Irving Wladawsky-Berger

Are GPT-3 and ChatGPT Truly Intelligent?

Large language models (LLMs) — e.g., BERT, GPT-3, — and chatbots like ChatGPT, are quite impressive. In the coming years, they will likely lead to a number of transformative tools and applications. They may well herald a coming AI revolution. But, how much closer are they getting us to the kind of general intelligence that, for the foreseeable future, only humans have? How can we best sort out their true significance and value from the accompanying hype? Let me discuss how two different articles addressed these questions.

“The success of the large neural language models on many NLP [Natural Language Processing] tasks is exciting” wrote linguistic professors Emiliy Bender and Alexander Koller in their 2020 paper “Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data.” “This has led to claims, in both academic and popular publications, that such models understand or comprehend natural language or learn its meaning. From our perspective, these are overclaims caused by a misunderstanding of the relationship between linguistic form and meaning.”

In their paper, Bender and Koller explain why LLMs like GPT-3 are likely to become innovative language tools, — kind of like highly advanced spell checkers or word processors, — while dismissing claims that they have the ability to reason and understand the meaning of the language they’re generating. Their explanations are couched in linguistic concepts which they carefully define: form, communicative intent, meaning, and understanding.

Management Priorities for Highly Turbulent Times

“The COVID-19 pandemic rewrote the rules, and now a new and potent disruption seems to arrive every other day,” said a recent McKinsey article, What matters most? Six priorities for CEOs in turbulent times. “You know the list of issues; we won’t go through them here. Suffice to say that managing complex organizations is much harder today than it was just a few years ago. And the hardest task of all for CEOs is to decide what needs to be done now and what can wait.”

To find out what matters most almost three years into the pandemic, McKinsey spoke with hundreds of leading CEOs around the world. Six priorities featured prominently on their agendas:

• Resilience. Not surprisingly, “resilience is emerging as a vital muscle for companies operating in a world of endless volatility and disruption.”
• Strategic Courage. “The best leaders and companies are ambidextrous: prudent about managing the downside while courageously pursuing the upside.”
• Building New Businesses. “Going for greatness within a company’s industry is one thing. Venturing into an entirely different sector is another, more complicated story.”
• Technology. “Building a new business inevitably means new and better technology.” 
• Net-zero emissions. “[T]he goals of sustainability, economic competitiveness, affordability, and national security dovetail as never before.”
• Rebuild the employee experience. “In the wake (we hope) of the COVID-19 pandemic, CEOs need to find a new plane of engagement.”

Can an AI System Exhibit Commonsense Intelligence?

“One of the fundamental limitations of AI can be characterized as its lack of commonsense intelligence: the ability to reason intuitively about everyday situations and events, which requires rich background knowledge about how the physical and social world works,” wrote University of Washington professor Yejin Choi in “The Curious Case of Commonsense Intelligence,” an essay published in the Spring 2022 issue of Dædalus. “Trivial for humans, acquiring commonsense intelligence has been considered a nearly impossible goal in AI, added Choi.”

What is commonsense intelligence? In a 2020 presentation, Choi defined common sense as “the basic level of practical knowledge and reasoning concerning everyday situations and events that are commonly shared among most people.” Common sense is essential for humans “to live and interact with each other in a reasonable and safe way,” and for an AI system, common sense is essential “to understand humans needs and actions better.” She further explained the difference between intuitive commonsense reasoning and analytical rational reasoning by discussing the pioneering research of Daniel Kahneman, — Princeton Professor Emeritus and recipient of the 2002 Nobel Prize in Economics, — and his long time collaborator Amos Tversky, who died in 1996.

In his excellent 2011 bestseller Thinking, Fast and Slow, Kahneman explained that our mind is composed of two very different systems of thinking, System 1 and System 2.  System 1 is the intuitive, unconscious, fast, and effortless part of the mind. Thoughts come automatically and very quickly to System 1 without us doing anything to make them happen.

Are Business Schools to Blame for Wage Stagnation?

Several weeks ago I listened to a very interesting podcast, Are MBAs to Blame for Wage Stagnation, with Freakonomics Radio host, Stephen Dubner and MIT economist Daron Acemoglu. The podcast discussed a working paper by Acemoglu, Alex He, and Daniel le Maire on the effect of a CEO’s education on the wages of their company’s employees that was recently published in the National Bureau of Economic Research (NBER).

In the podcast, Acemoglu noted that over the last few decades, wages have grown much slower than productivity, — a development that has long puzzled economists. “If you look at the real wages of workers with a high school degree, it increased about 2.5% a year in real terms between1945 and the late 1970s — a remarkable increase,” he said. “Since 1980, it has been declining in real terms every year. So a large fraction of the U.S. population has been becoming poorer and poorer, even as we are building the most amazing technologies, the largest companies humanity has seen, the most modern economy.”

The labor share, — that is, the share of GDP that goes to workers, — declined from 63% in the 1980s to 58% in 2020. And during those same four decades, the share of US firms that were run by a business manager, — that is, an executive with an MBA or some other business degree, — increased from 25% in the 1980s to over 40% in 2020. Were these two trends connected? “Did the professionalization of the managerial class cause the labor share to fall? The answer wasn’t obvious.”

To shed light on this question, Acemoglu and his collaborators analyzed US and Danish data, including firm- and worker-level information. They chose Denmark, in addition to the US, because the country has very high-quality data that makes it possible to do a lot of analysis that would be harder to do with US data alone. Their methodology was very simple. They essentially tracked what happens to the earnings of the same workers when there is a switch in the type of manager leading their company, i.e., from a business to a non-business manager and vice versa.