Irving Wladawsky-Berger

The Widening Earnings Gap Between College and High School Graduates

“In one of the richest nations on earth, the path to prosperity has narrowed significantly in recent decades — especially for those without a college education,” wrote the NY Times in its January 28 editorial, “See Workers as Workers, Not as a College Credential.” “More than 62 percent of Americans ages 25 and up do not hold bachelor’s degrees, and the earnings gap between those with a college education and those without one has never been wider,” it said. “That’s happening even as the cost of college spirals upward, putting it out of reach for many. This has fueled anxiety, bitterness and a sense of alienation among the millions who see themselves as shut out of an economy that does not value them.”

The editorial cites data from the Federal Reserve Bank of NY showing that the wage gap between recent college and high school graduates hit a record high in 2021. In 1990 the median annual earnings of recent college graduates ages 22 to 27 with a full-time job was $48,900 for those with a bachelor’s degree and $35,300 for those with a high school diploma, a difference of $13,600. But in 2021, the median annual earnings for similar recent college graduates was $52,000 and $30,000 for those with a high school diploma, a difference of $22,000. Not only was the difference in earnings over 60% higher in 2021 than in 1990, but the median earnings for those with a high school diploma has decreased by 15% in the intervening three decades.

This is a stark example of the so-called great divergence of the past four decades. In the 30 years between 1948 and 1978, US productivity as measured by output per hour rose by 108%, an annual growth rate of 2.4%, while the average compensation of production workers increased in lock-step by 95%. But, between 1978 and 2016, productivity rose by 66%, an annual growth rate of 1.3%, while the median compensation of workers increased by only 9%. In other words, while the U.S. witnessed major technological innovations and productivity gains in the past four decades, the wages of workers in the middle have barely changed, with most of the wage benefits of higher productivity going to high earners. Other advanced economies have experienced similar economic, technological and global forces but their workers have fared much better.


Large Language Models: A Cognitive and Neuroscience Perspective

In the past decade, powerful AI systems have matched or surpassed human levels of performance in a number of tasks such as image and speech recognition, skin cancer classification, breast cancer detection, and highly complex games like Go. These AI breakthroughs have been based on increasingly powerful and inexpensive computing technologies, innovative deep learning (DL) algorithms, and huge amounts of data on almost any subject. More recently, the advent of large language models (LLMs) are taking AI to the next level. And, for many technologists like me, LLMs and their associated chatbots have introduced us to the fascinating world of human language and cognition.

I recently learned the difference between form, communicative intent, meaning, and understanding from “Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data,” a 2020 paper by linguistic professors Emily Bender and Alexander Koller. These linguistic concepts helped me understand the authors’ argument that “in contrast to some current hype, meaning cannot be learned from form alone. This means that even large language models such as BERT do not learn meaning; they learn some reflection of meaning into the linguistic form which is very useful in applications.”

A few weeks ago, I came across another interesting paper, “Dissociating Language and Thought in Large Language Models: a Cognitive Perspective,” published in January, 2023 by principal authors linguist Kyle Mahowald and cognitive neuroscientist Anna Ivanova and four additional co-authors. The paper nicely explains how the study of human language, cognition and neuroscience sheds light on the potential capabilities of LLMs and chatbots. Let me briefly discuss what I learned.

“Today’s large language models (LLMs) routinely generate coherent, grammatical and seemingly meaningful paragraphs of text,” said the paper’s abstract. “This achievement has led to speculation that these networks are — or will soon become — thinking machines, capable of performing tasks that require abstract knowledge and reasoning. “Here, we review the capabilities of LLMs by considering their performance on two different aspects of language use: formal linguistic competence, which includes knowledge of rules and patterns of a given language, and functional linguistic competence, a host of cognitive abilities required for language understanding and use in the real world.”

The Economic Value of Open Source

The supply side of open source software (OSS), — that is, the reasons for joining and contributing to open source communities, — has been extensively studied and quantified. For example, the Linux Foundation (LF) has over 1,500 members, and is home to nearly 1,000 open source projects from over 15,000 contributing organizations. The LF estimates that over 50 million lines of code are added weekly to its sponsored projects, which comprise a significant percentage of the world’s mission-critical infrastructures, a figure that’s significantly higher if we include the contributions of other open source communities like the Apache Software Foundation.

However, we know much less about the demand side of OSS. Despite the wide adoption of OSS by firms and industries around the world, we don’t really understand, nor are we able to quantify its overall economic value to the many organizations that rely on open source. What benefits do these organizations derive from OSS? What costs  do they incur from using or contributing to OSS? And, if not able to use OSS in a project, what would have been the next-best alternatives, and how much would they have cost?

To get answers to these and other important OSS questions, the LF sponsored a study led by UC Berkeley professor Henry Chesbrough, — whose fourth book on open innovation, Open Innovation Results, was published in 2020. The key findings and conclusions from the study are discussed in a White Paper, “Measuring the Economic Value of Open Source.”

Driving Value with the Metaverse in the Enterprise

A killer app is an IT application whose value to individuals and companies is relatively simple to explain, its use is fairly intuitive, and the application turns out to be so useful that it can singlehandedly propel the success of a new product or service. Word processing and spreadsheets, for example, were major factors in the widespread adoption of personal computers in the 1980s.

I got interested in 3D immersive environments in the mid-2000s when IBM launched an exploratory initiative on the topic. As part of these efforts, we conducted a number of experiments on Second Life, a platform that allowed its users to develop a variety of virtual world applications. We built a number of workplace applications, including virtual meetings, and online events, and even built a virtual replica of Beijing’s Forbidden City in partnership with the Palace Museum that was opened to the public. But, despite our high expectations, we couldn’t find any compelling enterprise killer apps, and after about three years, the project was dissolved. In the ensuing years, 3D immersive technologies and applications continued to advance, but mostly focused on video-games and related industries.

The past few years have seen a surge of interest in the metaverse and extended reality. “After desktop computing, the consumer internet and the smartphone boom, the consumer-computing industry is past due its Next Big Thing,” wrote The Economist in  “A reality check for the metaverse is coming,” an article in its 2022 year-end issue. “[A]n internet which is still largely flat — based on two-dimensional text, images and video — is ripe for replacement with one that is three-dimensional and immersive.”

The Three Major Structural Changes in the Global Economy

“To say that the last few years have been economically turbulent would be a colossal understatement,” wrote economist and businessman Mohamed El-Erian in “Not Just Another Recession: Why the Global Economy May Never Be the Same,” a  recently published article in Foreign Affairs. El-Erian, — one of the world’s most influential economic thinkers, — is President of Queens College in Cambridge University, chief economic advisor at Allianz, and former CEO of PIMCO.

“Inflation has surged to its highest level in decades, and a combination of geopolitical tensions, supply chain disruptions, and rising interest rates now threatens to plunge the global economy into recession,” he added. “Yet for the most part, economists and financial analysts have treated these developments as outgrowths of the normal business cycle. But rather than one more turn of the economic wheel, the world may be experiencing major structural and secular changes that will outlast the current business cycle.”

As evidence of these changes, the article cites a series of highly unlikely economic and financial development that have taken place in the past few years. These include the strengthening of autocracies around the world, the political polarization in several liberal democracies, the increasing US turn from free trade to protectionism, and the growing tensions between China and the US.