligence is rapidly becoming one of the most important technologies of our era.  Every day we can read about the latest AI advances from startups and large companies.  AI technologies  are approaching or surpassing human levels of performance in vision, speech recognition, language translation, and other human domains. Machine learning advances, like deep learning, 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.

Deep learning is a powerful statistical technique for classifying patterns using large training data sets and multi-layer AI neural networks.  It’s essentially a method for machines to learn from all kinds of data, whether structured or unstructured, that’s loosely modeled on the way a biological brain learns new capabilities.  Each artificial neural unit is connected to many other such units, and the links can be statistically strengthened or decreased based on the data used to train the system.  Each successive layer in a multi-layer network uses the output from the previous layer as input.

Machine learning can be applied to just about any domain of knowledge given our ability to gather valuable data in almost any area of interest.  But, machine learning methods are significantly narrower and more specialized than humans.  There are many tasks for which they’re not effective given the current state-of-the-art.  In an article recently published in Science, professors Erik Brynjolfsson and Tom Mitchell identified the key criteria that help distinguish tasks that are particularly suitable for machine learning from those that are not.  These include:

• Tasks that map well-defined inputs to well-defined outputs, - e.g., labeling images of specific animals, the probability of cancer in medical record, the likelihood of defaulting on a loan application;
• Large data sets exist or can be created containing such input-output pairs, - the bigger the training data sets the more accurate the learning;
• The capability being learned should be relatively static, - If the function changes rapidly, retraining is typically required, including the acquisition of new training data; and
• No need for detailed explanation of how the decision was made, - the methods behind a machine  learning recommendation, - subtle adjustments to the numerical weights that interconnect its huge number of artificial neurons, - are difficult to explain because they’re so different from those used by humans.

The World Bank’s Human Capital Index

Last week I wrote about a recently published World Bank report, The Changing Nature of Work.  The report highlights the increasing importance of investing in human capital by both the public and private sectors.  All around the world, the demand for workers who are good at complex problem-solving, interactions with clients and colleagues, and able to adapt to new tools and technologies is rising, while the demand for less advanced skills that can be replaced by technology is declining.  Neglecting investments in human capital, it warned, can dramatically weaken a country’s competitiveness.

“The world is healthier and more educated than ever…,” notes the World Bank in a companion report, The Human Capital Project.  “But a large and unfinished agenda remains.  Life expectancy in the developing world still lags far behind that of rich countries…  Worldwide, more than 260 million children and youth are not in school. Meanwhile, nearly 60 percent of primary school children in developing countries fail to achieve minimum proficiency in learning.”

Why should countries invest in human capital?  Can early health care and education prepare children to succeed and prosper as adults in a rapidly changing world?  What are the barriers to nurturing human capital and how can countries overcome them?  To address these important questions, the World Bank introduced the Human Capital Index (HCI).  The HCI is designed to quantify the amount of human capital that a child born today can expect to attain by age 18 given the prevailing health and education conditions in the country where the child lives.  It will be updated periodically and refined as data measures improve.  This video nicely explains the HCI and what it measures. 

Blockchain Beyond the Hype

A recent issue of The Economist included a special report on cryptocurrencies and blockchains.  The Economist’s overall conclusion was that “Bitcoin has been a failure as a means of payment, but thrilling for speculators.”  Its assessment of blockchain was somewhat more positive.  “For blockchains, the jury is still out,… For all the technology’s potential, though, most attempts to use it remain tentative,…  The advantages of blockchains are often oversold.” 

In 2016, blockchain made the list of the World Economic Forum’s Top Ten Emerging Technologies.  That same year, blockchain also made its first appearance in Gartner’s yearly hype cycles.  Even The Economist has been guilty of overselling when it featured blockchain in the cover of its October 31, 2015 issue with the tag line “The Trust Machine: How the technology behind Bitcoin could change the world.” 

Is there too much hype surrounding blockchain?  Absolutely,… but not surprising.  All potentially transformative technologies are oversold in their early stages.  Remember the dot-com bubble of the late 1990s.  Blockchain is still in its early phases of experimentation and adoption.  Much work remains to be done on standards, platforms, interoperability, applications and governance. 

But, does blockchain have the potential to become a truly transformative technology over time?  Yes, said McKinsey in a recent article on the strategic value of blockchain beyond the hype.  The article starts out by acknowledging that all the hype around blockchain makes it difficult to nail down not just blockchain's long term strategic value, but also what it actually is and what it isn’t in the present. To make sure everyone is on the same page, the article first recaps what it calls “the nuts and bolts of blockchain.”  Let me start by summarizing blockchain’s nuts and bolts.