A polyester-dissolving process could make modern clothing recyclable  

Less than 1% of clothing is recycled, and most of the rest ends up dumped in a landfill or burned. A team of researchers hopes to change that with a new process that breaks down mixed-fiber clothing into reusable, recyclable parts without any sorting or separation in advance. 

“We need a better way to recycle modern garments that are complex, because we are never going to stop buying clothes,” says Erha Andini, a chemical engineer at the University of Delaware and lead author of a study on the process, which is out today in Science Advances. “We are looking to create a closed-loop system for textile recycling.” 

Many garments are made of a mix of natural and synthetic fibers. Once these fibers are combined, they are difficult to separate. This presents a problem for recycling, which often needs textiles to be sorted into uniform categories, similar to how we sort glass, aluminum, and paper. 

To tackle this problem, Andini and her team used a solvent that breaks the chemical bonds in polyester fabric while leaving cotton and nylon intact. To speed up the process, they power it with microwave energy and add a zinc oxide catalyst. This combination reduces the breakdown time to 15 minutes, whereas traditional plastic recycling methods take over an hour. What the polyester ultimately breaks down into is BHET, an organic compound that can, in theory, be turned into polyester once more. While similar methods have been used to recycle pre-sorted plastic, this is the first time they’ve been used to recycle mixed-fiber textiles without any sorting required.   

COURTESY OF THE RESEARCHERS

In addition to speeding things up, the use of microwave energy also reduces the technique’s carbon footprint because it’s quicker and uses less energy, says Andini. 

Nevertheless, the process could be difficult to scale, says Bryan Vogt, a chemical engineer at Penn State University, who was not involved in the study. That’s because the solvent used to break down polyester is expensive and difficult to recover after use. Further, according to Andini, even though BHET is easily turned back into clothing, it’s less clear what to do with the leftover fibers. Nylon could be especially tricky, as the fabric is degraded significantly by the team’s chemical recycling technique. 

“We are chemical engineers, so we think of this process as a whole,” says Andini. “Hopefully, once we are able to get pure components from each part, we can transform them back into yarn and make clothes again.” 

Andini, who just received a fellowship for entrepreneurs, is developing a business plan to commercialize the process. In the coming years, she aims to launch a startup that will take the clothes recycling technique out of the lab and into the real world. That could be a significant step toward reducing the large amounts of textile waste in landfills. “It’ll be a matter of having the capital or not,” she says, “but we’re working on it and excited for it.” 

Toys can change your life

In a November 1984 story for Technology Review, Carolyn Sumners, curator of astronomy at the Houston Museum of Natural Science, described how toys, games, and even amusement park rides could change how young minds view science and math. “The Slinky,” Sumners noted, “has long served teachers as a medium for demonstrating longitudinal (soundlike) waves and transverse (lightlike) waves.” A yo-yo can be used as a gauge (a “yo-yo meter”) to observe the forces on a roller coaster. Marbles employ mass and velocity. Even a simple ball offers insights into the laws of gravity.

While Sumners focused on physics, she was onto something bigger. Over the last several decades, evidence has emerged that childhood play can shape our future selves: the skills we develop, the professions we choose, our sense of self-worth, and even our relationships.

That doesn’t mean we should foist “educational” toys like telescopes or tiny toolboxes on kids to turn them into astronomers or carpenters. As Sumners explained, even “fun” toys offer opportunities to discover the basic principles of physics. 

According to Jacqueline Harding, a child development expert and author of The Brain That Loves to Play, “If you invest time in play, which helps with executive functioning, decision-making, resilience—all those things—then it’s going to propel you into a much more safe, secure space in the future.”

Sumners was focused mostly on hard skills, the scientific knowledge that toys and games can foster. But there are soft skills, too, like creativity, problem-­solving, teamwork, and empathy. According to Harding, the less structure there is to such play—the fewer rules and goals—the more these soft skills emerge.

“The kinds of playthings, or play activities, that really produce creative thought,” she says, “are natural materials, with no defined end to them—like clay, paint, water, and mud—so that there is no right or wrong way of playing with it.” 

Playing is by definition voluntary, spontaneous, and goal-free; it involves taking risks, testing boundaries, and experimenting. The best kind of play results in joyful discovery, and along the way, the building blocks of innovation and personal development take shape. But in the decades since Sumners wrote her story, the landscape of play has shifted considerably. Recent research by the American Academy of Pediatrics’ Council on Early Childhood suggests that digital games and virtual play don’t appear to confer the same developmental benefits as physical games and outdoor play. 

“The brain loves the rewards that are coming from digital media,” says Harding. But in screen-based play, “you’re not getting that autonomy.” The lack of physical interaction also concerns her: “It is the quality of human face-to-face interaction, body proximity, eye-to-eye gaze, and mutual engagement in a play activity that really makes a difference.”

Bill Gourgey is a science writer based in Washington, DC.

Do you want to play a game?

For children, play comes so naturally. They don’t have to be encouraged to play. They don’t need equipment, or the latest graphics processors, or the perfect conditions—they just do it. What’s more, study after study has found that play has a crucial role in childhood growth and development. If you want to witness the absolute rapture of creative expression, just observe the unstructured play of children.

So what happens to us as we grow older? Children begin to compete with each other by age four or five. Play begins to transform from something we do purely for fun into something we use to achieve status and rank ourselves against other people. We play to score points. We play to win. 

And with that, play starts to become something different. Not that it can’t still be fun and joyful! Even watching other people play will bring us joy. We enjoy watching other people play so much and get so much joy by proxy from watching their achievements that we spend massive amounts of money to do so. According to StubHub, the average price of a ticket to the Super Bowl this year was $8,600. The average price for a Super Bowl ad was a cool $7 million this year, according to Ad Age. 

This kind of interest doesn’t just apply to physical games. Video-game streaming has long been a mainstay on YouTube, and entire industries have risen up around it. Top streamers on Twitch—Amazon’s livestreaming service, which is heavily gaming focused—earn upwards of $100,000 per month. And the global market for video games themselves is projected to bring in some $282 billion in revenue this year. 

Simply put, play is serious business. 

There are fortunes to be had in making our play more appealing, more accessible, more fun. All of the features in this issue dig in on the enormous amount of research and development that goes into making play “better.”  

On our cover this month is executive editor Niall Firth’s feature on the ways AI is going to upend game development. As you will read, we are about to enter the Wild West—Red Dead or not—of game character development. How will games change when they become less predictable and more fully interactive, thanks to AI-driven nonplayer characters who can not only go off script but even continue to play with each other when we’re not there? Will these even be games anymore, or will we simply be playing around in experiences? What kinds of parasocial relationships will we develop in these new worlds? It’s a fascinating read. 

There is no sport more intimately connected to the ocean, and to water, than surfing. It’s pure play on top of the waves. And when you hear surfers talk about entering the flow state, this is very much the same kind of state children experience at play—intensely focused, losing all sense of time and the world around them. Finding that flow no longer means living by the water’s edge, Eileen Guo reports. At surf pools all over the world, we’re piping water into (or out of) deserts to create perfect waves hundreds of miles from the ocean. How will that change the sport, and at what environmental cost? 

Just as we can make games more interesting, or bring the ocean to the desert, we have long pushed the limits of how we can make our bodies better, faster, stronger. Among the most recent ways we have done this is with the advent of so-called supershoes—running shoes with rigid carbon-fiber plates and bouncy proprietary foams. The late Kelvin Kiptum utterly destroyed the men’s world record for the marathon last year wearing a pair of supershoes made by Nike, clocking in at a blisteringly hot 2:00:35. Jonathan W. Rosen explores the science and technology behind these shoes and how they are changing the sport, especially in Kenya. 

There’s plenty more, too. So I hope you enjoy the Play issue. We certainly put a lot of work into it. But of course, what fun is play if you don’t put in the work?

Thanks for reading,

Mat Honan

The quest to type Chinese on a QWERTY keyboard created autocomplete

This is an excerpt from The Chinese Computer: A Global History of the Information Age by Thomas S. Mullaney, published on May 28 by The MIT Press. It has been lightly edited.

ymiw2

klt4

pwyy1

wdy6

o1

dfb2

wdv2

fypw3

uet5

dm2

dlu1 …

A young Chinese man sat down at his QWERTY keyboard and rattled off an enigmatic string of letters and numbers.

Was it code? Child’s play? Confusion? It was Chinese.

The beginning of Chinese, at least. These forty-four keystrokes marked the first steps in a process known as “input” or shuru: the act of getting Chinese characters to appear on a computer monitor or other digital device using a QWERTY keyboard or trackpad.

COURTESY OF MIT PRESS

Across all computational and digital media, Chinese text entry relies on software programs known as “Input Method Editors”—better known as “IMEs” or simply “input methods” (shurufa). IMEs are a form of “middleware,” so-named because they operate in between the hardware of the user’s device and the software of its program or application. Whether a person is composing a Chinese document in Microsoft Word, searching the web, sending text messages, or otherwise, an IME is always at work, intercepting all of the user’s keystrokes and trying to figure out which Chinese characters the user wants to produce. Input, simply put, is the way ymiw2klt4pwyy … becomes a string of Chinese characters.

IMEs are restless creatures. From the moment a key is depressed, or a stroke swiped, they set off on a dynamic, iterative process, snatching up user-inputted data and searching computer memory for potential Chinese character matches. The most popular IMEs these days are based on Chinese phonetics—that is, they use the letters of the Latin alphabet to describe the sound of Chinese characters, with mainland Chinese operators using the country’s official Romanization system, Hanyu pinyin. 

COURTESY OF MIT PRESS

This young man’s name was Huang Zhenyu (also known by his nom de guerre, Yu Shi). He was one of around sixty contestants that day, each wearing a bright red shoulder sash—like a tickertape parade of old, or a beauty pageant. “Love Chinese Characters” (Ai Hanzi) was emblazoned in vivid, golden yellow on a poster at the front of the hall. The contestants’ task was to transcribe a speech by outgoing Chinese president Hu Jintao, as quickly and as accurately as they could. “Hold High the Great Banner of Socialism with Chinese Characteristics,” it began, or in the original:  高举中国特色社会主义伟大旗帜为夺取全面建设小康社会新胜利而奋斗. Huang’s QWERTY keyboard did not permit him to enter these characters directly, however, and so he entered the quasi-gibberish string of letters and numbers instead: ymiw2klt4pwyy1wdy6…

With these four-dozen keystrokes, Huang was well on his way, not only to winning the 2013 National Chinese Characters Typing Competition, but also to clock one of the fastest typing speeds ever recorded, anywhere in the world.

ymiw2klt4pwyy1wdy6 … is not the same as 高举中国特色社会主义 …  the keys that Huang actually depressed on his QWERTY keyboard—his “primary transcript,” as we could call it—were completely different than the symbols that ultimately appeared on his computer screen, namely the “secondary transcript” of Hu Jintao’s speech. This is true for every one of the world’s billion-plus Sinophone computer users. In Chinese computing, what you type is never what you get.

For readers accustomed to English-language word processing and computing, this should come as a surprise. For example, were you to compare the paragraph you’re reading right now against a key log showing exactly which buttons I depressed to produce it, the exercise would be unenlightening (to put it mildly). “F-o-r-_-r-e-a-d-e-r-s-_-a-c-c-u-s-t-o-m-e-d-_t-o-_-E-n-g-l-i-s-h … ” it would read (forgiving any typos or edits). In English-language typewriting and computer input, a typist’s primary and secondary transcripts are, in principle, identical. The symbols on the keys and the symbols on the screen are the same.

Not so for Chinese computing. When inputting Chinese, the symbols a person sees on their QWERTY keyboard are always different from the symbols that ultimately appear on the monitor or on paper. Every single computer and new media user in the Sinophone world—no matter if they are blazing-fast or molasses-slow—uses their device in exactly the same way as Huang Zhenyu, constantly engaged in this iterative process of criteria-candidacy-confirmation, using one IME or another. Not some Chinese-speaking users, mind you, but all. This is the first and most basic feature of Chinese computing: Chinese human-computer interaction (HCI) requires users to operate entirely in code all the time.

If Huang Zhenyu’s mastery of a complex alphanumeric code weren’t impressive enough, consider the staggering speed of his performance. He transcribed the first 31 Chinese characters of Hu Jintao’s speech in roughly 5 seconds, for an extrapolated speed of 372 Chinese characters per minute. By the close of the grueling 20-minute contest, one extending over thousands of characters, he crossed the finish line with an almost unbelievable speed of 221.9 characters per minute.

That’s 3.7 Chinese characters every second.

In the context of English, Huang’s opening 5 seconds would have been the equivalent of around 375 English words-per-minute, with his overall competition speed easily surpassing 200 WPM—a blistering pace unmatched by anyone in the Anglophone world (using QWERTY, at least). In 1985, Barbara Blackburn achieved a Guinness Book of World Records–verified performance of 170 English words-per-minute (on a typewriter, no less). Speed demon Sean Wrona later bested Blackburn’s score with a performance of 174 WPM (on a computer keyboard, it should be noted). As impressive as these milestones are, the fact remains: had Huang’s performance taken place in the Anglophone world, it would be his name enshrined in the Guinness Book of World Records as the new benchmark to beat.

Huang’s speed carried special historical significance as well.

For a person living between the years 1850 and 1950—the period examined in the book The Chinese Typewriter—the idea of producing Chinese by mechanical means at a rate of over two hundred characters per minute would have been virtually unimaginable. Throughout the history of Chinese telegraphy, dating back to the 1870s, operators maxed out at perhaps a few dozen characters per minute. In the heyday of mechanical Chinese typewriting, from the 1920s to the 1970s, the fastest speeds on record were just shy of eighty characters per minute (with the majority of typists operating at far slower rates). When it came to modern information technologies, that is to say, Chinese was consistently one of the slowest writing systems in the world.

What changed? How did a script so long disparaged as cumbersome and helplessly complex suddenly rival—exceed, even—computational typing speeds clocked in other parts of the world? Even if we accept that Chinese computer users are somehow able to engage in “real time” coding, shouldn’t Chinese IMEs result in a lower overall “ceiling” for Chinese text processing as compared to English? Chinese computer users have to jump through so many more hoops, after all, over the course of a cumbersome, multistep process: the IME has to intercept a user’s keystrokes, search in memory for a match, present potential candidates, and wait for the user’s confirmation. Meanwhile, English-language computer users need only depress whichever key they wish to see printed on screen. What could be simpler than the “immediacy” of “Q equals Q,” “W equals W,” and so on?

COURTESY OF TOM MULLANEY

To unravel this seeming paradox, we will examine the first Chinese computer ever designed: the Sinotype, also known as the Ideographic Composing Machine. Debuted in 1959 by MIT professor Samuel Hawks Caldwell and the Graphic Arts Research Foundation, this machine featured a QWERTY keyboard, which the operator used to input—not the phonetic values of Chinese characters—but the brushstrokes out of which Chinese characters are composed. The objective of Sinotype was not to “build up” Chinese characters on the page, though, the way a user builds up English words through the successive addition of letters. Instead, each stroke “spelling” served as an electronic address that Sinotype’s logical circuit used to retrieve a Chinese character from memory. In other words, the first Chinese computer in history was premised on the same kind of “additional steps” as seen in Huang Zhenyu’s prizewinning 2013 performance.

During Caldwell’s research, he discovered unexpected benefits of all these additional steps—benefits entirely unheard of in the context of Anglophone human-machine interaction at that time. The Sinotype, he found, needed far fewer keystrokes to find a Chinese character in memory than to compose one through conventional means of inscription. By way of analogy, to “spell” a nine-letter word like “crocodile” (c-r-o-c-o-d-i-l-e) took far more time than to retrieve that same word from memory (“c-r-o-c-o-d” would be enough for a computer to make an unambiguous match, after all, given the absence of other words with similar or identical spellings). Caldwell called his discovery “minimum spelling,” making it a core part of the first Chinese computer ever built. 

Today, we know this technique by a different name: “autocompletion,” a strategy of human-computer interaction in which additional layers of mediation result in faster textual input than the “unmediated” act of typing. Decades before its rediscovery in the Anglophone world, then, autocompletion was first invented in the arena of Chinese computing.

Deepfakes of your dead loved ones are a booming Chinese business

Once a week, Sun Kai has a video call with his mother. He opens up about work, the pressures he faces as a middle-aged man, and thoughts that he doesn’t even discuss with his wife. His mother will occasionally make a comment, like telling him to take care of himself—he’s her only child. But mostly, she just listens.

That’s because Sun’s mother died five years ago. And the person he’s talking to isn’t actually a person, but a digital replica he made of her—a moving image that can conduct basic conversations. They’ve been talking for a few years now. 

After she died of a sudden illness in 2019, Sun wanted to find a way to keep their connection alive. So he turned to a team at Silicon Intelligence, an AI company based in Nanjing, China, that he cofounded in 2017. He provided them with a photo of her and some audio clips from their WeChat conversations. While the company was mostly focused on audio generation, the staff spent four months researching synthetic tools and generated an avatar with the data Sun provided. Then he was able to see and talk to a digital version of his mom via an app on his phone. 

“My mom didn’t seem very natural, but I still heard the words that she often said: ‘Have you eaten yet?’” Sun recalls of the first interaction. Because generative AI was a nascent technology at the time, the replica of his mom can say only a few pre-written lines. But Sun says that’s what she was like anyway. “She would always repeat those questions over and over again, and it made me very emotional when I heard it,” he says.

There are plenty of people like Sun who want to use AI to preserve, animate, and interact with lost loved ones as they mourn and try to heal. The market is particularly strong in China, where at least half a dozen companies are now offering such technologies and thousands of people have already paid for them. In fact, the avatars are the newest manifestation of a cultural tradition: Chinese people have always taken solace from confiding in the dead. 

The technology isn’t perfect—avatars can still be stiff and robotic—but it’s maturing, and more tools are becoming available through more companies. In turn, the price of “resurrecting” someone—also called creating “digital immortality” in the Chinese industry—has dropped significantly. Now this technology is becoming accessible to the general public. 

Some people question whether interacting with AI replicas of the dead is actually a healthy way to process grief, and it’s not entirely clear what the legal and ethical implications of this technology may be. For now, the idea still makes a lot of people uncomfortable. But as Silicon Intelligence’s other cofounder, CEO Sima Huapeng, says, “Even if only 1% of Chinese people can accept [AI cloning of the dead], that’s still a huge market.” 

AI resurrection

Avatars of the dead are essentially deepfakes: the technologies used to replicate a living person and a dead person aren’t inherently different. Diffusion models generate a realistic avatar that can move and speak. Large language models can be attached to generate conversations. The more data these models ingest about someone’s life—including photos, videos, audio recordings, and texts—the more closely the result will mimic that person, whether dead or alive.

China has proved to be a ripe market for all kinds of digital doubles. For example, the country has a robust e-commerce sector, and consumer brands hire many livestreamers to sell products. Initially, these were real people—but as MIT Technology Review reported last fall—many brands are switching to AI-cloned influencers that can stream 24/7. 

In just the past three years, the Chinese sector developing AI avatars has matured rapidly, says Shen Yang, a professor studying AI and media at Tsinghua University in Beijing, and replicas have improved from minutes-long rendered videos to 3D “live” avatars that can interact with people.  

This year, Sima says, has seen a tipping point, with AI cloning becoming affordable for most individuals. “Last year, it cost about $2,000 to $3,000, but it now only costs a few hundred dollars,” he says. That’s thanks to a price war between Chinese AI companies, which are fighting to meet the thriving demand for digital avatars in other sectors like streaming.

In fact, demand for applications that re-create the dead has also boosted the capabilities of tools that digitally replicate the living. 

Silicon Intelligence offers both services. When Sun and Sima launched the company, they were focused on using text-to-speech technologies to create audio and then using those AI-generated voices in applications such as robocalls.

But after the company replicated Sun’s mother, it pivoted to generating realistic avatars. That decision turned the company into one of the leading Chinese players creating AI-powered influencers. 

Its technology has generated avatars for hundreds of thousands of TikTok-like videos and streaming channels, but Sima says more recently it’s seen around 1,000 clients use it to replicate someone who’s passed away. “We started our work on ‘resurrection’ in 2019 and 2020,” he says, but at first people were slow to accept it: “No one wanted to be the first adopters.” 

The quality of the avatars has improved, he says, which has boosted adoption. When the avatar looks increasingly lifelike and gives fewer out-of-character answers, it’s easier for users to treat it as their deceased family member. Plus, the idea is getting popularized through more depictions on Chinese TV. 

Now Silicon Intelligence offers the replication service for a price between several hundred and several thousand dollars. The most basic product comes as an interactive avatar in an app, and the options at the upper end of the range often involve more customization and better hardware components, such as a tablet or a display screen. There are at least a handful more Chinese companies working on the same technology.

A modern twist on tradition

The business in these deepfakes builds on China’s long cultural history of communicating with the dead. 

In Chinese homes, it’s common to put up a portrait of a deceased relative for a few years after the death. Zhang Zewei, founder of a Shanghai-based company called Super Brain, says he and his team wanted to revamp that tradition with an “AI photo frame.” They create avatars of deceased loved ones that are pre-loaded onto an Android tablet, which looks like a photo frame when standing up. Clients can choose a moving image that speaks words drawn from an offline database or from an LLM. 

“In its essence, it’s not much different from a traditional portrait, except that it’s interactive,” Zhang says.

Zhang says the company has made digital replicas for over 1,000 clients since March 2023 and charges $700 to $1,400, depending on the service purchased. The company plans to release an app-only product soon, so that users can access the avatars on their phones, and could further reduce the cost to around $140.

The purpose of his products, Zhang says, is therapeutic. “When you really miss someone or need consolation during certain holidays, you can talk to the artificial living and heal your inner wounds,” he says.

And even if that conversation is largely one-sided, that’s in keeping with a strong cultural tradition. Every April during the Qingming festival, Chinese people sweep the tombs of their ancestors, burn joss sticks and fake paper money, and tell them what has happened in the past year. Of course, those conversations have always been one-way. 

But that’s not the case for all Super Brain services. The company also offers deepfaked video calls in which a company employee or a contract therapist pretends to be the relative who passed away. Using DeepFace, an open-source tool that analyzes facial features, the deceased person’s face is reconstructed in 3D and swapped in for the live person’s face with a real-time filter. 

SUPER BRAIN

At the other end of the call is usually an elderly family member who may not know that the relative has died—and whose family has arranged the conversation as a ruse. 

Jonathan Yang, a Nanjing resident who works in the tech industry, paid for this service in September 2023. His uncle died in a construction accident, but the family hesitated to tell Yang’s grandmother, who is 93 and in poor health. They worried that she wouldn’t survive the devastating news.

So Yang paid $1,350 to commission three deepfaked calls of his dead uncle. He gave Super Brain a handful of photos and videos of his uncle to train the model. Then, on three Chinese holidays, a Super Brain employee video-called Yang’s grandmother and told her, as his uncle, that he was busy working in a faraway city and wouldn’t be able to come back home, even during the Chinese New Year. 

“The effect has met my expectations. My grandma didn’t suspect anything,” Yang says. His family did have mixed opinions about the idea, because some relatives thought maybe she would have wanted to see her son’s body before it was cremated. Still, the whole family got on board in the end, believing the ruse would be best for her health. After all, it’s pretty common for Chinese families to tell “necessary” lies to avoid overwhelming seniors, as depicted in the movie The Farewell. 

To Yang, a close follower of the AI industry trends, creating replicas of the dead is one of the best applications of the technology. “It best represents the warmth [of AI],” he says. His grandmother’s health has improved, and there may come a day when they finally tell her the truth. By that time, Yang says, he may purchase a digital avatar of his uncle for his grandma to talk to whenever she misses him.

Is AI really good for grief? 

Even as AI cloning technology improves, there are some significant barriers preventing more people from using it to speak with their dead relatives in China. 

On the tech side, there are limitations to what AI models can generate. Most LLMs can handle dominant languages like Mandarin and Cantonese, but they aren’t able to replicate the many niche dialects in China. It’s also challenging—and therefore costly—to replicate body movements and complex facial expressions in 3D models. 

Then there’s the issue of training data. Unlike cloning someone who’s still alive, which often involves asking the person to record body movements or say certain things, posthumous AI replications must rely on whatever videos or photos are already available. And many clients don’t have high-quality data, or enough of it, for the end result to be satisfactory. 

Complicating these technical challenges are myriad ethical questions. Notably, how can someone who is already dead consent to being digitally replicated? For now, companies like Super Brain and Silicon Intelligence rely on the permission of direct family members. But what if family members disagree? And if a digital avatar generates inappropriate answers, who is responsible?

Similar technology caused controversy earlier this year. A company in Ningbo reportedly used AI tools to create videos of deceased celebrities and posted them on social media to speak to their fans. The videos were generated using public data, but without seeking any approval or permission. The result was intense criticism from the celebrities’ families and fans, and the videos were eventually taken down. 

“It’s a new domain that only came about after the popularization of AI: the rights to digital eternity,” says Shen, the Tsinghua professor, who also runs a lab that creates digital replicas of people who have passed away. He believes it should be prohibited to use deepfake technology to replicate living people without their permission. For people who have passed away, all of their immediate living family members must agree beforehand, he says. 

There could be negative effects on clients’ mental health, too. While some people, like Sun, find their conversations with avatars to be therapeutic, not everyone thinks it’s a healthy way to grieve. “The controversy lies in the fact that if we replicate our family members because we miss them, we may constantly stay in the state of mourning and can’t withdraw from it to accept that they have truly passed away,” says Shen. A widowed person who’s in constant conversation with the digital version of their partner might be held back from seeking a new relationship, for instance. 

“When someone passes away, should we replace our real emotions with fictional ones and linger in that emotional state?” Shen asks. Psychologists and philosophers who talked to MIT Technology Review about the impact of grief tech have warned about the danger of doing so. 

Sun Kai, at least, has found the digital avatar of his mom to be a comfort. She’s like a 24/7 confidante on his phone. Even though it’s possible to remake his mother’s avatar with the latest technology, he hasn’t yet done that. “I’m so used to what she looks like and sounds like now,” he says. As years have gone by, the boundary between her avatar and his memory of her has begun to blur. “Sometimes I couldn’t even tell which one is the real her,” he says.

And Sun is still okay with doing most of the talking. “When I’m confiding in her, I’m merely letting off steam. Sometimes you already know the answer to your question, but you still need to say it out loud,” he says. “My conversations with my mom have always been like this throughout the years.” 

But now, unlike before, he gets to talk to her whenever he wants to.