Early next year, Google and its partners plan to launch the first in a series of satellites that together would provide close-up, frequently refreshed images of wildfires around the world, offering data that could help firefighters battle blazes more rapidly, effectively, and safely.

The online search giant’s nonprofit and research arms have collaborated with the Moore Foundation, the Environmental Defense Fund, the satellite company Muon Space, and others to deploy 52 satellites equipped with custom-developed sensors over the coming years. 

The FireSat satellites will be able to spot fires as small as 5 by 5 meters (16 by 16 feet) on any speck of the globe. Once the full constellation is in place, the system should be capable of updating those images about every 20 minutes, the group says.

Those capabilities together would mark a significant upgrade over what’s available from the satellites that currently provide data to fire agencies. Generally, they can provide either high-resolution images that aren’t updated rapidly enough to track fires closely or frequently refreshed images that are relatively low-resolution.

The Earth Fire Alliance collaboration will also leverage Google’s AI wildfire tools, which have been trained to detect early indications of wildfires and track their progression, to draw additional insights from the data.

The images and analysis will be provided free to fire agencies around the world, helping to improve understanding of where fires are, where they’re moving, and how hot they’re burning. The information could help agencies stamp out small fires before they turn into raging infernos, place limited firefighting resources where they’ll do the most good, and evacuate people along the safest paths.

“In the satellite image of the Earth, a lot of things can be mistaken for a fire: a glint, a hot roof, smoke from another fire,” says Chris Van Arsdale, climate and energy research lead at Google Research and chairman of the Earth Fire Alliance. “Detecting fires becomes a game of looking for needles in a world of haystacks. Solving this will enable first responders to act quickly and precisely when a fire is detected.”

Some details of FireSat were unveiled earlier this year. But the organizations involved will announce additional information about their plans today, including the news that Google.org, the company’s charitable arm, has provided $13 million to the program and that the inaugural launch is scheduled to occur next year. 

Reducing the fog of war

The news comes as large fires rage across millions of acres in the western US, putting people and property at risk. The blazes include the Line Fire in Southern California, the Shoe Fly Fire in central Oregon, and the Davis Fire south of Reno, Nevada.

Wildfires have become more frequent, extreme, and dangerous in recent decades. That, in part, is a consequence of climate change: Rising temperatures suck the moisture from trees, shrubs, and grasses. But fires increasingly contribute to global warming as well. A recent study found that the fires that scorched millions of acres across Canada last year pumped out 3 billion tons of carbon dioxide, four times the annual pollution produced by the airline industry.

GOOGLE

Humans have also increased fire risk by suppressing natural fires for decades, which has allowed fuel to build up in forests and grasslands, and by constructing communities on the edge of wilderness boundaries without appropriate rules, materials, and safeguards. 

Observers say that FireSat could play an important role in combating fires, both by enabling fire agencies to extinguish small ones before they grow into large ones and by informing effective strategies for battling them once they’re crossed that point.

“What these satellites will do is reduce the fog of war,” says Michael Wara, director of the climate and energy policy program at Stanford University’s Woods Institute for the Environment, who is focused on fire policy issues. “Like when a situation is really dynamic and very dangerous for firefighters and they’re trying to make decisions very quickly about whether to move in to defend structures or try to evacuate people.” 

(Wara serves on the advisory board of the Moore Foundation’s Wildfire Resilience Initiative.)

Some areas, like California, already have greater visibility into the current state of fires or early signs of outbreaks, thanks to technology like Department of Defense satellites, remote camera networks, and planes, helicopters, and drones. But FireSat will be especially helpful for “countries that have less-well-resourced wildland fighting capability,” Wara adds.

Better images, more data, and AI will not be able to fully counter the increased fire dangers. Wara and other fire experts argue that regions need to use prescribed burns and other efforts to more aggressively reduce the buildup of fuel, rethink where and how we build communities in fire-prone areas, and do more to fund and support the work of firefighters on the ground. 

Sounding an earlier alarm for fires will only help reduce dangers when regions have, or develop, the added firefighting resources needed to combat the most dangerous ones quickly and effectively. Communities will also need to put in place better policies to determine what types of fires should be left to burn, and under what conditions.

‘A game changer’

Kate Dargan Marquis, a senior wildfire advisor to the Moore Foundation who previously served as state fire marshal for California, says she can “personally attest” to the difference that such tools will make to firefighters in the field.

“It is a game changer, especially as wildfires are becoming more extreme, more frequent, and more dangerous for everyone,” she says. “Information like this will make a lifesaving difference for firefighters and communities around the globe.”

GOOGLE

Google Research developed the sensors for the satellite and tested them as well as the company’s AI fire detection models by conducting flights over controlled burns in California. Google intends to work with Earth Fire Alliance “to ensure AI can help make this data as useful as possible, and also that wildfire information is shared as widely as possible,” the company said.

Google’s Van Arsdale says that providing visual images of every incident around the world from start to finish will be enormously valuable to scientists studying wildfires and climate change. 

“We can combine this data with Google’s existing models of the Earth to help advance our understanding of fire behavior and fire dynamics across all of Earth’s ecosystems,” he says. “All this together really has the potential to help mitigate the environmental and social impact of fire while also improving people’s health and safety.”

Specifically, it could improve assessments of fire risk, as well as our understanding of the most effective means of preventing or slowing the spread of fires. For instance, it could help communities determine where it would be most cost-effective to remove trees and underbrush. 

Figuring out the best ways to conduct such interventions is another key goal of the program, given their high cost and the limited funds available for managing wildlands, says Genny Biggs, the program director for the Moore Foundation’s Wildfire Resilience Initiative.

The launch

The idea for FireSat grew out of a series of meetings that began with a 2019 workshop hosted by the Moore Foundation, which provided the first philanthropic funding for the program. 

The first satellite, scheduled to be launched aboard a SpaceX rocket early next year, will be fully functional aside from some data transmission features. The goals of the “protoflight” mission include testing the onboard systems and the data they send back. The Earth Fire Alliance will work with a handful of early-adopter agencies to prepare for the next phases. 

The group intends to launch three fully operational satellites in 2026, with additional deployments in the years that follow. Muon Space will build and operate the satellites. 

Agencies around the world should be able to receive hourly wildfire updates once about half of the constellation is operational, says Brian Collins, executive director of the Earth Fire Alliance. It hopes to launch all 52 satellites by around the end of this decade.

Each satellite is designed to last about five years, so the organization will eventually need to deploy 10 more each year to maintain the constellation.

The Earth Fire Alliance has secured about two-thirds of the funding it needs for the first phase of the program, which includes the first four launches. The organization will need to raise additional money from government agencies, international organizations, philanthropies, and other groups  to deploy, maintain, and operate the full constellation. It estimates the total cost will exceed $400 million, which Collins notes “is 1/1000th of the economic losses due to extreme wildfires annually in the US alone.”

Asked if commercial uses of the data could also support the program, including potentially military ones, Collins said in an email: “Adjacent applications range from land use management and agriculture to risk management and industrial impact and mitigation.” 

“At the same time, we know that as large agencies and government agencies adopt FireSat data to support a broad public safety mandate, they may develop all-hazard, emergenc[y] management, and security related uses of data,” he added. “As long as opportunities are in balance with our charter to advance a global approach to wildfire and climate resilience, we welcome new ideas and applications of our data.”

‘Living with fire’

A wide variety of startups have emerged in recent years promising to use technology to reduce the frequency and severity of wildfires—for example, by installing cameras and sensors in forests and grasslands, developing robots to carry out controlled burns, deploying autonomous helicopters that can drop suppressant, and harnessing AI to predict wildfire behavior and inform forest and fire management strategies. 

So far, even with all these new tools, it’s still been difficult for communities to keep pace with the rising dangers.

Dargan Marquis—who founded her own wildfire software company, Intterra—says she is confident the incidence of disastrous fires can be meaningfully reduced with programs like FireSat, along with other improved technologies and policies. But she says it’s likely to take decades to catch up with the growing risks, as the world continues warming up.

“We’re going to struggle in places like California, these Mediterranean climates around the world, while our technology and our capabilities and our inventions, etc., catch up with that level of the problem,” she says. 

“We can turn that corner,” she adds. “If we work together on a comprehensive strategy with the right data and a convincing plan over the next 50 years, I do think that by the end of the century, we absolutely can be living with fire.”

In the past couple of years, regulators have been caught off guard again and again as tech companies compete to launch ever more advanced AI models. It’s only a matter of time before labs release another round of models that pose new regulatory challenges. We’re likely just weeks away, for example, from OpenAI’s release of ChatGPT-5, which promises to push AI capabilities further than ever before. As it stands, it seems there’s little anyone can do to delay or prevent the release of a model that poses excessive risks.

Testing AI models before they’re released is a common approach to mitigating certain risks, and it may help regulators weigh up the costs and benefits—and potentially block models from being released if they’re deemed too dangerous. But the accuracy and comprehensiveness of these tests leaves a lot to be desired. AI models may “sandbag” the evaluation—hiding some of their capabilities to avoid raising any safety concerns. The evaluations may also fail to reliably uncover the full set of risks posed by any one model. Evaluations likewise suffer from limited scope—current tests are unlikely to uncover all the risks that warrant further investigation. There’s also the question of who conducts the evaluations and how their biases may influence testing efforts. For those reasons, evaluations need to be used alongside other governance tools. 

One such tool could be internal reporting mechanisms within the labs. Ideally, employees should feel empowered to regularly and fully share their AI safety concerns with their colleagues, and they should feel those colleagues can then be counted on to act on the concerns. However, there’s growing evidence that, far from being promoted, open criticism is becoming rarer in AI labs. Just three months ago, 13 former and current workers from OpenAI and other labs penned an open letter expressing fear of retaliation if they attempt to disclose questionable corporate behaviors that fall short of breaking the law. 

How to sound the alarm

In theory, external whistleblower protections could play a valuable role in the detection of AI risks. These could protect employees fired for disclosing corporate actions, and they could help make up for inadequate internal reporting mechanisms. Nearly every state has a public policy exception to at-will employment termination—in other words, terminated employees can seek recourse against their employers if they were retaliated against for calling out unsafe or illegal corporate practices. However, in practice this exception offers employees few assurances. Judges tend to favor employers in whistleblower cases. The likelihood of AI labs’ surviving such suits seems particularly high given that society has yet to reach any sort of consensus as to what qualifies as unsafe AI development and deployment. 

These and other shortcomings explain why the aforementioned 13 AI workers, including ex-OpenAI employee William Saunders, called for a novel “right to warn.” Companies would have to offer employees an anonymous process for disclosing risk-related concerns to the lab’s board, a regulatory authority, and an independent third body made up of subject-matter experts. The ins and outs of this process have yet to be figured out, but it would presumably be a formal, bureaucratic mechanism. The board, regulator, and third party would all need to make a record of the disclosure. It’s likely that each body would then initiate some sort of investigation. Subsequent meetings and hearings also seem like a necessary part of the process. Yet if Saunders is to be taken at his word, what AI workers really want is something different. 

When Saunders went on the Big Technology Podcast to outline his ideal process for sharing safety concerns, his focus was not on formal avenues for reporting established risks. Instead, he indicated a desire for some intermediate, informal step. He wants a chance to receive neutral, expert feedback on whether a safety concern is substantial enough to go through a “high stakes” process such as a right-to-warn system. Current government regulators, as Saunders says, could not serve that role. 

For one thing, they likely lack the expertise to help an AI worker think through safety concerns. What’s more, few workers will pick up the phone if they know it’s a government official on the other end—that sort of call may be “very intimidating,” as Saunders himself said on the podcast. Instead, he envisages being able to call an expert to discuss his concerns. In an ideal scenario, he’d be told that the risk in question does not seem that severe or likely to materialize, freeing him up to return to whatever he was doing with more peace of mind. 

Lowering the stakes

What Saunders is asking for in this podcast isn’t a right to warn, then, as that suggests the employee is already convinced there’s unsafe or illegal activity afoot. What he’s really calling for is a gut check—an opportunity to verify whether a suspicion of unsafe or illegal behavior seems warranted. The stakes would be much lower, so the regulatory response could be lighter. The third party responsible for weighing up these gut checks could be a much more informal one. For example, AI PhD students, retired AI industry workers, and other individuals with AI expertise could volunteer for an AI safety hotline. They could be tasked with quickly and expertly discussing safety matters with employees via a confidential and anonymous phone conversation. Hotline volunteers would have familiarity with leading safety practices, as well as extensive knowledge of what options, such as right-to-warn mechanisms, may be available to the employee. 

As Saunders indicated, few employees will likely want to go from 0 to 100 with their safety concerns—straight from colleagues to the board or even a government body. They are much more likely to raise their issues if an intermediary, informal step is available.

Studying examples elsewhere

The details of how precisely an AI safety hotline would work deserve more debate among AI community members, regulators, and civil society. For the hotline to realize its full potential, for instance, it may need some way to escalate the most urgent, verified reports to the appropriate authorities. How to ensure the confidentiality of hotline conversations is another matter that needs thorough investigation. How to recruit and retain volunteers is another key question. Given leading experts’ broad concern about AI risk, some may be willing to participate simply out of a desire to lend a hand. Should too few folks step forward, other incentives may be necessary. The essential first step, though, is acknowledging this missing piece in the puzzle of AI safety regulation. The next step is looking for models to emulate in building out the first AI hotline. 

One place to start is with ombudspersons. Other industries have recognized the value of identifying these neutral, independent individuals as resources for evaluating the seriousness of employee concerns. Ombudspersons exist in academia, nonprofits, and the private sector. The distinguishing attribute of these individuals and their staffers is neutrality—they have no incentive to favor one side or the other, and thus they’re more likely to be trusted by all. A glance at the use of ombudspersons in the federal government shows that when they are available, issues may be raised and resolved sooner than they would be otherwise.

This concept is relatively new. The US Department of Commerce established the first federal ombudsman in 1971. The office was tasked with helping citizens resolve disputes with the agency and investigate agency actions. Other agencies, including the Social Security Administration and the Internal Revenue Service, soon followed suit. A retrospective review of these early efforts concluded that effective ombudspersons can meaningfully improve citizen-government relations. On the whole, ombudspersons were associated with an uptick in voluntary compliance with regulations and cooperation with the government. 

An AI ombudsperson or safety hotline would surely have different tasks and staff from an ombudsperson in a federal agency. Nevertheless, the general concept is worthy of study by those advocating safeguards in the AI industry. 

A right to warn may play a role in getting AI safety concerns aired, but we need to set up more intermediate, informal steps as well. An AI safety hotline is low-hanging regulatory fruit. A pilot made up of volunteers could be organized in relatively short order and provide an immediate outlet for those, like Saunders, who merely want a sounding board.

Kevin Frazier is an assistant professor at St. Thomas University College of Law and senior research fellow in the Constitutional Studies Program at the University of Texas at Austin.

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Have you ever found yourself lost in a building that felt impossible to navigate? Thoughtful building design should center on the people who will be using those buildings. But that’s no mean feat.

It’s not just about navigation, either. Just think of an office that left you feeling sleepy or unproductive, or perhaps a health center that had a less-than-reviving atmosphere. A design that works for some people might not work for others. People have different minds and bodies, and varying wants and needs. So how can we factor them all in?

To answer that question, neuroscientists and architects are joining forces at an enormous laboratory in East London—one that allows researchers to build simulated worlds. In this lab, scientists can control light, temperature, and sound. They can create the illusion of a foggy night, or the tinkle of morning birdsong.

And they can study how volunteers respond to these environments, whether they be simulations of grocery stores, hospitals, pedestrian crossings, or schools. That’s how I found myself wandering around a fake art gallery, wearing a modified baseball cap with a sensor that tracked my movements.

I first visited the Person-Environment-Activity Research Lab, referred to as PEARL, back in July. I’d been chatting to Hugo Spiers, a neuroscientist based at University College London, about the use of video games to study how people navigate. Spiers had told me he was working on another project: exploring how people navigate a lifelike environment, and how they respond during evacuations (which, depending on the situation, could be a matter of life or death).

For their research, Spiers and his colleagues set up what they call a “mocked-up art gallery” within PEARL. The center in its entirety is pretty huge as labs go, measuring around 100 meters in length and 40 meters across, with 10-meter-high ceilings in places. There’s no other research center in the world like this, Spiers told me.

The gallery setup looked a little like a maze from above, with a pathway created out of hanging black sheets. The exhibits themselves were videos of dramatic artworks that had been created by UCL students.

When I visited in July, Spiers and his colleagues were running a small pilot study to trial their setup. As a volunteer participant, I was handed a numbered black cap with a square board on top, marked with a large QR code. This code would be tracked by cameras above and around the gallery. The cap also carried a sensor, transmitting radio signals to devices around the maze that could pinpoint my location within a range of 15 centimeters.

At first, all the volunteers (most of whom seemed to be students) were asked to explore the gallery as we would any other. I meandered around, watching the videos, and eavesdropping on the other volunteers, who were chatting about their research and upcoming dissertation deadlines. It all felt pretty pleasant and calm.

That feeling dissipated in the second part of the experiment, when we were each given a list of numbers, told that each one referred to a numbered screen, and informed that we had to visit all the screens in the order in which they appeared on our lists. “Good luck, everybody,” Spiers said.

Suddenly everyone seemed to be rushing around, slipping past each other and trying to move quickly while avoiding collisions. “It’s all got a bit frantic, hasn’t it?” I heard one volunteer comment as I accidentally bumped into another. I hadn’t managed to complete the task by the time Spiers told us the experiment was over. As I walked to the exit, I noticed that some people were visibly out of breath.

The full study took place on Wednesday, September 11. This time, there were around 100 volunteers (I wasn’t one of them). And while almost everyone was wearing a modified baseball cap, some had more complicated gear, including EEG caps to measure brainwaves, or caps that use near-infrared spectroscopy to measure blood flow in the brain. Some people were even wearing eye-tracking devices that monitored which direction they were looking.

“We will do something quite remarkable today,” Spiers told the volunteers, staff, and observers as the experiment started. Taking such detailed measurements from so many individuals in such a setting represented “a world first,” he said.

I have to say that being an observer was much more fun than being a participant. Gone was the stress of remembering instructions and speeding around a maze. Here in my seat, I could watch as the data collected from the cameras and sensors was projected onto a screen. The volunteers, represented as squiggly colored lines, made their way through the gallery in a way that reminded me of the game Snake.

The study itself was similar to the pilot study, although this time the volunteers were given additional tasks. At one point, they were given an envelope with the name of a town or city in it, and asked to find others in the group who had been given the same one. It was fascinating to see the groups form. Some had the names of destination cities like Bangkok, while others had been assigned fairly nondescript English towns like Slough, made famous as the setting of the British television series The Office. At another point, the volunteers were asked to evacuate the gallery from the nearest exit.

The data collected in this study represents something of a treasure trove for researchers like Spiers and his colleagues. The team is hoping to learn more about how people navigate a space, and whether they move differently if they are alone or in a group. How do friends and strangers interact, and does this depend on whether they have certain types of material to bond over? How do people respond to evacuations—will they take the nearest exit as directed, or will they run on autopilot to the exit they used to enter the space in the first place?

All this information is valuable to neuroscientists like Spiers, but it’s also useful to architects like his colleague Fiona Zisch, who is based at UCL’s Bartlett School of Architecture. “We do really care about how people feel about the places we design for them,” Zisch tells me. The findings can guide not only the construction of new buildings, but also efforts to modify and redesign existing ones.

PEARL was built in 2021 and has already been used to help engineers, scientists, and architects explore how neurodivergent people use grocery stores, and the ideal lighting to use for pedestrian crossings, for example. Zisch herself is passionate about creating equitable spaces—particularly for health and education—that everyone can make use of in the best possible way.

In the past, models used in architecture have been developed with typically built, able-bodied men in mind. “But not everyone is a 6’2″ male with a briefcase,” Zisch tells me. Age, gender, height, and a range of physical and psychological factors can all influence how a person will use a building. “We want to improve not just the space, but the experience of the space,” says Zisch. Good architecture isn’t just about creating stunning features; it’s about subtle adaptations that might not even be noticeable to most people, she says.

The art gallery study is just the first step for researchers like Zisch and Spiers, who plan to explore other aspects of neuroscience and architecture in more simulated environments at PEARL. The team won’t have results for a while yet. But it’s a fascinating start. Watch this space.

Now read the rest of The Checkup

Read more from MIT Technology Review’s archive

Brain-monitoring technology has come a long way, and tech designed to read our minds and probe our memories is already being used. Futurist and legal ethicist Nita Farahany explained why we need laws to protect our cognitive liberty in a previous edition of The Checkup.

Listening in on the brain can reveal surprising insights into how this mysterious organ works. One team of neuroscientists found that our brains seem to oscillate between states of order and chaos.

Last year, MIT Technology Review published our design issue of the magazine. If you’re curious, this piece on the history and future of the word “design,” by Nicholas de Monchaux, head of architecture at MIT, might be a good place to start

Design covers much more than buildings, of course. Designers are creating new ways for users of prosthetic devices to feel more comfortable in their own skin—some of which have third thumbs, spikes, or “superhero skins.”

Achim Menges is an architect creating what he calls “self-shaping” structures with wood, which can twist and curve with changes in humidity. His approach is a low-energy way to make complex curved architectures, Menges told John Wiegand.

From around the web

Scientists are meant to destroy research samples of the poliovirus, as part of efforts to eradicate the disease it causes. But lab leaks of the virus may be more common than we’d like to think. (Science)

Neurofeedback allows people to watch their own brain activity in real time, and learn to control it. It could be a useful way to combat the impacts of stress. (Trends in Neurosciences)

Microbes, some of which cause disease in people, can travel over a thousand miles on wind, researchers have shown. Some appear to be able to survive their journey. (The Guardian)

Is the X chromosome involved in Alzheimer’s disease? A study of over a million people suggests so. (JAMA Neurology)

A growing number of men are paying thousands of dollars a year for testosterone therapies that are meant to improve their physical performance. But some are left with enlarged breasts, shrunken testicles, blood clots, and infertility. (The Wall Street Journal)

Het bericht Signal: We gaan helemaal niet in zee met WhatsApp verscheen eerst op DutchCowboys.

The internet has made it easier than ever before to encounter and spread conspiracy theories. And while some are harmless, others can be deeply damaging, sowing discord and even leading to unnecessary deaths.

Now, researchers believe they’ve uncovered a new tool for combating false conspiracy theories: AI chatbots. Researchers from MIT Sloan and Cornell University found that chatting about a conspiracy theory with a large language model (LLM) reduced people’s belief in it by about 20%—even among participants who claimed that their beliefs were important to their identity. The research is published today in the journal Science.

The findings could represent an important step forward in how we engage with and educate people who espouse such baseless theories, says Yunhao (Jerry) Zhang, a postdoc fellow affiliated with the Psychology of Technology Institute who studies AI’s impacts on society.

“They show that with the help of large language models, we can—I wouldn’t say solve it, but we can at least mitigate this problem,” he says. “It points out a way to make society better.” 

Few interventions have been proven to change conspiracy theorists’ minds, says Thomas Costello, a research affiliate at MIT Sloan and the lead author of the study. Part of what makes it so hard is that different people tend to latch on to different parts of a theory. This means that while presenting certain bits of factual evidence may work on one believer, there’s no guarantee that it’ll prove effective on another.

That’s where AI models come in, he says. “They have access to a ton of information across diverse topics, and they’ve been trained on the internet. Because of that, they have the ability to tailor factual counterarguments to particular conspiracy theories that people believe.”

The team tested its method by asking 2,190 crowdsourced workers to participate in text conversations with GPT-4 Turbo, OpenAI’s latest large language model.

Participants were asked to share details about a conspiracy theory they found credible, why they found it compelling, and any evidence they felt supported it. These answers were used to tailor responses from the chatbot, which the researchers had prompted to be as persuasive as possible.

Participants were also asked to indicate how confident they were that their conspiracy theory was true, on a scale from 0 (definitely false) to 100 (definitely true), and then rate how important the theory was to their understanding of the world. Afterwards, they entered into three rounds of conversation with the AI bot. The researchers chose three to make sure they could collect enough substantive dialogue.

After each conversation, participants were asked the same rating questions. The researchers followed up with all the participants 10 days after the experiment, and then two months later, to assess whether their views had changed following the conversation with the AI bot. The participants reported a 20% reduction of belief in their chosen conspiracy theory on average, suggesting that talking to the bot had fundamentally changed some people’s minds.

“Even in a lab setting, 20% is a large effect on changing people’s beliefs,” says Zhang. “It might be weaker in the real world, but even 10% or 5% would still be very substantial.”

The authors sought to safeguard against AI models’ tendency to make up information—known as hallucinating—by employing a professional fact-checker to evaluate the accuracy of 128 claims the AI had made. Of these, 99.2% were found to be true, while 0.8% were deemed misleading. None were found to be completely false. 

One explanation for this high degree of accuracy is that a lot has been written about conspiracy theories on the internet, making them very well represented in the model’s training data, says David G. Rand, a professor at MIT Sloan who also worked on the project. The adaptable nature of GPT-4 Turbo means it could easily be connected to different platforms for users to interact with in the future, he adds.

“You could imagine just going to conspiracy forums and inviting people to do their own research by debating the chatbot,” he says. “Similarly, social media could be hooked up to LLMs to post corrective responses to people sharing conspiracy theories, or we could buy Google search ads against conspiracy-related search terms like ‘Deep State.’”

The research upended the authors’ preconceived notions about how receptive people were to solid evidence debunking not only conspiracy theories, but also other beliefs that are not rooted in good-quality information, says Gordon Pennycook, an associate professor at Cornell University who also worked on the project. 

“People were remarkably responsive to evidence. And that’s really important,” he says. “Evidence does matter.”

As long as chatbots have been around, they have made things up. Such “hallucinations” are an inherent part of how AI models work. However, they’re a big problem for companies betting big on AI, like Google, because they make the responses it generates unreliable. 

Google is releasing a tool today to address the issue. Called DataGemma, it uses two methods to help large language models fact-check their responses against reliable data and cite their sources more transparently to users. 

The first of the two methods is called Retrieval-Interleaved Generation (RIG), which acts as a sort of fact-checker. If a user prompts the model with a question—like “Has the use of renewable energy sources increased in the world?”—the model will come up with a “first draft” answer. Then RIG identifies what portions of the draft answer could be checked against Google’s Data Commons, a massive repository of data and statistics from reliable sources like the United Nations or the Centers for Disease Control and Prevention. Next, it runs those checks and replaces any incorrect original guesses with correct facts. It also cites its sources to the user.

The second method, which is commonly used in other large language models, is called Retrieval-Augmented Generation (RAG). Consider a prompt like “What progress has Pakistan made against global health goals?” In response, the model examines which data in the Data Commons could help it answer the question, such as information about access to safe drinking water, hepatitis B immunizations, and life expectancies. With those figures in hand, the model then builds its answer on top of the data and cites its sources.

“Our goal here was to use Data Commons to enhance the reasoning of LLMs by grounding them in real-world statistical data that you could source back to where you got it from,” says Prem Ramaswami, head of Data Commons at Google. Doing so, he says, will “create more trustable, reliable AI.”

It is only available to researchers for now, but Ramaswami says access could widen further after more testing. If it works as hoped, it could be a real boon for Google’s plan to embed AI deeper into its search engine.  

However, it comes with a host of caveats. First, the usefulness of the methods is limited by whether the relevant data is in the Data Commons, which is more of a data repository than an encyclopedia. It can tell you the GDP of Iran, but it’s unable to confirm the date of the First Battle of Fallujah or when Taylor Swift released her most recent single. In fact, Google’s researchers found that with about 75% of the test questions, the RIG method was unable to obtain any usable data from the Data Commons. And even if helpful data is indeed housed in the Data Commons, the model doesn’t always formulate the right questions to find it. 

Second, there is the question of accuracy. When testing the RAG method, researchers found that the model gave incorrect answers 6% to 20% of the time. Meanwhile, the RIG method pulled the correct stat from Data Commons only about 58% of the time (though that’s a big improvement over the 5% to 17% accuracy rate of Google’s large language models when they’re not pinging Data Commons). 

Ramaswami says DataGemma’s accuracy will improve as it gets trained on more and more data. The initial version has been trained on only about 700 questions, and fine-tuning the model required his team to manually check each individual fact it generated. To further improve the model, the team plans to increase that data set from hundreds of questions to millions.

Serhii “Flash” Beskrestnov hates going to the front line. The risks terrify him. “I’m really not happy to do it at all,” he says. But to perform his particular self-appointed role in the Russia-Ukraine war, he believes it’s critical to exchange the relative safety of his suburban home north of the capital for places where the prospect of death is much more immediate. “From Kyiv,” he says, “nobody sees the real situation.”

So about once a month, he drives hundreds of kilometers east in a homemade mobile intelligence center: a black VW van in which stacks of radio hardware connect to an array of antennas on the roof that stand like porcupine quills when in use. Two small devices on the dash monitor for nearby drones. Over several days at a time, Flash studies the skies for Russian radio transmissions and tries to learn about the problems facing troops in the fields and in the trenches.

He is, at least in an unofficial capacity, a spy. But unlike other spies, Flash does not keep his work secret. In fact, he shares the results of these missions with more than 127,000 followers—including many soldiers and government officials—on several public social media channels. Earlier this year, for instance, he described how he had recorded five different Russian reconnaissance drones in a single night—one of which was flying directly above his van.

As well as the frontline information he shares on his public channels, he runs a “support service” for almost 2,000 military communications specialists on Signal and writes guides for building anti-drone equipment on a tight budget. “He’s a celebrity,” one special forces officer recently shouted to me over the thump of music in a Kyiv techno club. He’s “like a ray of sun,” an aviation specialist in Ukraine’s army told me. Flash tells me that he gets 500 messages every day asking for help.

Despite this reputation among rank-and-file service members—and maybe because of it—Flash has also become a source of some controversy among the upper echelons of Ukraine’s military, he tells me. The Armed Forces of Ukraine declined multiple requests for comment, but Flash and his colleagues claim that some high-ranking officials perceive him as a security threat, worrying that he shares too much information and doesn’t do enough to secure sensitive intel. As a result, some refuse to support or engage with him. Others, Flash says, pretend he doesn’t exist. Either way, he believes they are simply insecure about the value of their own contributions—“because everybody knows that Serhii Flash is not sitting in Kyiv like a colonel in the Ministry of Defense,” he tells me in the abrasive fashion that I’ve come to learn is typical of his character. 

But above all else, hours of conversations with numerous people involved in Ukraine’s defense, including frontline signalmen and volunteers, have made clear that even if Flash is a complicated figure, he’s undoubtedly an influential one. His work has become greatly important to those fighting on the ground, and he recently received formal recognition from the military for his contributions to the fight, with two medals of commendation—one from the commander of Ukraine’s ground forces, the other from the Ministry of Defense. 

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Despite a small number of semi-autonomous machines with a reduced reliance on radio communications, the drones that saturate the skies above the battlefield will continue to largely depend on this technology for the foreseeable future. And in this race for survival—as each side constantly tries to best the other, only to start all over again when the other inevitably catches up—Ukrainian soldiers need to develop creative solutions, and fast. As Ukraine’s wartime radio guru, Flash may just be one of their best hopes for doing that. 

“I know nothing about his background,” says “Igrok,” who works with drones in Ukraine’s 110th Mechanized Brigade and whom we are identifying by his call sign, as is standard military practice. “But I do know that most engineers and all pilots know nothing about radios and antennas. His job is definitely one of the most powerful forces keeping Ukraine’s aerial defense in good condition.”

And given the mounting evidence that both militaries and militant groups in other parts of the world are now adopting drone tactics developed in Ukraine, it’s not only his country’s fate that Flash may help to determine—but also the ways that armies wage war for years to come.

A prescient hobby

Before I can even start asking questions during our meeting in May, Flash is rummaging around in the back of the Flash-mobile, pulling out bits of gear for his own version of show-and-tell: a drone monitor with a fin-shaped antenna; a walkie-talkie labeled with a sticker from Russia’s state security service, the FSB; an approximately 1.5-meter-long foldable antenna that he says probably came from a US-made Abrams tank.

Flash has parked on a small wooded road beside the Kyiv Sea, an enormous water reservoir north of the capital. He’s wearing a khaki sweat-wicking polo shirt, combat trousers, and combat boots, with a Glock 19 pistol strapped to his hip. (“I am a threat to the enemy,” he tells me, explaining that he feels he has to watch his back.) As we talk, he moves from one side to the other, as if the electromagnetic waves that he’s studied since childhood have somehow begun to control the motion of his body.

Now 49, Flash grew up in a suburb of Kyiv in the ’80s. His father, who was a colonel in the Soviet army, recalls bringing home broken radio equipment for his preteen son to tinker with. Flash showed talent from the start. He attended an after-school radio club, and his father fixed an antenna to the roof of their apartment for him. Later, Flash began communicating with people in countries beyond the Iron Curtain. “It was like an open door to the big world for me,” he says.

Flash recalls with amusement a time when a letter from the KGB arrived at his family home, giving his father the fright of his life. His father didn’t know that his son had sent a message on a prohibited radio frequency, and someone had noticed. Following the letter, when Flash reported to the service’s office in downtown Kyiv, his teenage appearance confounded them. Boy, what are you doing here? Flash recalls an embarrassed official saying. 

Ukraine had been a hub of innovation as part of the Soviet Union. But by the time Flash graduated from military communications college in 1997, Ukraine had been independent for six years, and corruption and a lack of investment had stripped away the armed forces’ former grandeur. Flash spent just a year working in a military radio factory before he joined a private communications company developing Ukraine’s first mobile network, where he worked with technologies far more advanced than what he had used in the military. The  project was called “Flash.” 

A decade and a half later, Flash had risen through the ranks of the industry to become head of department at the progenitor to the telecommunications company Vodafone Ukraine. But boredom prompted him to leave and become an entrepreneur. His many projects included a successful e-commerce site for construction services and a popular video game called Isotopium: Chernobyl, which he and a friend based on the “really neat concept,” according to a PC Gamer review, of allowing players to control real robots (fitted with radios, of course) around a physical arena. Released in 2019, it also received positive reviews from Reuters and BBC News.

But within just a few years, an unexpected attack would hurl his country into chaos—and upend Flash’s life. 

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By early 2022, rumors were growing of a potential attack from Russia. Though he was still working on Isotopium, Flash began to organize a radio network across the northern suburbs of Kyiv in preparation. Near his home, he set up a repeater about 65 meters above ground level that could receive and then rebroadcast transmissions from all the radios in its network across a 200-square-kilometer area. Another radio amateur programmed and distributed handheld radios.

When Russian forces did invade, on February 24, they took both fiber-optic and mobile networks offline, as Flash had anticipated. The radio network became the only means of instant communications for civilians and, critically, volunteers mobilizing to fight in the region, who used it to share information about Russian troop movements. Flash fed this intel to several professional Ukrainian army units, including a unit of special reconnaissance forces. He later received an award from the head of the district’s military administration for his part in Kyiv’s defense. The head of the district council referred to Flash as “one of the most worthy people” in the region.

Within roughly a month of Russia’s incursion, Flash had created a private group called “Military Signalmen” on the encrypted messaging platform Signal, and invited civilian radio experts from his personal network to join alongside military communications specialists. “I am here to help you with technical issues,” he remembers writing to the group. “Ask me anything and I will try to find the answer for you.”

The kinds of questions that Flash and his civilian colleagues answered in the first months were often basic. Group members wanted to know how to update the firmware on their devices, reset their radios’ passwords, or set up the internal communications networks for large vehicles. Many of the people drafted as communications specialists in the Ukrainian military had little relevant experience; Flash claims that even professional soldiers lacked appropriate training and has referred to large parts of Ukraine’s military communications courses as “either nonsense or junk.” (The Korolov Zhytomyr Military Institute, where many communications specialists train, declined a request for comment.)

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He demonstrates handheld spectrum analyzers with custom Ukrainian firmware.

News of the Signal group spread by word of mouth, and it soon became a kind of 24-hour support service that communications specialists in every sector of Ukraine’s frontline force subscribed to. “Any military engineer can ask anything and receive the answer within a couple of minutes,” Flash says. “It’s a nice way to teach people very quickly.” 

As the war progressed into its second year, Military Signalmen became, to an extent, self-sustaining. Its members had learned enough to answer one another’s questions themselves. And this is where several members tell me that Flash has contributed the most value. “The most important thing is that he brought together all these communications specialists in one team,” says Oleksandr “Moto,” a technician at an EU mission in Kyiv and an expert in Motorola equipment, who has advised members of the group. (He asked to not be identified by his surname, due to security concerns.) “It became very efficient.”

Today, Flash and his partners continue to answer occasional questions that require more advanced knowledge. But over the past year, as the group demanded less of his time, Flash has begun to focus on a rapidly proliferating weapon for which his experience had prepared him almost perfectly: the drone.  

A race without end

Larger strike drones similar to the US-made Reaper have been familiar in other recent conflicts, but sophisticated air defenses have rendered them less dominant in this war. Ukraine and Russia are developing and deploying vast numbers of other types of drones—including the now-notorious “FPV,” or first-person view, drone that pilots operate by wearing goggles that stream video of its perspective. These drones, which can carry payloads large enough to destroy tanks, are cheap (costing as little as $400), easy to produce, and difficult to shoot down. They use direct radio communications to transmit video feeds, receive commands, and navigate.

MARCO CORDONE/SOPA IMAGES/SIPA USA VIA AP IMAGES

But their reliance on radio technology is a major vulnerability, because enemies can disrupt the signals that the drones emit—making them far less effective, if not inoperable. This form of electronic warfare—which most often involves emitting a more powerful signal at the same frequency as the operator’s—is called “jamming.”

Jamming, though, is an imperfect solution. Like drones, jammers themselves emit radio signals that can enable enemies to locate them. There are also effective countermeasures to bypass jammers. For example, a drone operator can use a tactic called “frequency hopping,” rapidly jumping between different frequencies to avoid a jammer’s signal. But even this method can be disrupted by algorithms that calculate the hopping patterns.

For this reason, jamming is a frequent focus of Flash’s work. In a January post on his Telegram channel, for instance, which people viewed 48,000 times, Flash explained how jammers used by some Ukrainian tanks were actually disrupting their own communications. “The cause of the problems is not direct interference with the reception range of the radio station, but very powerful signals from several [electronic warfare] antennae,” he wrote, suggesting that other tank crews experiencing the same problem might try spreading their antennas across the body of the tank. 

It is all part of an existential race in which Russia and Ukraine are constantly hunting for new methods of drone operation, drone jamming, and counter-jamming—and there’s no end in sight. In March, for example, Flash says, a frontline contact sent him photos of a Russian drone with what looks like a 10-kilometer-long spool of fiber-optic cable attached to its rear—one particularly novel method to bypass Ukrainian jammers. “It’s really crazy,” Flash says. “It looks really strange, but Russia showed us that this was possible.”

Flash’s trips to the front line make it easier for him to track developments like this. Not only does he monitor Russian drone activity from his souped-up VW, but he can study the problems that soldiers face in situ and nurture relationships with people who may later send him useful intel—or even enemy equipment they’ve seized. “The main problem is that our generals are located in Kyiv,” Flash says. “They send some messages to the military but do not understand how these military people are fighting on the front.”

Besides the advice he provides to Ukrainian troops, Flash also publishes online his own manuals for building and operating equipment that can offer protection from drones. Building their own tools can be soldiers’ best option, since Western military technology is typically expensive and domestic production is insufficient. Flash recommends buying most of the parts on AliExpress, the Chinese e-commerce platform, to reduce costs.

While all his activity suggests a close or at least cooperative relationship between Flash and Ukraine’s military, he sometimes finds himself on the outside looking in. In a post on Telegram in May, as well as during one of our meetings, Flash shared one of his greatest disappointments of the war: the military’s refusal of his proposal to create a database of all the radio frequencies used by Ukrainian forces. But when I mentioned this to an employee of a major electronic warfare company, who requested anonymity to speak about the sensitive subject, he suggested that the only reason Flash still complains about this is that the military hasn’t told him it already exists. (Given its sensitivity, MIT Technology Review was unable to independently confirm the existence of this database.) 

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This anecdote is emblematic of Flash’s frustration with a military complex that may not always want his involvement. Ukraine’s armed forces, he has told me on several occasions, make no attempt to collaborate with him in an official manner. He claims not to receive any financial support, either. “I’m trying to help,” he says. “But nobody wants to help me.”

Both Flash and Yurii Pylypenko, another radio enthusiast who helps Flash manage his Telegram channel, say military officials have accused Flash of sharing too much information about Ukraine’s operations. Flash claims to verify every member of his closed Signal groups, which he says only discuss “technical issues” in any case. But he also admits the system is not perfect and that Russians could have gained access in the past. Several of the soldiers I interviewed for this story also claimed to have entered the groups without Flash’s verification process. 

It’s ultimately difficult to determine if some senior staff in the military hold Flash at arm’s length because of his regular, often strident criticism—or whether Flash’s criticism is the result of being held at arm’s length. But it seems unlikely either side’s grievances will subside soon; Pylypenko claims that senior officers have even tried to blackmail him over his involvement in Flash’s work. “They blame my help,” he wrote to me over Telegram, “because they think Serhii is a Russian agent reposting Russian propaganda.” 

Is the world prepared?

Flash’s greatest concern now is the prospect of Russia overwhelming Ukrainian forces with the cheap FPV drones. When they first started deploying FPVs, both sides were almost exclusively targeting expensive equipment. But as production has increased, they’re now using them to target individual soldiers, too. Because of Russia’s production superiority, this poses a serious danger—both physical and psychological—to Ukrainian soldiers. “Our army will be sitting under the ground because everybody who goes above ground will be killed,” Flash says. Some reports suggest that the prevalence of FPVs is already making it difficult for soldiers to expose themselves at all on the battlefield.

To combat this threat, Flash has a grand yet straightforward idea. He wants Ukraine to build a border “wall” of jamming systems that cover a broad range of the radio spectrum all along the front line. Russia has already done this itself with expensive vehicle-based systems, but these present easy targets for Ukrainian drones, which have destroyed several of them. Flash’s idea is to use a similar strategy, albeit with smaller, cheaper systems that are easier to replace. He claims, however, that military officials have shown no interest.

Although Flash is unwilling to divulge more details about this strategy (and who exactly he pitched it to), he believes that such a wall could provide a more sustainable means of protecting Ukrainian troops. Nevertheless, it’s difficult to say how long such a defense might last. Both sides are now in the process of developing artificial-intelligence programs that allow drones to lock on to targets while still outside enemy jamming range, rendering them jammer-proof when they come within it. Flash admits he is concerned—and he doesn’t appear to have a solution.

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He’s not alone. The world is entirely unprepared for this new type of warfare, says Yaroslav Kalinin, a former Ukrainian intelligence officer and the CEO of Infozahyst, a manufacturer of equipment for electronic warfare. Kalinin recounts talking at an electronic-warfare-focused conference in Washington, DC, last December where representatives from some Western defense companies weren’t able to recognize the basic radio signals emitted by different types of drones. “Governments don’t count [drones] as a threat,” he says. “I need to run through the streets like a prophet—the end is near!”

Nevertheless, Ukraine has become, in essence, a laboratory for a new era of drone warfare—and, many argue, a new era of warfare entirely. Ukraine’s and Russia’s soldiers are its technicians. And Flash, who sometimes sleeps curled up in the back of his van while on the road, is one of its most passionate researchers. “Military developers from all over the world come to us for experience and advice,” he says. Only time will tell whether their contributions will be enough to see Ukraine through to the other side of this war. 

Charlie Metcalfe is a British journalist. He writes for magazines and newspapers, including Wired, the Guardian, and MIT Technology Review.

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

One way to know where a field is going? Take a look at what the sharpest new innovators are working on.

Good news for all of us: MIT Technology Review’s list of 35 Innovators Under 35 just dropped. And a decent number of the people who made the list are working in fields that touch climate and energy in one way or another.

Looking through, I noticed a few trends that might provide some hints about the future of climate tech. Let’s dig into this year’s list and consider what these innovators’ work might mean for efforts to combat climate change.

Power to the people

Perhaps unsurprisingly, quite a few innovators on this list are working on energy—and many of them have an interest in making energy consistently available where and when it’s needed. Wind and solar are getting cheap, but we need solutions for when the sun isn’t shining and the wind isn’t blowing.

Tim Latimer cofounded Fervo Energy, a geothermal company hoping to provide consistently available, carbon-free energy using Earth’s heat. You may be familiar with his work, since Fervo was on our list of 15 Climate Tech Companies to Watch in 2023.

Another energy-focused innovator on the list is Andrew Ponec of Antora Energy, a company working to build thermal energy storage systems. Basically, the company’s technology heats up blocks when cheap renewables are available, and then stores that heat and delivers it to industrial processes that need constant power. (You, the readers, named thermal energy storage the readers’ choice on this year’s 10 Breakthrough Technologies list.)

Rock stars

While new ways of generating electricity and storing energy can help cut our emissions in the future, other people are focused on how to clean up the greenhouse gases already in the atmosphere. At this point, removing carbon dioxide from the atmosphere is basically required for any scenario where we limit warming to 1.5 °C over preindustrial levels. A few of the new class of innovators are turning to rocks for help soaking up and locking away atmospheric carbon. 

Noah McQueen cofounded Heirloom Carbon Technologies, a carbon removal company. The technology works by tweaking the way minerals soak up carbon dioxide from the air (before releasing it under controlled conditions, so they can do it all again). The company has plans for facilities that could remove hundreds of thousands of tons of carbon dioxide each year. 

Another major area of research focuses on how we might store captured carbon dioxide. Claire Nelson is the cofounder of Cella Mineral Storage, a company working on storage methods to better trap carbon dioxide underground once it’s been mopped up.  

Material world

Finally, some of the most interesting work on our new list of innovators is in materials. Some people are finding new ones that could help us address our toughest problems, and others are trying to reinvent old ones to clean up their climate impacts.

Julia Carpenter found a way to make a foam-like material from metal. Its high surface area makes it a stellar heat sink, meaning it can help cool things down efficiently. It could be a huge help in data centers, where 40% of energy demand goes to cooling.

And I spoke with Cody Finke, cofounder and CEO of Brimstone, a company working on cleaner ways of making cement. Cement alone is responsible for nearly 7% of global greenhouse-gas emissions, and about half of those come from chemical reactions necessary to make it. Finke and Brimstone are working to wipe out the need for these reactions by using different starting materials to make this crucial infrastructural glue.

Addressing climate change is a sprawling challenge, but the researchers and founders on this list are tackling a few of the biggest issues I think about every day. 

Ensuring that we can power our grid, and all the industrial processes that we rely on for the stuff in our daily lives, is one of the most substantial remaining challenges. Removing carbon dioxide from the atmosphere in an efficient, cheap process could help limit future warming and buy us time to clean up the toughest sectors. And finding new materials, and new methods of producing old ones, could be a major key to unlocking new climate solutions. 

To read more about the folks I mentioned here and other innovators working in climate change and beyond, check out the full list.

Now read the rest of The Spark

Related reading

Fervo Energy (cofounded by 2024 innovator Tim Latimer) showed last year that its wells can be used like a giant underground battery.

A growing number of companies—including Antora Energy, whose CEO Andrew Ponec is a 2024 innovator—are working to bring thermal energy storage systems to heavy industry.

Cement is one of our toughest challenges, as Brimstone CEO and 2024 innovator Cody Finke will tell you. I wrote about Brimstone and other efforts to reinvent cement earlier this year.

Another thing

We need a whole lot of metals to address climate change, from the copper in transmission lines to the nickel in lithium-ion batteries that power electric vehicles. Some researchers think plants might be able to help. 

Roughly 750 species of plants are so-called hyperaccumulators, meaning they naturally soak up and tolerate relatively high concentrations of metal. A new program is funding research into how we might use this trait to help source nickel, and potentially other metals, in the future. Read the full story here.

Keeping up with climate  

A hurricane that recently formed in the Gulf of Mexico is headed for Louisiana, ending an eerily quiet few weeks of the season. (Scientific American)

→ After forecasters predicted a particularly active season, the lull in hurricane activity was surprising. (New Scientist)

Rising sea levels are one of the symptoms of a changing climate, but nailing down exactly what “sea level” means is more complicated than you might think. We’ve gotten better at measuring sea level over the past few centuries, though. (New Yorker)

The US Department of Energy’s Loan Programs Office has nearly $400 million in lending authority. This year’s election could shift the focus of that office drastically, making it a bellwether of how the results could affect energy priorities. (Bloomberg)

What if fusion power ends up working, but it’s too expensive to play a significant role on the grid? Some modelers think the technology will remain expensive and could come too late to make a dent in emissions. (Heatmap)

Electric-vehicle sales are up overall, but some major automakers are backing away from goals on zero-emissions vehicles. Even though sales are increasing, uptake is slower than many thought it would be, contributing to the nervous energy in the industry. (Canary Media)

It’s a tough time to be in the business of next-generation batteries. The woes of three startups reveal that difficult times are here, likely for a while. (The Information)

WhatsApp is now letting small businesses in India sign up for a Meta Verified badge and giving them the ability to send customized messages to customers.

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No one likes standing in line. I was reminded of just how awful the experience can be last Saturday, while being herded like cattle through a two-hour queue for a nightclub in unseasonably cold weather. I’d not soon repeat the experience. Fortunately, there’s a startup for that. LineLeap, backed by Y Combinator, lets people pay […]

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Robots perceive the world around them very differently from the way humans do. 

When we walk down the street, we know what we need to pay attention to—passing cars, potential dangers, obstacles in our way—and what we don’t, like pedestrians walking in the distance. Robots, on the other hand, treat all the information they receive about their surroundings with equal importance. Driverless cars, for example, have to continuously analyze data about things around them whether or not they are relevant. This keeps drivers and pedestrians safe, but it draws on a lot of energy and computing power. What if there’s a way to cut that down by teaching robots what they should prioritize and what they can safely ignore?

That’s the principle underpinning “lazy robotics,” a field of study championed by René van de Molengraft, a professor at Eindhoven University of Technology in the Netherlands. He believes that teaching all kinds of robots to be “lazier” with their data could help pave the way for machines that are better at interacting with things in their real-world environments, including humans. Essentially, the more efficient a robot can be with information, the better.

Van de Molengraft’s lazy robotics is just one approach researchers and robotics companies are now taking as they train their robots to complete actions successfully, flexibly, and in the most efficient manner possible.

Teaching them to be smarter when they sift through the data they gather and then de-prioritize anything that’s safe to overlook will help make them safer and more reliable—a long-standing goal of the robotics community.

Simplifying tasks in this way is necessary if robots are to become more widely adopted, says Van de Molengraft, because their current energy usage won’t scale—it would be prohibitively expensive and harmful to the environment. “I think that the best robot is a lazy robot,” he says. “They should be lazy by default, just like we are.”

Learning to be lazier

Van de Molengraft has hit upon a fun way to test these efforts out: teaching robots to play soccer. He recently led his university’s autonomous robot soccer team, Tech United, to victory at RoboCup, an annual international robotics and AI competition that tests robots’ skills on the soccer field. Soccer is a tough challenge for robots, because both scoring and blocking goals require quick, controlled movements, strategic decision-making, and coordination. 

Learning to focus and tune out distractions around them, much as the best human soccer players do, will make them not only more energy efficient (especially for robots powered by batteries) but more likely to make smarter decisions in dynamic, fast-moving situations.

Tech United’s robots used several “lazy” tactics to give them an edge over their opponents during the RoboCup. One approach involved creating a “world model” of a soccer pitch that identifies and maps out its layout and line markings—things that remain the same throughout the game. This frees the battery-powered robots from constantly scanning their surroundings, which would waste precious power. Each robot also shares what its camera is capturing with its four teammates, creating a broader view of the pitch to help keep track of the fast-moving ball. 

Previously, the robots needed a precise, pre-coded trajectory to move around the pitch. Now Van de Molengraft and his team are experimenting with having them choose their own paths to a specified destination. This saves the energy needed to track a specific journey and helps the robots cope with obstacles they may encounter along the way.

The group also successfully taught the squad to execute “penetrating passes”—where a robot shoots toward an open region in the field and communicates to the best-positioned member of its team to receive it—and skills such as receiving or passing the ball within configurations such as triangles. Giving the robots access to world models built using data from the surrounding environment allows them to execute their skills anywhere on the pitch, instead of just in specific spots.

Beyond the soccer pitch

While soccer is a fun way to test how successful these robotics methods are, other researchers are also working on the problem of efficiency—and dealing with much higher stakes.

Making robots that work in warehouses better at prioritizing different data inputs is essential to ensuring that they can operate safely around humans and be relied upon to complete tasks, for example. If the machines can’t manage this, companies could end up with a delayed shipment, damaged goods, an injured human worker—or worse, says Chris Walti, the former head of Tesla’s robotics division. 

Walti left the company to set up his own firm after witnessing how challenging it was to get robots to simply move materials around. His startup, Mytra, designs fully autonomous machines that use computer vision and an AI reinforcement-learning system to give them awareness of other robots closest to them, and to help them reason and collaborate to complete tasks (like moving a broken pallet) in much more computationally efficient ways. 

The majority of mobile robots in warehouses today are controlled by a single central “brain” that dictates the paths they follow, meaning a robot has to wait for instructions before it can do anything. Not only is this approach difficult to scale, but it consumes a lot of central computing power and requires very dependable communication links.

Mytra believes it’s hit upon a significantly more efficient approach, which acknowledges that individual robots don’t really need to know what hundreds of other robots are doing on the other side of the warehouse. Its machine-learning system cuts down on this unnecessary data, and the computing power it would take to process it, by simulating the optimal route each robot can take through the warehouse to perform its task. This enables them to act much more autonomously. 

“In the context of soccer, being efficient allows you to score more goals. In the context of manufacturing, being efficient is even more important because it means a system operates more reliably,” he says. “By providing robots with the ability to to act and think autonomously and efficiently, you’re also optimizing the efficiency and the reliability of the broader operation.”

While simplifying the types of information that robots need to process is a major challenge, inroads are being made, says Daniel Polani, a professor from the University of Hertfordshire in the UK who specializes in replicating biological processes in artificial systems. He’s also a fan of the RoboCup challenge—in fact, he leads his university’s Bold Hearts robot soccer team, which made it to the second round of this year’s RoboCup’s humanoid league.

“Organisms try not to process information that they don’t need to because that processing is very expensive, in terms of metabolic energy,” he says. Polani is interested in applying these  lessons from biology to the vast networks that power robots to make them more efficient with their information. Reducing the amount of information a robot is allowed to process will just make it weaker depending on the nature of the task it’s been given, he says. Instead, they should learn to use the data they have in more intelligent ways.

Simplifying software

Amazon, which has more than 750,000 robots, the largest such fleet in the world, is also interested in using AI to help them make smarter, safer, and more efficient decisions. Amazon’s robots mostly fall into two categories: mobile robots that move stock, and robotic arms designed to handle objects. The AI systems that power these machines collect millions of data points every day to help train them to complete their tasks. For example, they must learn which item to grasp and move from a pile, or how to safely avoid human warehouse workers. These processes require a lot of computing power, which the new techniques can help minimize.

Generally, robotic arms and similar “manipulation” robots use machine learning to figure out how to identify objects, for example. Then they follow hard-coded rules or algorithms to decide how to act. With generative AI, these same robots can predict the outcome of an action before even attempting it, so they can choose the action most likely to succeed or determine the best possible approach to grasping an object that needs to be moved. 

These learning systems are much more scalable than traditional methods of training robots, and the combination of generative AI and massive data sets helps streamline the sequencing of a task and cut out layers of unnecessary analysis. That’s where the savings in computing power come in. “We can simplify the software by asking the models to do more,” says Michael Wolf, a principal scientist at Amazon Robotics. “We are entering a phase where we’re fundamentally rethinking how we build autonomy for our robotic systems.”

Achieving more by doing less

This year’s RoboCup competition may be over, but Van de Molengraft isn’t resting on his laurels after his team’s resounding success. “There’s still a lot of computational activities going on in each of the robots that are not per se necessary at each moment in time,” he says. He’s already starting work on new ways to make his robotic team even lazier to gain an edge on its rivals next year.  

Although current robots are still nowhere near able to match the energy efficiency of humans, he’s optimistic that researchers will continue to make headway and that we’ll start to see a lot more lazy robots that are better at their jobs. But it won’t happen overnight. “Increasing our robots’ awareness and understanding so that they can better perform their tasks, be it football or any other task in basically any domain in human-built environments—that’s a continuous work in progress,” he says.

A flaw in the design of WhatsApp's "View Once" privacy feature let's anyone save pictures and videos that should be ephemeral.

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Roblox plans to roll out a generative AI tool that will let creators make whole 3D scenes just using text prompts, it announced today. 

Once it’s up and running, developers on the hugely popular online game platform will be able to simply write “Generate a race track in the desert,” for example, and the AI will spin one up. Users will also be able to modify scenes or expand their scope—say, to change a daytime scene to night or switch the desert for a forest. 

Although developers can already create similar scenes like this manually in the platform’s creator studio, Roblox claims its new generative AI model will make the changes happen in a fraction of the time. It also claims that it will give developers with minimal 3D art skills the ability to craft more compelling environments. The firm didn’t give a specific date for when the tool will be live.

Developers are already excited. “Instead of sitting and doing it by hand, now you can test different approaches,” says Marcus Holmström, CEO of The Gang, a company that builds some of the top games on Roblox.  “For example, if you’re going to build a mountain, you can do different types of mountains, and on the fly, you can change it. Then we would tweak it and fix it manually so it fits. It’s going to save a lot of time.”

Roblox’s new tool works by “tokenizing” the 3D blocks that make up its millions of in-game worlds, or treating them as units that can be assigned a numerical value on the basis of how likely they are to come next in a sequence. This is similar to the way in which a large language model handles words or fractions of words. If you put “The capital of France is …” into a large language model like GPT-4, for example, it assesses what the next token is most likely to be. In this case, it would be “Paris.” Roblox’s system handles 3D blocks in much the same way to create the environment, block by most likely next block. 

Finding a way to do this has been difficult, for a couple of reasons. One, there’s far less data for 3D environments than there is for text. To train its models, Roblox has had to rely on user-generated data from creators as well as external data sets. 

“Finding high-quality 3D information is difficult,” says Anupam Singh, vice president of AI and growth engineering at Roblox. “Even if you get all the data sets that you would think of, being able to predict the next cube requires it to have literally three dimensions, X, Y, and Z.”

The lack of 3D data can create weird situations, where objects appear in unusual places—a tree in the middle of your racetrack, for example. To get around this issue, Roblox will use a second AI model that has been trained on more plentiful 2D data, pulled from open-source and licensed data sets, to check the work of the first one. 

Basically, while one AI is making a 3D environment, the 2D model will convert the new environment to 2D and assess whether or not the image is logically consistent. If the images don’t make sense and you have, say, a cat with 12 arms driving a racecar, the 3D AI generates a new block again and again until the 2D AI “approves.”

Roblox game designers will still need to be involved in crafting fun game environments for the platform’s millions of players, says Chris Totten, an associate professor in the animation game design program at Kent State University. “A lot of level generators will produce something that’s plain and flat. You need a human guiding hand,” he says. “It’s kind of like people trying to do an essay with ChatGPT for a class. It is also going to open up a conversation about what does it mean to do good, player-responsive level design?”

ROBLOX

The new tool is part of Roblox’s push to integrate AI into all its processes. The company currently has 250 AI models live. One AI analyzes voice chat in real time and screens for bad language, instantly issuing reprimands and possible bans for repeated infractions.

Roblox plans to open-source its 3D foundation model so that it can be modified and used as a basis for innovation. “We’re doing it in open source, which means anybody, including our competitors, can use this model,” says Singh. 

Getting it into as many hands as possible also opens creative possibilities for developers who are not as skilled at creating Roblox environments. “There are a lot of developers that are working alone, and for them, this is going to be a game changer, because now they don’t have to try to find someone else to work with,” says Holmström.

By the end of this decade, AI will likely surpass humans at all cognitive tasks, igniting the scientific revolution that futurists have long imagined. Digital scientists will have perfect memory of every research paper ever published and think a million times faster than we can. Our plodding progress in fields like robotics, nanotechnology, and genomics will become a sprint. Within the lifetimes of most people alive today, society will achieve radical material abundance, and medicine will conquer aging itself. But our destiny isn’t a hollow Jetsons future of gadgetry and pampered boredom. By freeing us from the struggle to meet the most basic needs, technology will serve our deepest human aspirations to learn, create, and connect.

This sounds fantastically utopian, but humans have made such a leap before. Our hunter-gatherer ancestors lived on a razor’s edge of precarity. Every winter was a battle with starvation. Violence or infection likely killed most people before age 30. The constant struggle to survive left little opportunity for invention or philosophy. But the discovery of agriculture afforded just enough stability to create a feedback loop. Material surplus meant some could develop skills that created even larger surpluses. In a blink of evolutionary time, civilization appeared—literature, law, science, engineering. While modern life can often feel like a rat race, to our paleolithic ancestors, we would seem to enjoy impossible abundance and freedom.

What will the next leap look like? One of the first shifts will be in learning. From the ancient Greeks through the Enlightenment, education’s primary goal was to nourish the mind and cultivate virtue. But the Industrial Revolution reframed education as training for economic success in an increasingly technical society. Today, kids are told from an early age that they have to study for instrumental reasons—to get into a good college, to get into a good grad school, to get a good job. All too often, this deadens their natural curiosity and love of learning.

As superhuman AI makes most goods and services so abundant as to be almost free, the need to structure our lives around jobs will fade away. We’ll then be free to learn for its own sake—nurturing the knowledge and wisdom that define our humanity. And learning itself will become vastly richer. Instead of just reading about Rome in dry text, you’ll be able to explore the Forum in virtual reality and debate an AI Cicero trained on the original’s speeches. Instead of getting lost in a crowded lecture hall, you’ll work one on one with a supremely patient digital tutor that’s been trained by the greatest teachers on Earth and knows exactly how you learn best. 

AI tools will also supercharge your creativity. Today, expressing your artistic impulses requires both technical skill and resources—for films and games, sometimes hundreds of millions of dollars. These bottlenecks keep countless brilliant ideas trapped in people’s heads, and we are all poorer for it. But systems like Midjourney and Sora let us glimpse a different future. You’ll be able to speak a painting into being like a muse in Rembrandt’s ear. Or you’ll hum a tune and then work with a digital Wagner to orchestrate it into a symphony.

Thanks to this creative revolution, the coming medical breakthroughs won’t just offer longer lives but fuller ones, enriched by all the art, music, literature, film, and games created by humanity during those extra years. Most important, you’ll share all this with the people you love most. Imagine being healthy as you watch your great-grandchildren grow into adults! And material abundance will ease economic pressures and afford families the quality time together they’ve long yearned for. 

This is the profound leap that awaits us—a future where our technological wonders don’t diminish our humanity but allow it to flourish.

Ray Kurzweil is a technologist and futurist and the author, most recently, of The Singularity Is Nearer: When We Merge with AI. The views represented here are his own.

Around 2012, at a bakery in Cambridge, Massachusetts, Skylar Tibbits noticed someone wearing a shirt with the logo of a 3D-printing company. Tibbits, a designer and computer scientist, approached her and posed a question: “Why can’t I print something that walks off the machine?”

The idea kicked off a multiyear collaboration between the industrial 3D-printing company behind the logo, called Stratasys, and Tibbits’s Self-Assembly Lab at the Massachusetts Institute of Technology. Together, they explored 3D-printed materials that, while they couldn’t walk off the machine, could change their shape or properties after being printed—a concept that Tibbits dubbed “4D printing,” where the fourth dimension is time. Today, 4D printing is its own field—the subject of a professional society and thousands of papers, with researchers around the world looking into potential applications from self-adjusting biomedical devices to soft robotics. 

COURTESY OF SKYLAR TIBBITS

The concept of materials that transform—specifically, that “remember” their form after being deformed—had already been around for a couple of decades, says Thomas Gries, a mechanical engineer who was inspired by Tibbits’s innovations to conduct research on 4D textiles at RWTH Aachen University in Germany. “But to give it a name and bring it to a next stage of, let’s say, perception … this was definitely the main breakthrough by Skylar Tibbits,” he says.

Not long after 4D printing took off, Tibbits was already looking toward a new challenge: What other capabilities can we build into materials? And can we do that without printing?

Tibbits still does a lot of printing in his Self-Assembly Lab, which he founded around 2011. It recently spun out a company, Rapid Liquid Print, that can print large, stretchable products like furniture or prosthetics inside a gel bath; Tibbits also invented a novel technique called liquid metal printing that makes furniture from molten aluminum in a matter of seconds. But his research extends far beyond printing into a world that he refers to as “programmable materials”—those that can transform, sense, reconfigure in shape or property, or self-assemble without relying on robotic mechanisms. 

Tibbits’s interest in transforming materials stretches back to his bachelor’s degree in architecture at Philadelphia University in the 2000s. There he became “obsessively interested” in two emerging fields: digital fabrication (in which machines like 3D printers or laser cutters use code to physically make things) and the use of computation for design. He started working at a classmate’s father’s sign shop, which had an early computer numerically controlled machine. (The classmate, Jared Laucks, now co-directs the Self-Assembly Lab with Tibbits.) “We had unprecedented access to this early digital fabrication tool that our school didn’t even have at the time,” Tibbits recalls. Meanwhile, he was teaching himself to code so that he could design and fabricate things computationally. 

By the time he arrived at MIT, where he earned separate master’s degrees in computer science and design computation, 3D printing had exploded in popularity. True to his nature, Tibbits was already looking to the next unknown. His start in architecture and a few stints at design firms continued to influence his curiosity: He had studied code for design and code for digital fabrication, but there was not yet a way to code for assembling parts into a whole.  

“Most things that are assembled are still either by hand or it’s like a robot in a factory,” he says. “There is no elegant process for building very complicated things.” 

He imagined a way to embed information into material parts so that they could build themselves. That led him to self-assembly, which refers to components that come together on their own. For example, an early project at the Self-Assembly Lab involved placing unique chair parts, each designed to fit into one precise location in a final structure, into a tank of turbulent water, which caused the parts to bump into each other and, after several hours, click into their proper spot to form a chair. Around the same time, he became interested in the idea of programmable materials, in which properties and geometry—like what a yarn is made of and how it’s woven together—can determine behavior. 

Over the past decade, Tibbits’s team has worked on a long list of projects in many industries, from fashion to aerospace. He has helped make specially knitted spacesuits, developed an engine component for Airbus, and printed shoes for Converse. For BMW, he worked on printed car seats that can morph around a passenger’s body. For Google, he designed a structure for meeting spaces that can contract into the ceiling when not in use. With the National Science Foundation, he integrated dynamic braille into a textile sleeve. For Arc’teryx, he collaborated on a wearable textile prototype that he and the company’s senior design director, Greg Grenzke, tested out on a ski slope at Whistler Blackcomb in Canada. In all cases, Tibbits sees his team’s role as making the impossible possible. “That’s where we’re super, super good,” he says. 

“[Tibbits is] one of those people that thinks anything is possible,” says Grenzke. “And he’s very exploratory, which I think for innovation and more big blue-sky thinking—that’s a must-have.” 

It’s particularly important in manufacturing, where old methods often go unquestioned and change is difficult. “Sometimes people get a hammer, and then they just go around their whole lives looking for nails even when it’d be better to pick up a screwdriver,” says Michael Dickey, a chemical engineer at North Carolina State University in Raleigh, who has collaborated with Tibbits. “What Skylar’s done has been, I would say, fearless.” 

That mindset was important to Emeco, a US-based furniture company established in the 1940s. When they were struggling to make the foam in their upholstery more sustainable, Tibbits’s team was “really good at taking a step back and saying, ‘Okay, well, what if it didn’t have to be that way?’” says Jaye Buchbinder, a product development engineer at the company. “In our heads, it’s ‘Let’s find a better foam,’ and in their heads, it’s ‘How do you not use foam?’” Buchbinder also worked with Tibbits to run a course at MIT about reimagining a chair for the future—one that could be reconfigured or infinitely recycled. 

Lately, Tibbits has turned his attention to textiles—in particular, using fibers and yarns to create active structures that can sense and transform. For example, climate-adaptive clothing can open and close its “pores” in response to heat in order to regulate the wearer’s temperature. Or extreme heat can be harnessed selectively to custom-tailor a generic article of clothing. The lab, in collaboration with the fashion brand Ministry of Supply, recently showcased a knitted dress prototype that can be sized and styled in the store to fit a customer’s needs. A robotic arm applies heat to specific parts of the dress, shortening fibers and changing the garment’s shape.  

In all cases, the magic lies in the process. “There’s nothing magic about the material,” says Tibbits. “Every material is active.” In fact, that property is usually considered a nuisance: think warping wood. But, he says, “if you can help guide it to do some kind of useful transformation, then that’s great.”

If it all sounds a little whimsical, it is. Tibbits sees the importance of creating something practical—the textiles are exciting to him because they can be made on preexisting factory machines, which makes them possible to scale—but he likes it when the result is also radical. One of his most out-there projects is happening on the other side of the world in the Maldives, where the team has worked in collaboration with a company called Invena on underwater structures designed to influence wave energy and promote sand accumulation in certain spots along island shores—an alternative to dredging. The idea is to harness natural forces, rather than fight them, to help protect coasts against erosion and rising sea levels.

The way Tibbits sees it, his position in academia allows his lab to “fail often and, frankly, waste time and money”—resources that most companies could not afford to waste. He sees it as his obligation as a designer to spend time considering the radical and irrelevant.

“Sometimes they’re playful, sometimes they’re weird, sometimes they’re funny,” he says of his designs. “Hopefully, eventually, they’re useful, they’re helpful, they’re important.” 

Anna Gibbs is a freelance science journalist based in New York City.

Prominent AI researchers expect the arrival of artificial general intelligence anywhere between “the next couple of years” and “possibly never.” At the same time, leading economists disagree about the potential impact of AI: Some anticipate a future of perpetually accelerating productivity, while others project more modest gains. But most experts agree that technological advancement, however buoyant, is no guarantee that everyone benefits. 

And unfortunately, even though some of the most notable AI R&D efforts declare that making sure everyone benefits is a key goal or guiding principle, ensuring that AI helps create a more inclusive future remains one of the least invested-in areas of AI governance. This might seem natural given the state of the field: The impact AI will have on labor and inequality is still highly uncertain, making it difficult to design interventions. But we know at least some of the factors that will influence the interplay between AI and inequality over the next few decades. Paying attention to those can help us make the idea that AI will benefit everyone into more than just a pipe dream.

Because they’re largely driven by the private sector, AI development and use are heavily influenced by the incentive structures of the world’s economies. And if there is something important that can be predicted with reasonable certainty about those economies, it is their future demographic composition. There is a stark divide between higher-income countries, whose populations are aging rapidly and will shrink without migration, and low- and lower-middle income countries, which will continue to grow for the rest of the century thanks to the excess of births over deaths.

What does this have to do with AI? AI development is concentrated in the aging countries, and thus it will follow the path set by the realities, needs, and incentives in those places. Aging countries are seeing the ratio of working-age people to retirees collapse, making it more difficult to sustain pension schemes and contain health-care costs. Countries looking to maintain their retirees’ living standards and their overall economic dynamism will seek ways to expand their effective labor force, be that with humans or with artificial agents. Limited (and likely highly unpopular) gains could come from increasing the retirement age. More sizable gains could come from immigration. But keeping the ratio of the working-age to retiree populations constant would require a significant increase in immigration to the higher-income countries. Widespread anti-immigration sentiment makes that seem unlikely, though opinions could change relatively quickly when people are faced with the prospect of diminishing pensions and rising health-care costs.  

If overly restrictive immigration policies do not relax in rich countries, we will likely see the economic incentives to fill labor gaps with AI go into overdrive over the next few decades. It might seem on the surface that this won’t exacerbate inequality if there are fewer people than available jobs. But if the trend is associated with an uneven distribution of gains and losses, increasingly precarious employment, excessive surveillance of workers, and digitization of their know-how without adequate compensation, we should expect a spike in inequality. 

And even if the efforts to replace labor with AI unfold incredibly well for the populations of rich countries, they might dramatically deepen inequality between countries. For the rest of the 21st century, lower-income countries will continue to have young, growing populations in need not of labor-­replacing tech, but of gainful employment. The problem is that machines invented to fill in for missing workers in countries with labor shortages often quickly spread even to countries where unemployment is in the double digits and the majority of the working population is employed by unregistered informal businesses. That is how we find self-service kiosks in South African restaurants and Indian airports, replacing formal-­sector jobs in these and many more countries struggling to create enough of them. 

In such a world, many beneficial applications of AI could remain relatively underdeveloped compared with the merely labor-saving ones. For example, efforts to develop AI for climate-­change resilience, early prediction of natural disasters, or affordable personalized tutoring might end up taking a back seat to projects geared to cutting labor costs in retail, hospitality, and transportation. Deliberate, large-scale efforts by governments, development banks, and philanthropies will be needed to make sure AI is used to help address the needs of poorer countries, not only richer ones. The budgets for such efforts are currently quite small, leaving AI on its default path—which is far from inclusive. 

But default is not destiny. We could choose to channel more public R&D efforts toward pressing global challenges like accelerating the green transition and improving educational outcomes. We could invest more in creating and supporting AI development hubs in lower-income countries. Policy choices that allow for greater labor mobility would help create a more balanced distribution of the working-age population between countries and relieve the economic pressures that would drive commercial AI to displace jobs. If we do none of that, distorted incentives will continue to shape this powerful technology, leading to profound negative consequences not only for lower-income countries but for everyone. 

Katya Klinova is the head of data and AI at UN Global Pulse, the secretary-general’s innovation lab. The views represented here are her own.