Irving Wladawsky-Berger

(I Can’t Keep No) Satisfaction

(I Can’t Get No) Satisfaction, the Rolling Stones’ 1965 megahit, should have more appropriately been titled (I Can’t Keep No) Satisfaction, wrote social scientist and author Arthur C. Brooks in How to Want Less: The secret to satisfaction has nothing to do with achievement, money, or stuff, - a recent essay in The Atlantic. Brooks has been in the faculty of the Harvard Kennedy School for the past two years, after being president of the American Enterprise Institute from 2009 to 2019.

Satisfaction, he explained, “is the greatest paradox of human life. We crave it, we believe we can get it, we glimpse it and maybe even experience it for a brief moment, and then it vanishes. … In fact, our natural state is dissatisfaction, punctuated by brief moments of satisfaction.”

Our human tendency to pursue satisfaction despite its fleeing nature condemns us to continuously live in a so-called hedonic treadmill. We might not like it, “but Mother Nature thinks it’s pretty great. She likes watching you strive to achieve an elusive goal, because strivers get the goods - even if they don’t enjoy them for long. More mates, better mates, better chances of survival for our children - these ancient mandates are responsible for much of the code that runs incessantly in the deep recesses of our brains.”

The 2022 Work Trend Index: How to Get Hybrid Right

“The people returning to the office are not the same as those who left in March 2020,” wrote Jared Spataro, Microsoft Corporate VP for Modern Work, in a recent Harvard Business Review (HBR) article, 5 Key Trends Leaders Need to Understand to Get Hybrid Right. The article summarized the findings of Microsoft’s 2022 Work Trend Index, a report based on various sources of information, including a survey of 31,000 people in 31 countries, labor trends from LinkedIn, and trillions of signals from Microsoft 365 suite of productivity apps.

“The data shows that the past two years left an indelible mark on the psyche of workers, altering their expectations and fundamentally shifting the way work gets done,” said Spataro. “This is a pivotal moment, where leadership matters more than ever. Those who embrace a new mindset and shift cultural norms will best position their people and their business for long-term success. Adapting to the influx of change in this business environment is no easy feat.”

“After many false starts, organizations are finally making the shift into a true hybrid work model. Like every other turning point over the past two years, there’s no shortage of perspectives on what this next phase will look like.” However, there’s no going back to the way work once was. Adapting to the many changes in this business environment is a challenge now facing every leader. To navigate the transition to hybrid work, leaders need to understand five key trends.

How Can You Trust the Predictions of a Large Machine Learning Model?

Artificial intelligence has emerged as the defining technology of our era, as transformative over time as the steam engine, electricity, computers, and the Internet. AI technologies are approaching or surpassing human levels of performance in vision, speech recognition, language translation, and other human domains. Machine learning (ML) 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. 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. But such statistical methods are not equally suitable for all tasks. Tasks that are particularly suitable for machine learning, exhibit several key criteria, such as the availability of large data sets of well-defined input-output pairs for training ML classifiers, - e.g., carefully labeled cat, not-cat pictures for cat recognition classifiers, and english-french document pairs for machine translation algorithms.

The methods behind a machine learning prediction, - subtle adjustments to the numerical weights that interconnect its huge number of artificial neurons, - are very difficult to explain because they’re so different from the methods used by humans. The bigger the training data set, the more accurate the prediction, but the more difficult it will be to provide a detailed, understandable explanation to a human of how the prediction was made.

A few weeks ago I attended an online seminar, - How Can You Trust Machine Learning? by Stanford professor Carlos Guestrin, - on the difficulty of understanding machine learning predictions. Guestrin’s seminar was based on a 2016 article he co-authored with Marco Tulio Ribeiro and Sameer Singh , “Why Should I Trust You?” Explaining the Predictions of Any Classifier.

Beyond GDP: A Framework for Measuring Economic Progress

“What is meant by economic progress, and how should it be measured?,” asked economists Diane Coyle and Leonard Nakamura in a recent paper, Time Use and Household-Centric Measurement of Welfare in the Digital Economy. “The conventional answer is growth in real GDP over time or compared across countries, a monetary measure adjusted for the general rate of increase in prices. However, there is increasing interest in developing an alternative understanding of economic progress, particularly in the context of digitalization of the economy and the consequent significant changes Internet use is bringing about in production and household activity.”

Gross Domestic Product (GDP) became the accepted international measure of economic progress in the 1940s. It was a good measure for an industrial economy dominated by the production of physical goods, but GDP doesn’t reflect important economic activity beyond production, such as income, consumption and quality of life, nor does it capture measures of economic welfare or utility, that is, the extent to which a service or good satisfies an individual’s wants and needs. In addition, GDP doesn’t include the value of the increasing amounts of free information goods now available in the Internet-based digital economy, including email, texts, social media, maps, apps and videos.

In their paper, Coyle and Nakamura propose an alternative approach for measuring economic progress based on how much time people spend on different daily activities - e.g., paid work, household tasks, leisure, and consumption - combined with measures of well-being while engaged in these different activities. “In an economy that is four-fifths services rather than goods, with time to consume therefore inherent in the majority of economic activity, the utility of the different uses of time seems key to understanding economic welfare as well as productivity.”