Irving Wladawsky-Berger

The Evolving Automotive Ecosystem

Much as was once the case with electrification, digitization is now transforming industry after industry.  In media and communications, for example, everything seems to be changing at once, from the way content is produced and delivered, to the sources of revenue and profits.  In financial services, money and payment ecosystems are becoming increasingly digital now that just about everyone in the world can afford a personal mobile device.  Smart, connected products are reshaping industry boundaries and forcing companies to ask themselves a fundamental question: What business am I really in? 

This is also the case in the automobile industry, one of the largest in the world.  “Not since the first automotive revolution has there been such stunning innovation in the industry,” notes an excellent recent KPMG report, - Me, my car, my life… in the ultraconnected age.  “Autonomous vehicles are only part of the story.  The convergence of consumer and automotive technologies and the rise of mobility services are transforming the automotive industry and the way we live our lives.” 

Self-driving cars have commanded our attention in the last few years.  The advent of self-driving vehicles like the Google car is not only a truly dramatic milestone in AI, but concrete evidence that digital technologies are having a huge impact on the future of the automobile.  Opinions vary as to the commercial prospects for such truly autonomous vehicles.  Some feel that they will be all around us within a decade, navigating our present roads right along human-driven cars.  Others are not quite so sure due to the highly complex technical and societal issue that remain to be worked out.  Time will tell. 

In any event, car companies are working hard to try to keep pace with the speed of innovation in consumer technologies, a huge challenge to the auto industry, notes the KPMG report: “Melding the two worlds - consumer electronics, with its rapid new product launch cadences and willingness to accept iterative software releases, and automotive engineering, with its mass customization, millions of product configurations, and critical safety, durability, and reliability requirements - is not an easy prospect.”

Complex Systems and the “Rashomon Effect”

I recently read a very interesting NY Times column, The Reality of Quantum Weirdness, by UC Berkeley professor Edward Frenkel.  In the column, professor Frenkel discusses a very deep and important question:  Is there such a thing as a true reality, or “is our belief in a definite, objective, observer-independent reality an illusion?”  The article is about the strange world of quantum mechanics, a world that’s very different from our everyday life experiences.  But part of my fascination with the subject is that I often ask myself similar questions when thinking about the equally mysterious world of highly complex emergent systems, that is, systems where the whole can at times be quite different from the sum of their parts.

Frenkel is an author, - Love and Math is his most recent book, - and a filmmaker in addition to being a mathematician.  He uses the so-called Rashomon effect  to illustrate his points, an effect named after Rashomon, a classic 1950 film by Japanese director Akira Kurosawa, one of the most prominent and influential directors of all time.   

The movie is famous for its novel plot device.  Near Kyoto, a samurai has been killed, but it’s not clear why or by whom.  Four different characters tell widely different versions of the same event:  the samurai’s wife who says she was raped by a bandit, subsequently fainted and then awoke and found her husband dead; a bandit who says he seduced the wife and then killed the samurai in an honorable duel; a woodcutter who says he witnessed the rape and murder but did not want to get involved; and the dead samurai, who speaking through a medium said that the shame of the events he witnessed drove him to kill himself.

The film is an exploration of multiple realities, where it’s not at all clear if there is a real truth, let alone what it might be.  The Rashomon effect has thus come to stand for the contradictory interpretations of the same event by different people.

The Promise and Challenges of Health Analytics

On February 24 I attended a workshop in MIT on the Future of Health Analytics.  The event was sponsored by MIT Connection Science, a recently organized research initiative aimed at leveraging data science to quantify and analyze human behaviors, and to leverage the new insights thus obtained in key societal applications, including healthcare, transportation and finance.  Connection Science, - with which I’m affiliated as a Fellow, - was founded by Media Lab professor Alex “Sandy” Pentland.  He’s the author of several books, including the recently published Social Physics: How Good Ideas Spread.  

I’ve worked with Pentland for the past several years, and have previously written about his research on Reinventing Society in the Wake of Big Data, as well as his work with the World Economic Forum and others on the creation of trust frameworks for the sharing and protection of personal data.  In his opening remarks at the workshop, Pentland talked about Big Data and Health.  The little data breadcrumbs that we leave behind as we move around in the world can now be reality mined to help us better understand our behaviors and thus improve our lives and health.  He discussed several applications of big data to health, based on research at his MIT Human Dynamics Lab, as well as startups he’s involved in.

Following Pentland, Dr. Dennis Ausiello talked about Quantitative Human Phenotyping and the opportunities it offers to transform the practice of medicine.  Ausiello is professor of medicine at the Harvard Medical School, Chief Emeritus of Medicine at Massachusetts General Hospital (MGH), and director and co-founder of the Center for Assessment Technology and Continuous Health (CATCH), a joint MGH-MIT initiative aimed at finding new ways of measuring the human condition, i.e., phenotypes.

The Rise of the On-Demand Economy

The Industrial Revolution led to the creation of many large and mid-size companies in which people worked together to produce goods and/or deliver services.  Having a good job meant working for one such company.  These workers, many of them unionized, enjoyed relatively secure, stable jobs and a number of company benefits, including health care insurance, vacations and retirement pensions. 

This corporate capitalism era, - characterized by hierarchically organized institutions, - reached its height in the US in the period following World War 2, when the country craved a sense of stability following the pain and chaos of the Great Depression and the war years.  But those days are long gone.  In the more recent past, these relatively slow moving organizations were not able to keep up with fast changing technologies and markets, nor could individuals assume that being loyal to a company would translate into a secure job with good benefits. 

In its stead, we are seeing the rise of what The Economist called the On-Demand Economy in a recent article.  “Ever since the 1970s,…  Manufacturing jobs have been automated out of existence or outsourced abroad, while big companies have abandoned lifetime employment.  Some 53m American workers already work as freelances.”

What Will Life Be Like in an AI Future?

People have long argued about the future impact of technology.  But, as AI is now seemingly everywhere, the concerns surrounding its long term impact may well be in a class by themselves.  Like no other technology, AI forces us to explore the boundaries between machines and humans.  What will life be like in such an AI future?

Not surprisingly, considerable speculation surrounds this question.  At one end we find books and articles exploring AI’s impact on jobs and the economy.  Will AI turn out like other major innovations, e.g., steam power, electricity, cars, -  highly disruptive in the near term, but ultimately beneficial to society?  Or, as our smart machines are being increasingly applied to cognitive activities, will we see more radical economic and societal transformations?  We don’t really know.

These concerns are not new.  In a 1930 essay, for example, English economist John Maynard Keynes warned about the coming technological unemployment, a new societal disease whereby automation would outrun our ability to create new jobs.