Category: Science
What should I ask Alison Gopnik?
Yes, I will be doing a Conversation with her. Here is Wikipedia:
Alison Gopnik (born June 16, 1955) is an American professor of psychology and affiliate professor of philosophy at the University of California, Berkeley. She is known for her work in the areas of cognitive and language development, specializing in the effect of language on thought, the development of a theory of mind, and causal learning. Her writing on psychology and cognitive science has appeared in Science, Scientific American, The Times Literary Supplement, The New York Review of Books, The New York Times, New Scientist, Slate and others. Her body of work also includes four books and over 100 journal articles…
Gopnik has carried out extensive work in applying Bayesian networks to human learning and has published and presented numerous papers on the topic…Gopnik was one of the first psychologists to note that the mathematical models also resemble how children learn.
Gopnik is known for advocating the “theory theory” which postulates that the same mechanisms used by scientists to develop scientific theories are used by children to develop causal models of their environment.
Here is her home page. So what should I ask her?
H1-B visa fees and the academic job market
Assume the courts do not strike this down (perhaps they will?).
Will foreigners still be hired at the entry level with an extra 100k surcharge? I would think not,as university budgets are tight these days. I presume there is some way to turn them down legally, without courting discrimination lawsuits?
What if you ask them to accept a lower starting wage? A different deal in some other manner, such as no summer money or a higher teaching load? Is that legal? Will schools have the stomach to even try? I would guess not. Is there a way to amortize the 100k over five or six years? What if the new hire leaves the institution in year three of the deal?
In economics at least, a pretty high percentage of the graduate students at top institutions do not have green cards or citizenships.
So how exactly is this going to work? There are not so many jobs in Europe, not enough to absorb those students even if they wish to work there. Will many drop out right now? And if the flow of graduate students is not replenished, given that entry into the US job market is now tougher, how many graduate programs will close up?
Will Chinese universities suddenly hire a lot more quality talent?
Here is some related discussion on Twitter.
As they say, solve for the equilibrium…
AI and weather tracking as a very positive intervention
India’s monsoon season was unusual this year, but many farmers there had new AI weather-forecasting tools to help them ride out the storms.
Google’s open-source artificial intelligence model NeuralGCM and the European Center for Medium-Range Weather Forecasts’s AI systems are making sophisticated and granular forecasting data available to even the smallest farms in poor areas. Thanks to the open-source AI, and decades of rainfall data, the Indian government sent out forecasts to 38 million farmers to warn them about looming monsoons.
The initiative to help farmers adapt is the latest example of how companies are expanding their weather-tracking capabilities amid mounting concerns about extreme weather and climate change.
The effort is part of a growing “democratization of weather forecasting,” said Pedram Hassanzadeh, a researcher at the University of Chicago who focuses on machine learning and extreme weather. Researchers from the university partnered with the Indian government to gather and send out the monsoon predictions.
“Up until very recently, to run a weather model, you needed a 100 million-dollar supercomputer,” said Olivia Graham, a product manager at Google Research. But now, farmers in India can make better-informed agricultural decisions quickly, she said.
These projects seem to have very high benefit to cost ratios. Here is one relevant RCT, here is another. Here is more from the WSJ, via Michael Kremer. Here is a useful and informative press release.
New print edition of Works in Progress
Amazing, here is the site: “The print edition contains everything you can get online, plus a lot more: print-exclusive columns, beautiful art and design, letters from our readers and editors, and visual explainers of technology.”
Celebrate Vishvakarma: A Holiday for Machines, Robots, and AI
Most holidays celebrate people, gods or military victories. Today is India’s Vishvakarma Puja, a celebration of machines. In India on this day, workers clean and honor their equipment and engineers pay tribute to Vishvakarma, the god of architecture, engineering and manufacturing.
Call it a celebration of Solow and a reminder that capital, not just labor, drives growth.
Capital today isn’t just looms and tractors—it’s robots, software, and AI. These are the new force multipliers, the machines that extend not only our muscles but our minds. To celebrate Vishvakarma is to celebrate tools, tool makers and the capital that makes us productive.
We have Labor Day for workers and Earth Day for nature. Viskvakarma Day is for the machines. So today don’t thank Mother Earth, thank the machines, reflect on their power and productivity and be grateful for all that they make possible. Capital is the true source of abundance.
Vishvakarma Day should be our national holiday for abundance and progress.
Hat tip: Nimai Mehta.
AI Agents for Economic Research
The objective of this paper is to demystify AI agents – autonomous LLM-based systems that plan, use tools, and execute multi-step research tasks – and to provide hands-on instructions for economists to build their own, even if they do not have programming expertise. As AI has evolved from simple chatbots to reasoning models and now to autonomous agents, the main focus of this paper is to make these powerful tools accessible to all researchers. Through working examples and step-by-step code, it shows how economists can create agents that autonomously conduct literature reviews across myriads of sources, write and debug econometric code, fetch and analyze economic data, and coordinate complex research workflows. The paper demonstrates that by “vibe coding” (programming through natural language) and building on modern agentic frameworks like LangGraph, any economist can build sophisticated research assistants and other autonomous tools in minutes. By providing complete, working implementations alongside conceptual frameworks, this guide demonstrates how to employ AI agents in every stage of the research process, from initial investigation to final analysis.
The Simple Mathematics of Chinese Innovation
The NYTimes has a good data-driven piece on How China Went From Clean Energy Copycat to Global Innovator, the upshot of which is that the old view of China as simply copying (“stealing” in some eyes) no longer describes reality. In some fields, including solar, batteries and hydrogen, China is now the leading innovator as measured by high-quality patents and scientific citations.
None of this should surprise anyone. China employs roughly 2.6 million full-time equivalent (FTE) researchers versus about 1.7 million in the United States. On a per-capita basis the U.S. is ahead—about 4,500 researchers per million people versus China’s 1,700—but population scale tips the balance. China simply has more researchers in absolute terms. If you frame it in terms of rare cognitive talent, as in my post on The Extreme Shortage of High IQ Workers—the arithmetic is even more striking: 1-in-1,000 workers (≈IQ 145) ~170,000 in the U.S. labor force and ~770,000 in China. Scale matters.
In the 20th century the world’s most populous countries were poor but that was neither the case historically nor will it be true in the 21st century. The standard of living in China remains well below that in the United States and China may never catch U.S. GDP per capita, but quantity is a quality of its own. More people means more ideas.
To be clear, the rise of China and India as scientific superpowers is not per se a threat. Whiners complain about US pharmaceutical R&D “subsidizing” the world. Well, Chinese pharmaceutical innovation is now saving American lives. Terrific. Ideas don’t stop at borders, and their spread raises living standards everywhere. It would be wonderful if an American cured cancer. It would be 99% as wonderful if a Chinese scientist did. What matters is that when more scientists attack the problem, the odds of a cure rise so we should look favorably on a world with more scientists. That is progress.
The danger is not China’s rise but America’s mindset. Treat science as zero-sum and every Chinese patent looks like a loss. But ideas are nonrival: a Chinese breakthrough doesn’t make Americans poorer, it makes the world richer. A multi-polar scientific world means faster growth, greater wealth, and accelerating technology—even if America wins a smaller share of the Nobels.
What determines business school faculty pay?
We examine the determinants of business school faculty pay, using detailed data on compensation, research, teaching, and administrative service. We estimate that a top-tier journal publication is worth $116,000, with significant variation across disciplines. Second-tier publications are worth one-third as much, and other publications have no impact. Further analysis of salaries and cross-discipline publication records suggests that researchers are compensated based on the journals they publish in rather than the departments they belong to. Conference presentations and teaching evaluations have significant but smaller effects than top-tier publications. Faculty administrators earn a premium, with department chairs receiving 11-35% and deans 58-94%. Post-Covid-19, real faculty pay has fallen more than in comparable fields and the sensitivity of pay to research performance has weakened.
That is from a new paper by Michelle Lowry, Daniel Bradley, April M. Knill, and Jared Williams. Via Arpit Gupta.
*How to be a Public Ambassador for Science*
The subtitle is The Scientist as Public Intellectual, and the author is my very good friend Jim Olds, who works at George Mason University. A very timely topic, here is one excerpt:
I was only about eight weeks into my new job. I’d been sworn in and found myself very much thrown into the pool’s deep end. First, the job was much more than serving as the National Science Foundation’s (NSF) lead for President Obama’s Brain Research through Advancing Innovative Neurotechnologies (BRAIN) project. Second, the learning curve was very steep. There were meetings full of acronyms that meant nothing to me. And these were my meetings — with my direct reports. I had learned the hard way that the Eisenhower Conference Center in the White House complex was made of steel and acted like a Faraday cage: cell phones didn’t work there. Tuesdays started with breakfast at 7:30 a.m. and went straight through for 12 hours with meeting after meeting.
Recommended, most of all informative about the NSF and also neuroscience.
The robustness reproducibility of the American Economic Review
We estimate the robustness reproducibility of key results from 17 non-experimental AER papers published in 2013 (8 papers) and 2022/23 (9 papers). We then subject each robustness report to two independent, expert reviews. Including robustness tests rated as equally or more valid than the original analyses by expert reviewers, the fraction of significant robustness tests (p<0.05) varies between 0% and 93% across papers with a mean of 51%. The mean relative t/z-value of our robustness tests varies between 11% and 152% with a mean of 70%. Surveyed economists overestimate robustness but are able to predict which papers are most robust.
That is from a new paper by Douglas Campbell, Abel Brodeur, Anna Dreber, Magnus Johannesson, Joseph Kopecky, Lester Lusher, and Nikita Tsoy. Here is very useful Twitter coverage from Douglas Campbell.
AI and the Detection of Gravity Waves
Researchers at the Laser Interferometer Gravitational-Wave Observatory, the giant two-observatory machine to detect gravitational waves, developed an AI to improve the sensitivity of the design:
Wired: Initially, the AI’s designs seemed outlandish. “The outputs that the thing was giving us were really not comprehensible by people,” Adhikari said. “They were too complicated, and they looked like alien things or AI things. Just nothing that a human being would make, because it had no sense of symmetry, beauty, anything. It was just a mess.”
The researchers figured out how to clean up the AI’s outputs to produce interpretable ideas. Even so, the researchers were befuddled by the AI’s design. “If my students had tried to give me this thing, I would have said, ‘No, no, that’s ridiculous,’” Adhikari said. But the design was clearly effective.
It took months of effort to understand what the AI was doing. It turned out that the machine had used a counterintuitive trick to achieve its goals. It added an additional three-kilometer-long ring between the main interferometer and the detector to circulate the light before it exited the interferometer’s arms. Adhikari’s team realized that the AI was probably using some esoteric theoretical principles that Russian physicists had identified decades ago to reduce quantum mechanical noise. No one had ever pursued those ideas experimentally. “It takes a lot to think this far outside of the accepted solution,” Adhikari said. “We really needed the AI.”
…If the AI’s insights had been available when LIGO was being built, “we would have had something like 10 or 15 percent better LIGO sensitivity all along,” he said. In a world of sub-proton precision, 10 to 15 percent is enormous.
As with AlphaGO and Move 37 the AI developed entirely novel approaches:
“LIGO is this huge thing that thousands of people have been thinking about deeply for 40 years,” said Aephraim Steinberg, an expert on quantum optics at the University of Toronto. “They’ve thought of everything they could have, and anything new [the AI] comes up with is a demonstration that it’s something thousands of people failed to do.”
Bird trivia
Potvin’s team dissected and examined the bodies of nearly 500 birds belonging to five common Australian species: the Australian magpie, laughing kookaburra, crested pigeon, rainbow lorikeet, and the scaly breasted lorikeet(…)In addition to identifying the birds’ reproductive organs, researchers also tested their DNA to reveal their genetic sex.
The team was surprised to find sex-reversed individuals in all five species, at rates of 3% to 6%. Nearly all these discordant birds were genetically female but had male reproductive organs. However, the researchers also found a few genetic males with ovaries—including a genetically male kookaburra with a distended oviduct, indicating it had recently laid an egg(…)
Here is the full article by Phie Jacobs. Via John.
The Rising Returns to R&D: Ideas Are not Getting Harder to Find (one hypothesis)
R&D investment has grown robustly, yet aggregate productivity growth has stagnated. Is this because “ideas are getting harder to find”? This paper uses micro-data from the US Census Bureau to explore the relationship between R&D and productivity in the manufacturing sector from 1976 to 2018. We find that both the elasticity of output (TFP) with respect to R&D and the marginal returns to R&D have risen sharply. Exploring factors affecting returns, we conclude that R&D obsolescence rates must have risen. Using a novel estimation approach, we find consistent evidence of sharply rising technological rivalry and obsolescence. These findings suggest that R&D has become more effective at finding productivity-enhancing ideas, but these ideas may also render rivals’ technologies obsolete, making innovations more transient. Because of obsolescence, rising R&D does not necessarily mean rising aggregate productivity growth.
Here is the paper by Yoshiki Ando (Singapore Management University, TPRI), James Bessen (BU, TPRI), and Xiupeng Wang. Via Arjun.
New data on tenure
Tenure is a defining feature of the US academic system with significant implications for research productivity and creative search. Yet the impact of tenure on faculty research trajectories remains poorly understood. We analyze the careers of 12,000 US faculty across 15 disciplines to reveal key patterns, pre- and post-tenure. Publication rates rise sharply during the tenure-track, peaking just before tenure. However, post-tenure trajectories diverge: Researchers in lab-based fields sustain high output, while those in non-lab-based fields typically exhibit a decline. After tenure, faculty produce more novel works, though fewer highly cited papers. These findings highlight tenure’s pivotal role in shaping scientific careers, offering insights into the interplay between academic incentives, creativity, and impact while informing debates about the academic system.
Here is the paper. That is by Giorgio Tripodi, Ziang Zheng, Yifan Qian, and Dashun Wang, via the excellent Kevin Lewis.
Genius, Rejected: Emergent Ventures Versus the System
Quanta Magazine has a good piece on a 17-year-old student who disproved a long-standing conjecture in harmonic analysis:
Yet a paper posted on February 10(opens a new tab) left the math world by turns stunned, delighted and ready to welcome a bold new talent into its midst. Its author was Hannah Cairo(opens a new tab), just 17 at the time. She had solved a 40-year-old mystery about how functions behave, called the Mizohata-Takeuchi conjecture.
“We were all shocked, absolutely. I don’t remember ever seeing anything like that,” said Itamar Oliveira (opens aof the University of Birmingham, who has spent the past two years trying to prove that the conjecture was true. In her paper, Cairo showed that it’s false. The result defies mathematicians’ usual intuitions about what functions can and cannot do.
…The proof, and its unlikely author, have energized the math community since Cairo posted it in February. “I was absolutely, ‘Wow.’ This has been my favorite problem for nigh on 40 years, and I was completely blown away,” Carbery said.
Here is the abstract to the paper:
I can’t speak to the mathematics but this is Quanta Magazine not People Magazine and Cairo is not coming out of nowhere. As the article discusses, she has been taking graduate classes in mathematics at Berkeley from people like Ruixiang Zhang. So what is the problem?
I was enraged by the following:
After completing the proof, she decided to apply straight to graduate school, skipping college (and a high school diploma) altogether. As she saw it, she was already living the life of a graduate student. Cairo applied to 10 graduate programs. Six rejected her because she didn’t have a college degree. Two admitted her, but then higher-ups in those universities’ administrations overrode those decisions.
Only the University of Maryland and Johns Hopkins University were willing to welcome her straight into a doctoral program.
Kudos to UMD and JHU! But what is going on at those other universities?!! Their sole mission is to identify and nurture talent. They have armies of admissions staff and tout their “holistic” approach to recognizing creativity and intellectual promise even when it follows an unconventional path. Yet they can’t make room for a genius who has been vetted by some of the top mathematicians in the world? This is institutional failure.
We saw similar failures during COVID: researchers at Yale’s School of Public Health, working on new tests, couldn’t get funding from their own billion-dollar institution and would have stalled without Tyler’s Fast Grants. But the problem isn’t just speed. Emergent Ventures isn’t about speed but about discovering talent. If you wonder why EV has been so successful look to Tyler and people like Shruti Rajagopalan and to the noble funders but look also to the fact that their competitors are so bureaucratic that they can’t recognize talent even when it is thrust upon them.
It’s a very good thing EV exists. But you know your city is broken when you need Batman to fight crime. EV will have truly succeeded when the rest of the system is inspired into raising its game.