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China’s path to modernization has, for centuries, gone through my hometown

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Nobel prize winners


In 1957, Yang and Tsung-Dao Lee, a fellow Chinese graduate of the University of Chicago, won the Nobel Prize for proposing that when some elementary particles decay, they do so in a way that distinguishes left from right. They were the first Chinese laureates. Speaking at the Nobel banquet, Yang noted that the prize had first been awarded in 1901, the same year as the Boxer Protocol. “As I stand here today and tell you about these, I am heavy with an awareness of the fact that I am in more than one sense a product of both the Chinese and Western cultures, in harmony and in conflict,” he said.

Yang became a US citizen in 1964 and moved to Stony Brook University on Long Island in 1966 as the founding director of its Institute for Theoretical Physics, which was later named after him. As the relationship between the US and China began to thaw, Yang visited his homeland in 1971—his first trip in a quarter of a century. A lot had changed. His father’s health was failing. The Cultural Revolution was raging, and both Western science and Chinese tradition had been deemed heresy. Many of Yang’s former colleagues, including Huang and Deng, were persecuted and forced to perform hard labor. The Nobel laureate, on the other hand, was received like a foreign dignitary. He met with officials at the highest levels of the Chinese government and advocated for the importance of basic research. 

In the years that followed, Yang visited China regularly. At first, his trips drew attention from the FBI, which saw exchanges with Chinese scientists as suspect. But by the late 1970s, hostilities had waned. Mao Zedong was dead. The Cultural Revolution was over. Beijing adopted reforms and opening-up policies. Chinese students could go abroad for study. Yang helped raise funding for Chinese scholars to come to the US and for international experts to travel to conferences in China, where he also helped establish new research centers. When Deng Jiaxian died in 1986, Yang wrote an emotional eulogy for his friend, who had devoted his life to China’s nuclear defense. It concluded with a song from 1906, one of his father’s favorites: “[T]he sons of China, they hold the sky aloft with a single hand … The crimson never fades from their blood spilled in the sand.” 

Yang (seated, left) with fellow Nobel Prize winners (clockwise from left) Val Fitch, James Cronin, Samuel C.C. Ting, and Isidor Isaac Rabi

ENERGY.GOV, PUBLIC DOMAIN, VIA WIKIMEDIA

Yang retired from Stony Brook in 1999 and moved back to China a few years later to teach freshman physics at Tsinghua. In 2015, he renounced his US citizenship and became a citizen of the People’s Republic of China. In an essay remembering his father, Yang recounted his earlier decision to emigrate. He wrote, “I know that until his final days, in a corner of his heart, my father never forgave me for abandoning my homeland.” 


In 2007, when he was 85 years old, Yang stopped by our hometown on an autumn day and gave a talk at my university. My roommates and I waited outside the venue hours in advance, earning precious seats in the packed auditorium. He took the stage to thunderous applause and delivered a presentation in English about his Nobel-winning work. I was a little perplexed by his choice of language. One of my roommates muttered, wondering whether Yang was too good to speak in his mother tongue. We listened attentively nevertheless, grateful to be in the same room as the great scientist. 

A college junior and physics major, I was preparing to apply to graduate school in the US. I’d been raised with the notion that the best of China would leave China. Two years after hearing Yang in person, I too enrolled at the University of Chicago. I received my PhD in 2015 and stayed in the US for postdoctoral research. 

Months before I bid farewell to my homeland, the central government launched its flagship overseas recruitment program, the Thousand Talents Plan, encouraging scientists and tech entrepreneurs to move to China with the promise of generous personal compensation and robust research funding. In the decade since, scores of similar programs have sprung up. Some, like Thousand Talents, are supported by the central government. Others are financed by local municipalities.

Beijing’s aggressive pursuit of foreign-trained talent is an indicator of the country’s new wealth and technological ambition. Though most of these programs are not exclusive to people of Chinese origin, the promotional materials routinely appeal to sentiments of national belonging, calling on the Chinese diaspora to come home. Bold red Chinese characters headlined the web page for the Thousand Talents Plan: “The motherland needs you. The motherland welcomes you. The motherland places her hope in you.” 

These days, though, the website isn’t accessible. Since 2020, mentions of the Thousand Talents Plan have largely disappeared from the Chinese internet. Though the program continues, its name is censored on search engines and forbidden in official documents in China. Since the final years of the Obama administration, the Chinese government’s overseas recruitment has come under intensifying scrutiny from US law enforcement. In 2018, the Justice Department started a China Initiative intended to combat economic espionage, with a focus on academic exchange between the two countries. The US government has also placed various restrictions on Chinese students, shortening their visas and denying access to facilities in disciplines deemed “sensitive.”

My mother is afraid that the borders between the US and China will be closed again as they were during the pandemic, shut down by forces just as invisible as a virus and even more deadly.

There are real problems of illicit behavior in Chinese talent programs. Earlier this year, a chemist associated with Thousand Talents was convicted in Tennessee of stealing trade secrets for BPA-free beverage can liners. A hospital researcher in Ohio pled guilty to stealing designs for exosome isolation used in medical diagnosis. Some US-based scientists failed to disclose additional income from China in federal grant proposals or on tax returns. All these are cases of individual greed or negligence. Yet the FBI considers them part of a “China threat” that demands a “whole-of-society” response. 

The Biden administration is reportedly considering changes to the China Initiative, which many science associations and civil rights groups have criticized as “racial profiling.” But no official announcements have been made. New cases have opened under Biden; restrictions on Chinese students remain in effect. 

Seen from China, the sanctions, prosecutions, and export controls imposed by the US look like continuations of foreign “bullying.” What has changed in the past 120 years is China’s status. It is now not a crumbling empire but a rising superpower. Policymakers in both countries use similar techno-nationalistic language to describe science as a tool of national greatness and scientists as strategic assets in geopolitics. Both governments are pursuing military use of technologies like quantum computing and artificial intelligence. 

“We do not seek conflict, but we welcome stiff competition,” National Security Advisor Jake Sullivan said at the Alaska summit. Yang Jiechi responded by arguing that past confrontations between the two countries had only damaged the US, while China pulled through. 

Much of the Chinese public relishes the prospect of competing against the US. Take a popular saying of Mao’s: “Those who fall behind will get beaten up!” The expression originated from a speech by Joseph Stalin, who stressed the importance of industrialization for the Soviet Union. For the Chinese public, largely unaware of its origins, it evokes the recent past, when a weak China was plundered by foreigners. When I was little, my mother often repeated the expression at home, distilling a century of national humiliation into a personal motivation for excellence. It was only later, in adulthood, that I began to question the underlying logic: Is a competition between nations meaningful? By what metric, and to what end?

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Deep learning can almost perfectly predict how ice forms

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Deep learning can almost perfectly predict how ice forms


Researchers have used deep learning techniques to model how ice crystals form in the atmosphere with much higher precision than ever before. Their paper, published this week in PNAS, hints at the potential for the new method to significantly increase the accuracy of weather and climate forecasting.

The researchers used deep learning to predict how atoms and molecules behave. First, deep learning models were trained on small-scale simulations of 64 water molecules to help them predict how electrons in atoms interact. The models then replicated those interactions on a larger scale, with more atoms and molecules. It’s this ability to precisely simulate electron interactions that allowed the team to accurately predict physical and chemical behavior. 

“The properties of matter emerge from how electrons behave,” says Pablo Piaggi, a research fellow at Princeton University and the lead author on the study. “Simulating explicitly what happens at that level is a way to capture much more rich physical phenomena.”

It’s the first time this method has been used to model something as complex as the formation of ice crystals, also known as ice nucleation. This development may eventually improve the accuracy of weather and climate forecasting, because the formation of ice crystals is one of the first steps in the formation of clouds, which is where all precipitation comes from. 

Xiaohong Liu, professor of atmospheric sciences at Texas A&M University, who was not involved in the study, says half of all precipitation events—whether it’s snow or rain or sleet—begin as ice crystals, which then grow larger and result in precipitation. If researchers can model ice nucleation more accurately, it could give a big boost to weather prediction overall.

Ice nucleation is currently predicted based on laboratory experiments. Researchers collect data on ice formation under different laboratory conditions, and that data is fed into weather prediction models under similar real-world conditions. This method works well enough sometimes, but often ends up being inaccurate because of the sheer number of variables in real-world conditions. If even a few factors vary between the lab and actual conditions, the results can be quite different.

“Your data is only valid for a certain region, temperature, or kind of laboratory setting,” Liu says.

Basing ice nucleation on how electrons interact is much more precise, but it’s also extremely computationally expensive. Predicting ice nucleation requires researchers to model at least 4000 to 100,000 water molecules, which even on supercomputers could take years to run. And even that would only be able to model the interactions for 100 picoseconds, or 10-10 seconds, not enough to observe the ice nucleation process.

Using deep learning, however, researchers were able to run the calculations in just 10 days. The time duration was also 1,000 times longer—still a fraction of a second, but just enough to see the ice nucleation process.

Of course, more accurate ice nucleation models alone won’t make weather forecasting perfect, says Liu. Ice nucleation is only a small but critical component of weather modeling. Other aspects, like understanding how water droplets and ice crystals grow, and how they move and interact together under different conditions, is also important.

Still, the ability to more accurately model how ice crystals form in the atmosphere would significantly improve weather predictions, especially whether it’s likely to rain or snow, and by how much. It could also improve climate forecasting by improving the ability to model clouds, which are vital players in the absorption of sunlight and abundance of greenhouse gasses.

Piaggi says future research could model ice nucleation when there are substances like smoke in the air, which can improve the accuracy of models even more. Because of deep learning techniques, it’s now possible to use electron interactions to model larger systems for longer periods of time.

“That has opened essentially a new field,” Piaggi says. “It’s already having and will have an even greater role in simulations in chemistry and in our simulations of materials.”

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How to craft effective AI policy

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How to craft effective AI policy


So to your first question, I think you’re right. That policy makers should actually define the guardrails, but I don’t think they need to do it for everything. I think we need to pick those areas that are most sensitive. The EU has called them high risk. And maybe we might take from that, some models that help us think about what’s high risk and where should we spend more time and potentially policy makers, where should we spend time together?

I’m a huge fan of regulatory sandboxes when it comes to co-design and co-evolution of feedback. Uh, I have an article coming out in an Oxford University press book on an incentive-based rating system that I could talk about in just a moment. But I also think on the flip side that all of you have to take account for your reputational risk.

As we move into a much more digitally advanced society, it is incumbent upon developers to do their due diligence too. You can’t afford as a company to go out and put an algorithm that you think, or an autonomous system that you think is the best idea, and then land up on the first page of the newspaper. Because what that does is it degrades the trustworthiness by your consumers of your product.

And so what I tell, you know, both sides is that I think it’s worth a conversation where we have certain guardrails when it comes to facial recognition technology, because we don’t have the technical accuracy when it applies to all populations. When it comes to disparate impact on financial products and services.There are great models that I’ve found in my work, in the banking industry, where they actually have triggers because they have regulatory bodies that help them understand what proxies actually deliver disparate impact. There are areas that we just saw this right in the housing and appraisal market, where AI is being used to sort of, um, replace a subjective decision making, but contributing more to the type of discrimination and predatory appraisals that we see. There are certain cases that we actually need policy makers to impose guardrails, but more so be proactive. I tell policymakers all the time, you can’t blame data scientists. If the data is horrible.

Anthony Green: Right.

Nicol Turner Lee: Put more money in R and D. Help us create better data sets that are overrepresented in certain areas or underrepresented in terms of minority populations. The key thing is, it has to work together. I don’t think that we’ll have a good winning solution if policy makers actually, you know, lead this or data scientists lead it by itself in certain areas. I think you really need people working together and collaborating on what those principles are. We create these models. Computers don’t. We know what we’re doing with these models when we’re creating algorithms or autonomous systems or ad targeting. We know! We in this room, we cannot sit back and say, we don’t understand why we use these technologies. We know because they actually have a precedent for how they’ve been expanded in our society, but we need some accountability. And that’s really what I’m trying to get at. Who’s making us accountable for these systems that we’re creating?

It’s so interesting, Anthony, these last few, uh, weeks, as many of us have watched the, uh, conflict in Ukraine. My daughter, because I have a 15 year old, has come to me with a variety of TikToks and other things that she’s seen to sort of say, “Hey mom, did you know that this is happening?” And I’ve had to sort of pull myself back cause I’ve gotten really involved in the conversation, not knowing that in some ways, once I go down that path with her. I’m going deeper and deeper and deeper into that well.

Anthony Green: Yeah.

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A bioengineered cornea can restore sight to blind people

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A bioengineered cornea can restore sight to blind people


One unexpected bonus was that the implant changed the shape of the cornea enough for its recipients to wear contact lenses for the best possible vision, even though they had been previously unable to tolerate them.

The cornea helps focus light rays on the retina at the back of the eye and protects the eye from dirt and germs. When damaged by infection or injury, it can prevent light from reaching the retina, making it difficult to see.

Corneal blindness is a big problem: around 12.7 million people are estimated to be affected by the condition, and cases are rising at a rate of around a million each year. Iran, India, China, and various countries in Africa have particularly high levels of corneal blindness, and specifically keratoconus.

Because pig skin is a by-product of the food industry, using this bioengineered implant should cost fraction as much as transplanting a human donor cornea, said Neil Lagali, a professor at the Department of Biomedical and Clinical Sciences at Linköping University, one of the researchers behind the study.

“It will be affordable, even to people in low-income countries,” he said. “There’s a much bigger cost saving compared to the way traditional corneal transplantation is being done today.”

The team is hoping to run a larger clinical trial of at least 100 patients in Europe and the US. In the meantime, they plan to kick-start the regulatory process required for the US Food and Drug Administration to eventually approve the device for the market.

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