PsiQuantum plans to build the biggest quantum computing facility in the US

The quantum computing firm PsiQuantum is partnering with the state of Illinois to build the largest US-based quantum computing facility, the company announced today. 

The firm, which has headquarters in California, says it aims to house a quantum computer containing up to 1 million quantum bits, or qubits, within the next 10 years. At the moment, the largest quantum computers have around 1,000 qubits. 

Quantum computers promise to do a wide range of tasks, from drug discovery to cryptography, at record-breaking speeds. Companies are using different approaches to build the systems and working hard to scale them up. Both Google and IBM, for example, make the qubits out of superconducting material. IonQ makes qubits by trapping ions using electromagnetic fields. PsiQuantum is building qubits from photons.  

A major benefit of photonic quantum computing is the ability to operate at higher temperatures than superconducting systems. “Photons don’t feel heat and they don’t feel electromagnetic interference,” says Pete Shadbolt, PsiQuantum’s cofounder and chief scientific officer. This imperturbability makes the technology easier and cheaper to test in the lab, Shadbolt says. 

It also reduces the cooling requirements, which should make the technology more energy efficient and easier to scale up. PsiQuantum’s computer can’t be operated at room temperature, because it needs superconducting detectors to locate photons and perform error correction. But those sensors only need to be cooled to a few degrees Kelvin, or a little under -450 °F. While that’s an icy temperature, it is still easier to achieve than what’s required for superconducting systems, which demand cryogenic cooling. 

The company has opted not to build small-scale quantum computers (such as IBM’s Condor, which uses a little over 1,100 qubits). Instead it is aiming to manufacture and test what it calls “intermediate systems.” These include chips, cabinets, and superconducting photon detectors. PsiQuantum says it is targeting these larger-scale systems in part because smaller devices are unable to adequately correct errors and operate at a realistic price point.  

Getting smaller-scale systems to do useful work has been an area of active research. But “just in the last few years, we’ve seen people waking up to the fact that small systems are not going to be useful,” says Shadbolt. In order to adequately correct the inevitable errors, he says, “you have to build a big system with about a million qubits.” The approach conserves resources, he says, because the company doesn’t spend time piecing together smaller systems. But skipping over them makes PsiQuantum’s technology difficult to compare to what’s already on the market. 

The company won’t share details about the exact timeline of the Illinois project, which will include a collaboration with the University of Chicago, and several other Illinois universities. It does say it is hoping to break ground on a similar facility in Brisbane, Australia, next year and hopes that facility, which will house its own large-scale quantum computer, will be fully operational by 2027. “We expect Chicago to follow thereafter in terms of the site being operational,” the company said in a statement. 

“It’s all or nothing [with PsiQuantum], which doesn’t mean it’s invalid,” says Christopher Monroe, a computer scientist at Duke University and ex-IonQ employee. “It’s just hard to measure progress along the way, so it’s a very risky kind of investment.”

Significant hurdles lie ahead. Building the infrastructure for this facility, particularly for the cooling system, will be the slowest and most expensive aspect of the construction. And when the facility is finally constructed, there will need to be improvements in the quantum algorithms run on the computers. Shadbolt says the current algorithms are far too expensive and resource intensive. 

The sheer complexity of the construction project might seem daunting. “This could be the most complex quantum optical electronic system humans have ever built, and that’s hard,” says Shadbolt. “We take comfort in the fact that it resembles a supercomputer or a data center, and we’re building it using the same fabs, the same contract manufacturers, and the same engineers.”

Correction: we have updated the story to reflect that the partnership is only with the state of Illinois and its universities, and not a national lab

Update: we added comments from Christopher Monroe

How to fix a Windows PC affected by the global outage

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more here.

Windows PCs have crashed in a major IT outage around the world, bringing airlines, major banks, TV broadcasters, health-care providers, and other businesses to a standstill.

Airlines including United, Delta, and American have been forced to ground and delay flights, stranding passengers in airports, while the UK broadcaster Sky News was temporarily pulled off air. Meanwhile, banking customers in Europe, Australia, and India have been unable to access their online accounts. Doctor’s offices and hospitals in the UK have lost access to patient records and appointment scheduling systems. 

The problem stems from a defect in a single content update for Windows machines from the cybersecurity provider CrowdStrike. George Kurtz, CrowdStrike’s CEO, says that the company is actively working with customers affected.

“This is not a security incident or cyberattack,” he said in a statement on X. “The issue has been identified, isolated and a fix has been deployed. We refer customers to the support portal for the latest updates and will continue to provide complete and continuous updates on our website.” CrowdStrike pointed MIT Technology Review to its blog with additional updates for customers.

What caused the issue?

The issue originates from a faulty update from CrowdStrike, which has knocked affected servers and PCs offline and caused some Windows workstations to display the “blue screen of death” when users attempt to boot them. Mac and Linux hosts are not affected.

The update was intended for CrowdStrike’s Falcon software, which is “endpoint detection and response” software designed to protect companies’ computer systems from cyberattacks and malware. But instead of working as expected, the update caused computers running Windows software to crash and fail to reboot. Home PCs running Windows are less likely to have been affected, because CrowdStrike is predominantly used by large organizations. Microsoft did not immediately respond to a request for comment.

“The CrowdStrike software works at the low-level operating system layer. Issues at this level make the OS not bootable,” says Lukasz Olejnik, an independent cybersecurity researcher and consultant, and author of Philosophy of Cybersecurity.

Not all computers running Windows were affected in the same way, he says, pointing out that if a machine’s systems had been turned off at the time CrowdStrike pushed out the update (which has since been withdrawn), it wouldn’t have received it.

For the machines running systems that received the mangled update and were rebooted, an automated update from CloudStrike’s server management infrastructure should suffice, he says.

“But in thousands or millions of cases, this may require manual human intervention,” he adds. “That means a really bad weekend ahead for plenty of IT staff.”

How to manually fix your affected computer

There is a known workaround for Windows computers that requires administrative access to its systems. If you’re affected and have that high level of access, CrowdStrike has recommended the following steps:

1. Boot Windows into safe mode or the Windows Recovery Environment.

2. Navigate to the C:WindowsSystem32driversCrowdStrike directory.

3. Locate the file matching “C-00000291*.sys” and delete it.

4. Boot the machine normally.

Sounds simple, right? But while the above fix is fairly easy to administer, it requires someone to enter it physically, meaning IT teams will need to track down remote machines that have been affected, says Andrew Dwyer of the Department of Information Security at Royal Holloway, University of London.

“We’ve been quite lucky that this is an outage and not an exploitation by a criminal gang or another state,” he says. “It also shows how easy it is to inflict quite significant global damage if you get into the right part of the IT supply chain.”

While fixing the problem is going to cause headaches for IT teams for the next week or so, it’s highly unlikely to cause significant long-term damage to the affected systems—which would not have been the case if it had been ransomware rather than a bungled update, he says.

“If this was a piece of ransomware, there could have been significant outages for months,” he adds. “Without endpoint detection software, many organizations would be in a much more vulnerable place. But they’re critical nodes in the system that have a lot of access to the computer systems that we use.”

Unlocking secure, private AI with confidential computing

All of a sudden, it seems that AI is everywhere, from executive assistant chatbots to AI code assistants.

But despite the proliferation of AI in the zeitgeist, many organizations are proceeding with caution. This is due to the perception of the security quagmires AI presents. For the emerging technology to reach its full potential, data must be secured through every stage of the AI lifecycle including model training, fine-tuning, and inferencing.

This is where confidential computing comes into play. Vikas Bhatia, head of product for Azure Confidential Computing at Microsoft, explains the significance of this architectural innovation: “AI is being used to provide solutions for a lot of highly sensitive data, whether that’s personal data, company data, or multiparty data,” he says. “Confidential computing is an emerging technology that protects that data when it is in memory and in use. We see a future where model creators who need to protect their IP will leverage confidential computing to safeguard their models and to protect their customer data.”

Understanding confidential computing

“The tech industry has done a great job in ensuring that data stays protected at rest and in transit using encryption,” Bhatia says. “Bad actors can steal a laptop and remove its hard drive but won’t be able to get anything out of it if the data is encrypted by security features like BitLocker. Similarly, nobody can run away with data in the cloud. And data in transit is secure thanks to HTTPS and TLS, which have long been industry standards.”

But data in use, when data is in memory and being operated upon, has typically been harder to secure. Confidential computing addresses this critical gap—what Bhatia calls the “missing third leg of the three-legged data protection stool”—via a hardware-based root of trust.

Essentially, confidential computing ensures the only thing customers need to trust is the data running inside of a trusted execution environment (TEE) and the underlying hardware. “The concept of a TEE is basically an enclave, or I like to use the word ‘box.’ Everything inside that box is trusted, anything outside it is not,” explains Bhatia.

Until recently, confidential computing only worked on central processing units (CPUs). However, NVIDIA has recently brought confidential computing capabilities to the H100 Tensor Core GPU and Microsoft has made this technology available in Azure. This has the potential to protect the entire confidential AI lifecycle—including model weights, training data, and inference workloads.

“Historically, devices such as GPUs were controlled by the host operating system, which, in turn, was controlled by the cloud service provider,” notes Krishnaprasad Hande, Technical Program Manager at Microsoft. “So, in order to meet confidential computing requirements, we needed technological improvements to reduce trust in the host operating system, i.e., its ability to observe or tamper with application workloads when the GPU is assigned to a confidential virtual machine, while retaining sufficient control to monitor and manage the device. NVIDIA and Microsoft have worked together to achieve this.”

Attestation mechanisms are another key component of confidential computing. Attestation allows users to verify the integrity and authenticity of the TEE, and the user code within it, ensuring the environment hasn’t been tampered with. “Customers can validate that trust by running an attestation report themselves against the CPU and the GPU to validate the state of their environment,” says Bhatia.

Additionally, secure key management systems play a critical role in confidential computing ecosystems. “We’ve extended our Azure Key Vault with Managed HSM service which runs inside a TEE,” says Bhatia. “The keys get securely released inside that TEE such that the data can be decrypted.”

Confidential computing use cases and benefits

GPU-accelerated confidential computing has far-reaching implications for AI in enterprise contexts. It also addresses privacy issues that apply to any analysis of sensitive data in the public cloud. This is of particular concern to organizations trying to gain insights from multiparty data while maintaining utmost privacy.

Another of the key advantages of Microsoft’s confidential computing offering is that it requires no code changes on the part of the customer, facilitating seamless adoption. “The confidential computing environment we’re building does not require customers to change a single line of code,” notes Bhatia. “They can redeploy from a non-confidential environment to a confidential environment. It’s as simple as choosing a particular VM size that supports confidential computing capabilities.”

Some industries and use cases that stand to benefit from confidential computing advancements include:

  • Governments and sovereign entities dealing with sensitive data and intellectual property.
  • Healthcare organizations using AI for drug discovery and doctor-patient confidentiality.
  • Banks and financial firms using AI to detect fraud and money laundering through shared analysis without revealing sensitive customer information.
  • Manufacturers optimizing supply chains by securely sharing data with partners.

Further, Bhatia says confidential computing helps facilitate data “clean rooms” for secure analysis in contexts like advertising. “We see a lot of sensitivity around use cases such as advertising and the way customers’ data is being handled and shared with third parties,” he says. “So, in these multiparty computation scenarios, or ‘data clean rooms,’ multiple parties can merge in their data sets, and no single party gets access to the combined data set. Only the code that is authorized will get access.”

The current state—and expected future—of confidential computing

Although large language models (LLMs) have captured attention in recent months, enterprises have found early success with a more scaled-down approach: small language models (SLMs), which are more efficient and less resource-intensive for many use cases. “We can see some targeted SLM models that can run in early confidential GPUs,” notes Bhatia.

This is just the start. Microsoft envisions a future that will support larger models and expanded AI scenarios—a progression that could see AI in the enterprise become less of a boardroom buzzword and more of an everyday reality driving business outcomes. “We’re starting with SLMs and adding in capabilities that allow larger models to run using multiple GPUs and multi-node communication. Over time, [the goal is eventually] for the largest models that the world might come up with could run in a confidential environment,” says Bhatia.

Bringing this to fruition will be a collaborative effort. Partnerships among major players like Microsoft and NVIDIA have already propelled significant advancements, and more are on the horizon. Organizations like the Confidential Computing Consortium will also be instrumental in advancing the underpinning technologies needed to make widespread and secure use of enterprise AI a reality.

“We’re seeing a lot of the critical pieces fall into place right now,” says Bhatia. “We don’t question today why something is HTTPS. That’s the world we’re moving toward [with confidential computing], but it’s not going to happen overnight. It’s certainly a journey, and one that NVIDIA and Microsoft are committed to.”

Microsoft Azure customers can start on this journey today with Azure confidential VMs with NVIDIA H100 GPUs. Learn more here.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

Housetraining robot dogs: How generative AI might change consumer IoT

As technology goes, the internet of things (IoT) is old: internet-connected devices outnumbered people on Earth around 2008 or 2009, according to a contemporary Cisco report. Since then, IoT has grown rapidly. Researchers say that by the early 2020s, estimates of the number of devices ranged anywhere from the low tens of billions to over 50 billion.

Currently, though, IoT is seeing unusually intense new interest for a long-established technology, even one still experiencing market growth. A sure sign of this buzz is the appearance of acronyms, such as AIoT and GenAIoT, or “artificial intelligence of things” and “generative artificial intelligence of things.”

What is going on? Why now? Examining potential changes to consumer IoT could provide some answers. Specifically, the vast range of areas where the technology finds home and personal uses, from smart home controls through smart watches and other wearables to VR gaming—to name just a handful. The underlying technological changes sparking interest in this specific area mirror those in IoT as a whole.

Rapid advances converging at the edge

IoT is much more than a huge collection of “things,” such as automated sensing devices and attached actuators to take limited actions. These devices, of course, play a key role. A recent IDC report estimated that all edge devices—many of them IoT ones—account for 20% of the world’s current data generation.

IoT, however, is much more. It is a huge technological ecosystem that encompasses and empowers these devices. This ecosystem is multi-layered, although no single agreed taxonomy exists.

Most analyses will include among the strata the physical devices themselves (sensors, actuators, and other machines with which these immediately interact); the data generated by these devices; the networking and communication technology used to gather and send the generated data to, and to receive information from, other devices or central data stores; and the software applications that draw on such information and other possible inputs, often to suggest or make decisions.

The inherent value from IoT is not the data itself, but the capacity to use it in order to understand what is happening in and around the devices and, in turn, to use these insights, where necessary, to recommend that humans take action or to direct connected devices to do so.

Download the full report.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

How gamification took over the world

It’s a thought that occurs to every video-game player at some point: What if the weird, hyper-focused state I enter when playing in virtual worlds could somehow be applied to the real one? 

Often pondered during especially challenging or tedious tasks in meatspace (writing essays, say, or doing your taxes), it’s an eminently reasonable question to ask. Life, after all, is hard. And while video games are too, there’s something almost magical about the way they can promote sustained bouts of superhuman concentration and resolve.

For some, this phenomenon leads to an interest in flow states and immersion. For others, it’s simply a reason to play more games. For a handful of consultants, startup gurus, and game designers in the late 2000s, it became the key to unlocking our true human potential.

In her 2010 TED Talk, “Gaming Can Make a Better World,” the game designer Jane McGonigal called this engaged state “blissful productivity.” “There’s a reason why the average World of Warcraft gamer plays for 22 hours a week,” she said. “It’s because we know when we’re playing a game that we’re actually happier working hard than we are relaxing or hanging out. We know that we are optimized as human beings to do hard and meaningful work. And gamers are willing to work hard all the time.”

McGonigal’s basic pitch was this: By making the real world more like a video game, we could harness the blissful productivity of millions of people and direct it at some of humanity’s thorniest problems—things like poverty, obesity, and climate change. The exact details of how to accomplish this were a bit vague (play more games?), but her objective was clear: “My goal for the next decade is to try to make it as easy to save the world in real life as it is to save the world in online games.”

While the word “gamification” never came up during her talk, by that time anyone following the big-ideas circuit (TED, South by Southwest, DICE, etc.) or using the new Foursquare app would have been familiar with the basic idea. Broadly defined as the application of game design elements and principles to non-game activities—think points, levels, missions, badges, leaderboards, reinforcement loops, and so on—gamification was already being hawked as a revolutionary new tool for transforming education, work, health and fitness, and countless other parts of life. 

Instead of liberating us, gamification turned out to be just another tool for coercion, distraction, and control.

Adding “world-saving” to the list of potential benefits was perhaps inevitable, given the prevalence of that theme in video-game storylines. But it also spoke to gamification’s foundational premise: the idea that reality is somehow broken. According to McGonigal and other gamification boosters, the real world is insufficiently engaging and motivating, and too often it fails to make us happy. Gamification promises to remedy this design flawby engineering a new reality, one that transforms the dull, difficult, and depressing parts of life into something fun and inspiring. Studying for exams, doing household chores, flossing, exercising, learning a new language—there was no limit to the tasks that could be turned into games, making everything IRL better.

Today, we live in an undeniably gamified world. We stand up and move around to close colorful rings and earn achievement badges on our smartwatches; we meditate and sleep to recharge our body batteries; we plant virtual trees to be more productive; we chase “likes” and “karma” on social media sites and try to swipe our way toward social connection. And yet for all the crude gamelike elements that have been grafted onto our lives, the more hopeful and collaborative world that gamification promised more than a decade ago seems as far away as ever. Instead of liberating us from drudgery and maximizing our potential, gamification turned out to be just another tool for coercion, distraction, and control. 

Con game

This was not an unforeseeable outcome. From the start, a small but vocal group of journalists and game designers warned against the fairy-tale thinking and facile view of video games that they saw in the concept of gamification. Adrian Hon, author of You’ve Been Played, a recent book that chronicles its dangers, was one of them. 

“As someone who was building so-called ‘serious games’ at the time the concept was taking off, I knew that a lot of the claims being made around the possibility of games to transform people’s behaviors and change the world were completely overblown,” he says. 

Hon isn’t some knee-jerk polemicist. A trained neuroscientist who switched to a career in game design and development, he’s the co-creator of Zombies, Run!—one of the most popular gamified fitness apps in the world. While he still believes games can benefit and enrich aspects of our nongaming lives, Hon says a one-size-fits-all approach is bound to fail. For this reason, he’s firmly against both the superficial layering of generic points, leaderboards, and missions atop everyday activities and the more coercive forms of gamification that have invaded the workplace.

three snakes in concentric circles

SELMAN DESIGN

Ironically, it’s these broad and varied uses that make criticizing the practice so difficult. As Hon notes in his book, gamification has always been a fast-moving target, varying dramatically in scale, scope, and technology over the years. As the concept has evolved, so too have its applications, whether you think of the gambling mechanics that now encourage users of dating apps to keep swiping, the “quests” that compel exhausted Uber drivers to complete just a few more trips, or the utopian ambition of using gamification to save the world.

In the same way that AI’s lack of a fixed definition today makes it easy to dismiss any one critique for not addressing some other potential definition of it, so too do gamification’s varied interpretations. “I remember giving talks critical of gamification at gamification conferences, and people would come up to me afterwards and be like, ‘Yeah, bad gamification is bad, right? But we’re doing good gamification,’” says Hon. (They weren’t.) 

For some critics, the very idea of “good gamification” was anathema. Their main gripe with the term and practice was, and remains, that it has little to nothing to do with actual games.

“A game is about play and disruption and creativity and ambiguity and surprise,” wrote the late Jeff Watson, a game designer, writer, and educator who taught at the University of Southern California’s School of Cinematic Arts. Gamification is about the opposite—the known, the badgeable, the quantifiable. “It’s about ‘checking in,’ being tracked … [and] becoming more regimented. It’s a surveillance and discipline system—a wolf in sheep’s clothing. Beware its lure.”

Another game designer, Margaret Robertson, has argued that gamification should really be called “pointsification,” writing: “What we’re currently terming gamification is in fact the process of taking the thing that is least essential to games and representing it as the core of the experience. Points and badges have no closer a relationship to games than they do to websites and fitness apps and loyalty cards.”

For the author and game designer Ian Bogost, the entire concept amounted to a marketing gimmick. In a now-famous essay published in the Atlantic in 2011, he likened gamification to the moral philosopher Harry Frankfurt’s definition of bullshit—that is, a strategy intended to persuade or coerce without regard for actual truth. 

“The idea of learning or borrowing lessons from game design and applying them to other areas was never the issue for me,” Bogost told me. “Rather, it was not doing that—acknowledging that there’s something mysterious, powerful, and compelling about games, but rather than doing the hard work, doing no work at all and absconding with the spirit of the form.” 

Gaming the system

So how did a misleading term for a misunderstood process that’s probably just bullshit come to infiltrate virtually every part of our lives? There’s no one simple answer. But gamification’s meteoric rise starts to make a lot more sense when you look at the period that gave birth to the idea. 

The late 2000s and early 2010s were, as many have noted, a kind of high-water mark for techno-­optimism. For people both inside the tech industry and out, there was a sense that humanity had finally wrapped its arms around a difficult set of problems, and that technology was going to help us squeeze out some solutions. The Arab Spring bloomed in 2011 with the help of platforms like Facebook and Twitter, money was more or less free, and “____ can save the world” articles were legion (with ____ being everything from “eating bugs” to “design thinking”).

This was also the era that produced the 10,000-hours rule of success, the long tail, the four-hour workweek, the wisdom of crowds, nudge theory, and a number of other highly simplistic (or, often, flat-out wrong) theories about the way humans, the internet, and the world work. 

“All of a sudden you had VC money and all sorts of important, high-net-worth people showing up at game developer conferences.”

Ian Bogost, author and game designer

Adding video games to this heady stew of optimism gave the game industry something it had long sought but never achieved: legitimacy. Even with games ascendant in popular culture—and on track to eclipse both the film and music industries in terms of revenue—they still were largely seen as a frivolous, productivity-­squandering, violence-encouraging form of entertainment. Seemingly overnight, gamification changed all that. 

“There was definitely this black-sheep mentality in the game development community—the sense that what we had been doing for decades was just a joke to people,” says Bogost. “All of a sudden you had VC money and all sorts of important, high-net-worth people showing up at game developer conferences, and it was like, ‘Finally someone’s noticing. They realize that we have something to offer.’”

This wasn’t just flattering; it was intoxicating. Gamification took a derided pursuit and recast it as a force for positive change, a way to make the real world better. While  enthusiastic calls to “build a game layer on top of reality” may sound dystopian to many of us today, the sentiment didn’t necessarily have the same ominous undertones at the end of the aughts. 

Combine the cultural recasting of games with an array of cheaper and faster technologies—GPS, ubiquitous and reliable mobile internet, powerful smartphones, Web 2.0 tools and services—and you arguably had all the ingredients needed for gamification’s rise. In a very real sense, reality in 2010 was ready to be gamified. Or to put it a slightly different way: Gamification was an idea perfectly suited for its moment. 

Gaming behavior

Fine, you might be asking at this point, but does it work? Surely, companies like Apple, Uber, Strava, Microsoft, Garmin, and others wouldn’t bother gamifying their products and services if there were no evidence of the strategy’s efficacy. The answer to the question, unfortunately, is super annoying: Define work.

Because gamification is so pervasive and varied, it’s hard to address its effectiveness in any direct or comprehensive way. But one can confidently say this: Gamification did not save the world. Climate change still exists. As do obesity, poverty, and war. Much of generic gamification’s power supposedly resides in its ability to nudge or steer us toward, or away from, certain behaviors using competition (challenges and leaderboards), rewards (points and achievement badges), and other sources of positive and negative feedback. 

Gamification is, and has always been, a way to induce specific behaviors in people using virtual carrots and sticks.

On that front, the results are mixed. Nudge theory lost much of its shine with academics in 2022 after a meta-analysis of previous studies concluded that, after correcting for publication bias, there wasn’t much evidence it worked to change behavior at all. Still, there are a lot of ways to nudge and a lot of behaviors to modify. The fact remains that plenty of people claim to be highly motivated to close their rings, earn their sleep crowns, or hit or exceed some increasingly ridiculous number of steps on their Fitbits (see humorist David Sedaris). 

Sebastian Deterding, a leading researcher in the field, argues that gamification can work, but its successes tend to be really hard to replicate. Not only do academics not know what works, when, and how, according to Deterding, but “we mostly have just-so stories without data or empirical testing.” 

8bit carrot dangling from a stick

SELMAN DESIGN

In truth, gamification acolytes were always pulling from an old playbook—one that dates back to the early 20th century. Then, behaviorists like John Watson and B.F. Skinner saw human behaviors (a category that for Skinner included thoughts, actions, feelings, and emotions) not as the products of internal mental states or cognitive processes but, rather, as the result of external forces—forces that could conveniently be manipulated. 

If Skinner’s theory of operant conditioning, which doled out rewards to positively reinforce certain behaviors, sounds a lot like Amazon’s “Fulfillment Center Games,” which dole out rewards to compel workers to work harder, faster, and longer—well, that’s not a coincidence. Gamification is, and has always been, a way to induce specific behaviors in people using virtual carrots and sticks. 

Sometimes this may work; other times not. But ultimately, as Hon points out, the question of efficacy may be beside the point. “There is no before or after to compare against if your life is always being gamified,” he writes. “There isn’t even a static form of gamification that can be measured, since the design of coercive gamification is always changing, a moving target that only goes toward greater and more granular intrusion.” 

The game of life

Like any other art form, video games offer a staggering array of possibilities. They can educate, entertain, foster social connection, inspire, and encourage us to see the world in different ways. Some of the best ones manage to do all of this at once.

Yet for many of us, there’s the sense today that we’re stuck playing an exhausting game that we didn’t opt into. This one assumes that our behaviors can be changed with shiny digital baubles, constant artificial competition, and meaningless prizes. Even more insulting, the game acts as if it exists for our benefit—promising to make us fitter, happier, and more productive—when in truth it’s really serving the commercial and business interests of its makers. 

Metaphors can be an imperfect but necessary way to make sense of the world. Today, it’s not uncommon to hear talk of leveling up, having a God Mode mindset, gaining XP, and turning life’s difficulty settings up (or down). But the metaphor that resonates most for me—the one that seems to neatly capture our current predicament—is that of the NPC, or non-player character.  

NPCs are the “Sisyphean machines” of video games, programmed to follow a defined script forever and never question or deviate. They’re background players in someone else’s story, typically tasked with furthering a specific plotline or performing some manual labor. To call someone an NPC in real life is to accuse them of just going through the motions, not thinking for themselves, not being able to make their own decisions. This, for me, is gamification’s real end result. It’s acquiescence pretending to be empowerment. It strips away the very thing that makes games unique—a sense of agency—and then tries to mask that with crude stand-ins for accomplishment.

So what can we do? Given the reach and pervasiveness of gamification, critiquing it at this point can feel a little pointless, like railing against capitalism. And yet its own failed promises may point the way to a possible respite. If gamifying the world has turned our lives into a bad version of a video game, perhaps this is the perfect moment to reacquaint ourselves with why actual video games are great in the first place. Maybe, to borrow an idea from McGonigal, we should all start playing better games. 

Bryan Gardiner is a writer based in Oakland, California. 

How a simple circuit could offer an alternative to energy-intensive GPUs

On a table in his lab at the University of Pennsylvania, physicist Sam Dillavou has connected an array of breadboards via a web of brightly colored wires. The setup looks like a DIY home electronics project—and not a particularly elegant one. But this unassuming assembly, which contains 32 variable resistors, can learn to sort data like a machine-learning model.

While its current capability is rudimentary, the hope is that the prototype will offer a low-power alternative to the energy-guzzling graphical processing unit (GPU) chips widely used in machine learning. 

“Each resistor is simple and kind of meaningless on its own,” says Dillavou. “But when you put them in a network, you can train them to do a variety of things.”

breadboards connected in a grid
Sam Dillavou’s laboratory at the University of Pennsylvania is using circuits composed of resistors to perform simple machine learning classification tasks. 
FELICE MACERA

A task the circuit has performed: classifying flowers by properties such as petal length and width. When given these flower measurements, the circuit could sort them into three species of iris. This kind of activity is known as a “linear” classification problem, because when the iris information is plotted on a graph, the data can be cleanly divided into the correct categories using straight lines. In practice, the researchers represented the flower measurements as voltages, which they fed as input into the circuit. The circuit then produced an output voltage, which corresponded to one of the three species. 

This is a fundamentally different way of encoding data from the approach used in GPUs, which represent information as binary 1s and 0s. In this circuit, information can take on a maximum or minimum voltage or anything in between. The circuit classified 120 irises with 95% accuracy. 

Now the team has managed to make the circuit perform a more complex problem. In a preprint currently under review, the researchers have shown that it can perform a logic operation known as XOR, in which the circuit takes in two binary numbers and determines whether the inputs are the same. This is a “nonlinear” classification task, says Dillavou, and “nonlinearities are the secret sauce behind all machine learning.” 

Their demonstrations are a walk in the park for the devices you use every day. But that’s not the point: Dillavou and his colleagues built this circuit as an exploratory effort to find better computing designs. The computing industry faces an existential challenge as it strives to deliver ever more powerful machines. Between 2012 and 2018, the computing power required for cutting-edge AI models increased 300,000-fold. Now, training a large language model takes the same amount of energy as the annual consumption of more than a hundred US homes. Dillavou hopes that his design offers an alternative, more energy-efficient approach to building faster AI.

Training in pairs

To perform its various tasks correctly, the circuitry requires training, just like contemporary machine-learning models that run on conventional computing chips. ChatGPT, for example, learned to generate human-sounding text after being shown many instances of real human text; the circuit learned to predict which measurements corresponded to which type of iris after being shown flower measurements labeled with their species. 

Training the device involves using a second, identical circuit to “instruct” the first device. Both circuits start with the same resistance values for each of their 32 variable resistors. Dillavou feeds both circuits the same inputs—a voltage corresponding to, say, petal width—and adjusts the output voltage of the second circuit to correspond to the correct species. The first circuit receives feedback from that second circuit, and both circuits adjust their resistances so they converge on the same values. The cycle starts again with a new input, until the circuits have settled on a set of resistance levels that produce the correct output for the training examples. In essence, the team trains the device via a method known as supervised learning, where an AI model learns from labeled data to predict the labels for new examples.

It can help, Dillavou says, to think of the electric current in the circuit as water flowing through a network of pipes. The equations governing fluid flow are analogous to those governing electron flow and voltage. Voltage corresponds to fluid pressure, while electrical resistance corresponds to the pipe diameter. During training, the different “pipes” in the network adjust their diameter in various parts of the network in order to achieve the desired output pressure. In fact, early on, the team considered building the circuit out of water pipes rather than electronics. 

For Dillavou, one fascinating aspect of the circuit is what he calls its “emergent learning.” In a human, “every neuron is doing its own thing,” he says. “And then as an emergent phenomenon, you learn. You have behaviors. You ride a bike.” It’s similar in the circuit. Each resistor adjusts itself according to a simple rule, but collectively they “find” the answer to a more complicated question without any explicit instructions. 

A potential energy advantage

Dillavou’s prototype qualifies as a type of analog computer—one that encodes information along a continuum of values instead of the discrete 1s and 0s used in digital circuitry. The first computers were analog, but their digital counterparts superseded them after engineers developed fabrication techniques to squeeze more transistors onto digital chips to boost their speed. Still, experts have long known that as they increase in computational power, analog computers offer better energy efficiency than digital computers, says Aatmesh Shrivastava, an electrical engineer at Northeastern University. “The power efficiency benefits are not up for debate,” he says. However, he adds, analog signals are much noisier than digital ones, which make them ill suited for any computing tasks that require high precision.

In practice, Dillavou’s circuit hasn’t yet surpassed digital chips in energy efficiency. His team estimates that their design uses about 5 to 20 picojoules per resistor to generate a single output, where each resistor represents a single parameter in a neural network. Dillavou says this is about a tenth as efficient as state-of-the-art AI chips. But he says that the promise of the analog approach lies in scaling the circuit up, to increase its number of resistors and thus its computing power.

He explains the potential energy savings this way: Digital chips like GPUs expend energy per operation, so making a chip that can perform more operations per second just means a chip that uses more energy per second. In contrast, the energy usage of his analog computer is based on how long it is on. Should they make their computer twice as fast, it would also become twice as energy efficient. 

Dillavou’s circuit is also a type of neuromorphic computer, meaning one inspired by the brain. Like other neuromorphic schemes, the researchers’ circuitry doesn’t operate according to top-down instruction the way a conventional computer does. Instead, the resistors adjust their values in response to external feedback in a bottom-up approach, similar to how neurons respond to stimuli. In addition, the device does not have a dedicated component for memory. This could offer another energy efficiency advantage, since a conventional computer expends a significant amount of energy shuttling data between processor and memory. 

While researchers have already built a variety of neuromorphic machines based on different materials and designs, the most technologically mature designs are built on semiconducting chips. One example is Intel’s neuromorphic computer Loihi 2, to which the company began providing access for government, academic, and industry researchers in 2021. DeepSouth, a chip-based neuromorphic machine at Western Sydney University that is designed to be able to simulate the synapses of the human brain at scale, is scheduled to come online this year.

The machine-learning industry has shown interest in chip-based neuromorphic computing as well, with a San Francisco–based startup called Rain Neuromorphics raising $25 million in February. However, researchers still haven’t found a commercial application where neuromorphic computing definitively demonstrates an advantage over conventional computers. In the meantime, researchers like Dillavou’s team are putting forth new schemes to push the field forward. A few people in industry have expressed interest in his circuit. “People are most interested in the energy efficiency angle,” says Dillavou. 

But their design is still a prototype, with its energy savings unconfirmed. For their demonstrations, the team kept the circuit on breadboards because it’s “the easiest to work with and the quickest to change things,” says Dillavou, but the format suffers from all sorts of inefficiencies. They are testing their device on printed circuit boards to improve its energy efficiency, and they plan to scale up the design so it can perform more complicated tasks. It remains to be seen whether their clever idea can take hold out of the lab.

Industry- and AI-focused cloud transformation

For years, cloud technology has demonstrated its ability to cut costs, improve efficiencies, and boost productivity. But today’s organizations are looking to cloud for more than simply operational gains. Faced with an ever-evolving regulatory landscape, a complex business environment, and rapid technological change, organizations are increasingly recognizing cloud’s potential to catalyze business transformation.

Cloud can transform business by making it ready for AI and other emerging technologies. The global consultancy McKinsey projects that a staggering $3 trillion in value could be created by cloud transformations by 2030. Key value drivers range from innovation-driven growth to accelerated product development.

“As applications move to the cloud, more and more opportunities are getting unlocked,” says Vinod Mamtani, vice president and general manager of generative AI services for Oracle Cloud Infrastructure. “For example, the application of AI and generative AI are transforming businesses in deep ways.”

No longer simply a software and infrastructure upgrade, cloud is now a powerful technology capable of accelerating innovation, improving agility, and supporting emerging tools. In order to capitalize on cloud’s competitive advantages, however, businesses must ask for more from their cloud transformations.

Every business operates in its own context, and so a strong cloud solution should have built-in support for industry-specific best practices. And because emerging technology increasingly drives all businesses, an effective cloud platform must be ready for AI and the immense impacts it will have on the way organizations operate and employees work.

An industry-specific approach

The imperative for cloud transformation is evident: In today’s fast-faced business environment, cloud can help organizations enhance innovation, scalability, agility, and speed while simultaneously alleviating the burden on time-strapped IT teams. Yet most organizations have not fully made the leap to cloud. McKinsey, for example, reports a broad mismatch between leading companies’ cloud aspirations and realities—though nearly all organizations say they aspire to run the majority of their applications in the cloud within the decade, the average organization has currently relocated only 15–20% of them.

Cloud solutions that take an industry-specific approach can help companies meet their business needs more easily, making cloud adoption faster, smoother, and more immediately useful. “Cloud requirements can vary significantly across vertical industries due to differences in compliance requirements, data sensitivity, scalability, and specific business objectives,” says Deviprasad Rambhatla, senior vice president and sector head of retail services and transportation at Wipro.

Health-care organizations, for instance, need to manage sensitive patient data while complying with strict regulations such as HIPAA. As a result, cloud solutions for that industry must ensure features such as high availability, disaster recovery capabilities, and continuous access to critical patient information.

Retailers, on the other hand, are more likely to experience seasonal business fluctuations, requiring cloud solutions that allow for greater flexibility. “Cloud solutions allow retailers to scale infrastructure on an up-and-down basis,” says Rambhatla. “Moreover, they’re able to do it on demand, ensuring optimal performance and cost efficiency.”

Cloud-based applications can also be tailored to meet the precise requirements of a particular industry. For retailers, these might include analytics tools that ingest vast volumes of data and generate insights that help the business better understand consumer behavior and anticipate market trends.

Download the full report.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

Optimizing the supply chain with a data lakehouse

When a commercial ship travels from the port of Ras Tanura in Saudi Arabia to Tokyo Bay, it’s not only carrying cargo; it’s also transporting millions of data points across a wide array of partners and complex technology systems.

Consider, for example, Maersk. The global shipping container and logistics company has more than 100,000 employees, offices in 120 countries, and operates about 800 container ships that can each hold 18,000 tractor-trailer containers. From manufacture to delivery, the items within these containers carry hundreds or thousands of data points, highlighting the amount of supply chain data organizations manage on a daily basis.

Until recently, access to the bulk of an organizations’ supply chain data has been limited to specialists, distributed across myriad data systems. Constrained by traditional data warehouse limitations, maintaining the data requires considerable engineering effort; heavy oversight, and substantial financial commitment. Today, a huge amount of data—generated by an increasingly digital supply chain—languishes in data lakes without ever being made available to the business.

A 2023 Boston Consulting Group survey notes that 56% of managers say although investment in modernizing data architectures continues, managing data operating costs remains a major pain point. The consultancy also expects data deluge issues are likely to worsen as the volume of data generated grows at a rate of 21% from 2021 to 2024, to 149 zettabytes globally.

“Data is everywhere,” says Mark Sear, director of AI, data, and integration at Maersk. “Just consider the life of a product and what goes into transporting a computer mouse from China to the United Kingdom. You have to work out how you get it from the factory to the port, the port to the next port, the port to the warehouse, and the warehouse to the consumer. There are vast amounts of data points throughout that journey.”

Sear says organizations that manage to integrate these rich sets of data are poised to reap valuable business benefits. “Every single data point is an opportunity for improvement—to improve profitability, knowledge, our ability to price correctly, our ability to staff correctly, and to satisfy the customer,” he says.

Organizations like Maersk are increasingly turning to a data lakehouse architecture. By combining the cost-effective scale of a data lake with the capability and performance of a data warehouse, a data lakehouse promises to help companies unify disparate supply chain data and provide a larger group of users with access to data, including structured, semi-structured, and unstructured data. Building analytics on top of the lakehouse not only allows this new architectural approach to advance supply chain efficiency with better performance and governance, but it can also support easy and immediate data analysis and help reduce operational costs.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

Almost every Chinese keyboard app has a security flaw that reveals what users type

Almost all keyboard apps used by Chinese people around the world share a security loophole that makes it possible to spy on what users are typing. 

The vulnerability, which allows the keystroke data that these apps send to the cloud to be intercepted, has existed for years and could have been exploited by cybercriminals and state surveillance groups, according to researchers at the Citizen Lab, a technology and security research lab affiliated with the University of Toronto.

These apps help users type Chinese characters more efficiently and are ubiquitous on devices used by Chinese people. The four most popular apps—built by major internet companies like Baidu, Tencent, and iFlytek—basically account for all the typing methods that Chinese people use. Researchers also looked into the keyboard apps that come preinstalled on Android phones sold in China. 

What they discovered was shocking. Almost every third-party app and every Android phone with preinstalled keyboards failed to protect users by properly encrypting the content they typed. A smartphone made by Huawei was the only device where no such security vulnerability was found.

In August 2023, the same researchers found that Sogou, one of the most popular keyboard apps, did not use Transport Layer Security (TLS) when transmitting keystroke data to its cloud server for better typing predictions. Without TLS, a widely adopted international cryptographic protocol that protects users from a known encryption loophole, keystrokes can be collected and then decrypted by third parties.

“Because we had so much luck looking at this one, we figured maybe this generalizes to the others, and they suffer from the same kinds of problems for the same reason that the one did,” says Jeffrey Knockel, a senior research associate at the Citizen Lab, “and as it turns out, we were unfortunately right.”

Even though Sogou fixed the issue after it was made public last year, some Sogou keyboards preinstalled on phones are not updated to the latest version, so they are still subject to eavesdropping. 

This new finding shows that the vulnerability is far more widespread than previously believed. 

“As someone who also has used these keyboards, this was absolutely horrifying,” says Mona Wang, a PhD student in computer science at Princeton University and a coauthor of the report. 

“The scale of this was really shocking to us,” says Wang. “And also, these are completely different manufacturers making very similar mistakes independently of one another, which is just absolutely shocking as well.”

The massive scale of the problem is compounded by the fact that these vulnerabilities aren’t hard to exploit. “You don’t need huge supercomputers crunching numbers to crack this. You don’t need to collect terabytes of data to crack it,” says Knockel. “If you’re just a person who wants to target another person on your Wi-Fi, you could do that once you understand the vulnerability.” 

The ease of exploiting the vulnerabilities and the huge payoff—knowing everything a person types, potentially including bank account passwords or confidential materials—suggest that it’s likely they have already been taken advantage of by hackers, the researchers say. But there’s no evidence of this, though state hackers working for Western governments targeted a similar loophole in a Chinese browser app in 2011.

Most of the loopholes found in this report are “so far behind modern best practices” that it’s very easy to decrypt what people are typing, says Jedidiah Crandall, an associate professor of security and cryptography at Arizona State University, who was consulted in the writing of this report. Because it doesn’t take much effort to decrypt the messages, this type of loophole can be a great target for large-scale surveillance of massive groups, he says.

After the researchers got in contact with companies that developed these keyboard apps, the majority of the loopholes were fixed. But a few companies have been unresponsive, and the vulnerability still exists in some apps and phones, including QQ Pinyin and Baidu, as well as in any keyboard app that hasn’t been updated to the latest version. Baidu, Tencent, iFlytek, and Samsung did not immediately reply to press inquiries sent by MIT Technology Review.

One potential cause of the loopholes’ ubiquity is that most of these keyboard apps were developed in the 2000s, before the TLS protocol was commonly adopted in software development. Even though the apps have been through numerous rounds of updates since then, inertia could have prevented developers from adopting a safer alternative.

The report points out that language barriers and different tech ecosystems prevent English- and Chinese-speaking security researchers from sharing information that could fix issues like this more quickly. For example, because Google’s Play store is blocked in China, most Chinese apps are not available in Google Play, where Western researchers often go for apps to analyze. 

Sometimes all it takes is a little additional effort. After two emails about the issue to iFlytek were met with silence, the Citizen Lab researchers changed the email title to Chinese and added a one-line summary in Chinese to the English text. Just three days later, they received an email from iFlytek, saying that the problem had been resolved.

Why it’s so hard for China’s chip industry to become self-sufficient

This story first appeared in China Report, MIT Technology Review’s newsletter about technology in China. Sign up to receive it in your inbox every Tuesday.

I don’t know about you, but I only learned last week that there’s something connecting MSG and computer chips.

Inside most laptop and data center chips today, there’s a tiny component called ABF. It’s a thin insulating layer around the wires that conduct electricity. And over 90% of the materials around the world used to make this insulator are produced by a single Japanese company named Ajinomoto, more commonly known for commercializing the seasoning powder MSG in 1909.

Hold on, what? 

As my colleague James O’Donnell explained in his story last week, it turns out Ajinomoto figured out in the 1990s that a chemical by-product of MSG production can be used to make insulator films, which proved to be essential for high-performance chips. And in the 30 years since, the company has totally dominated ABF supply. The product—Ajinomoto Build-up Film—is even named after it.

James talked to Thintronics, a California-based company that’s developing a new insulating material it hopes could challenge Ajinomoto’s monopoly. It already has a lab product with impressive attributes but still needs to test it in manufacturing reality.

Beyond Thintronics, the struggle to break up Ajinomoto’s monopoly is not just a US effort.

Within China, at least three companies are also developing similar insulator products. Xi’an Tianhe Defense Technology, which makes products for both military and civilian use, introduced its take on the material, which it calls QBF, in 2023; Zhejiang Wazam New Material and Guangdong Hinno-tech have also announced similar products in recent years. But all of them are still going through industrial testing with chipmakers, and few have recent updates on how well these materials have performed in mass-production settings.

“It’s interesting that there’s this parallel competition going on,” James told me when we recently discussed his story. “In some ways, it’s about the materials. But in other ways, it’s totally shaped by government funding and incentives.”

For decades, the fact that the semiconductor supply chain was in a few companies’ hands was seen as a strength, not a problem, so governments were not concerned that one Japanese company controlled almost the entire supply of ABF. Similar monopolies exist for many other materials and components that go into a chip.

But in the last few years, both the US and Chinese governments have changed that way of thinking. And new policies subsidizing domestic chip manufacturing are creating a favorable environment for companies to challenge monopolies like Ajinomoto’s.

In the US, this trend is driven by the fear of supply chain disruptions and a will to rebuild domestic semiconductor manufacturing capabilities. The CHIPS Act was announced to inject investment into chip companies that bring their plants back to the US, but smaller companies like Thintronics could also benefit, both directly through funding and indirectly through the establishment of a US-based supply chain.

Meanwhile, China is being cornered by a US-led blockade to deny it access to the most advanced chip technologies. While materials like ABF are not restricted in any way today, the fact that one foreign company controls almost the entire supply of an indispensable material raises the stakes enough to make the government worry. It needs to find a domestic alternative in case ABF becomes subject to sanctions too.

But it takes a lot more than government policies to change the status quo. Even if these companies are able to find alternative materials that perform better than ABF, there’s still an uphill battle to convince the industry to adopt it en masse.

“You can look at any dielectric film supplier (many from Japan and some from the US), and they have all at one time or another tried to break into ABF market dominance and had limited success,” Venky Sundaram, a semiconductor researcher and entrepreneur, told James. 

It’s not as simple as just swapping out ABF and swapping in a new insulator material. Chipmaking is a deeply intricate process, with components closely depending on each other. Changing one material could require a lot more knock-on changes to other components and the entire process. “Convincing someone to do that depends on what relationships you have with the industry. These big manufacturing players are a little bit less likely to take on a small materials company, because any time they’re taking on new material, they’re slowing down their production,” James said.

As a result, Ajinomoto’s market monopoly will probably remain while other companies keep trying to develop a new material that significantly improves on ABF. 

That result, however, will have different implications for the US and China. 

The US and Japan have long had a strategic technological alliance, and that could be set to deepen because both of them consider the rise of China a threat. In fact, Japan’s prime minister, Fumio Kishida, was just visiting the US last week, hoping to score more collaborations on next-generation chips. Even though there has been some pushback from the Japanese chip industry about how strict US export restrictions could become, this hasn’t been strong enough to sway Japan to China’s side.

All these factors give the Chinese government an even greater sense of urgency to become self-sufficient. The country has already been investing vast sums of money to that end, but progress has been limited, with many industry insiders pessimistic about whether China can catch up fast enough. If Ajinomoto’s failed competitors in the past tell us anything, it’s that this will not be an easy journey for China either.

Do you think China has a chance of cracking Ajinomoto’s monopoly over this very specific insulating material? Let me know your thoughts at zeyi@technologyreview.com.


Now read the rest of China Report

Catch up with China

1. Following the explosive popularity of minute-long short dramas made for phones, China’s culture regulator will soon announce new regulations that tighten its control of them. (Sixth Tone)

  • This is not a surprise to the companies involved. Some Chinese short-drama companies have already started to expand overseas, driven out by domestic policy pressures. I profiled one named FlexTV. (MIT Technology Review)

2. There have been many minor conflicts between China and the Philippines recently over maritime territory claims. Here’s what it feels like to live on one of those contested islands. (NPR)

3. The Chinese government has asked domestic telecom companies to replace all foreign chips by 2027. It’s a move that mirrors previous requests from the US to replace all Huawei and ZTE equipment in telecom networks. (Wall Street Journal $)

4. A decade ago, about 25,000 American students were studying in China. Today, there are only about 750. It may be unsurprising given recent geopolitical tensions, but neither country is happy with the situation. (Associated Press)

5. Latin America is importing large amounts of Chinese green technologies—mostly electric vehicles, lithium-ion batteries, and solar panels. (The Economist $)

6. China’s top spy agency says foreign agents have been trying to intercept information about the country’s rare earth industry. (South China Morning Post $)

7. Amid the current semiconductor boom, Southeast Asian youths are flocking to Taiwan to train and work in the chip industry. (Rest of World)

Lost in translation

The bodies of eight Chinese migrants were recently discovered on a beach in Mexico. According to Initium Media, a Singapore-based publication, this was the first confirmed shipwreck incident with Chinese migrants heading to the US, but many more have taken the perilous route in recent years. In 2023, over 37,000 Chinese people illegally entered the US through the border with Mexico.

The traffickers often arrange shabby boats with no safety measures to sail from Tapachula to Oaxaca, a popular route that circumvents police checkpoints on land but makes for an extremely dangerous journey often rocked by strong winds and waves. There had always been rumors of people going missing in the ocean, but these proved impossible to confirm, as no bodies were found. The latest tragedy was the first one to come to public attention. Of the nine Chinese migrants onboard the boat, only one survived. Three bodies remain unidentified today.

One more thing

Forget about the New York Times’ election-result needles and CNN’s relentless coverage by John King. In South Korea, the results of national elections are broadcast on TV with wild and whimsical animations. To illustrate the results of parliamentary elections that just concluded last week, candidates were shown fighting on a fictional train heading toward the National Assembly, parodying Mission: Impossible’s fight scene. According to the BBC, these election-night animations took a team of 70 to prepare in advance and about 200 people working on election night.