The inside of a tokamak—the doughnut-shaped vessel designed to contain a nuclear fusion reaction—presents a special kind of chaos. Hydrogen atoms are smashed together at unfathomably high temperatures, creating a whirling, roiling plasma that’s hotter than the surface of the sun. Finding smart ways to control and confine that plasma will be key to unlocking the potential of nuclear fusion, which has been mooted as the clean energy source of the future for decades. At this point, the science underlying fusion seems sound, so what remains is an engineering challenge. “We need to be able to heat this matter up and hold it together for long enough for us to take energy out of it,” says Ambrogio Fasoli, director of the Swiss Plasma Center at École Polytechnique Fédérale de Lausanne in Switzerland.
That’s where DeepMind comes in. The artificial intelligence firm, backed by Google parent company Alphabet, has previously turned its hand to video games and protein folding, and has been working on a joint research project with the Swiss Plasma Center to develop an AI for controlling a nuclear fusion reaction.
In stars, which are also powered by fusion, the sheer gravitational mass is enough to pull hydrogen atoms together and overcome their opposing charges. On Earth, scientists instead use powerful magnetic coils to confine the nuclear fusion reaction, nudging it into the desired position and shaping it like a potter manipulating clay on a wheel. The coils have to be carefully controlled to prevent the plasma from touching the sides of the vessel: this can damage the walls and slow down the fusion reaction. (There’s little risk of an explosion as the fusion reaction cannot survive without magnetic confinement).
But every time researchers want to change the configuration of the plasma and try out different shapes that may yield more power or a cleaner plasma, it necessitates a huge amount of engineering and design work. Conventional systems are computer-controlled and based on models and careful simulations, but they are, Fasoli says, “complex and not always necessarily optimized.”
DeepMind has developed an AI that can control the plasma autonomously. A paper published in the journal Nature describes how researchers from the two groups taught a deep reinforcement learning system to control the 19 magnetic coils inside TCV, the variable-configuration tokamak at the Swiss Plasma Center, which is used to carry out research that will inform the design of bigger fusion reactors in the future. “AI, and specifically reinforcement learning, is particularly well suited to the complex problems presented by controlling plasma in a tokamak,” says Martin Riedmiller, control team lead at DeepMind.
The neural network—a type of AI setup designed to mimic the architecture of the human brain—was initially trained in a simulation. It started by observing how changing the settings on each of the 19 coils affected the shape of the plasma inside the vessel. Then it was given different shapes to try to re-create in the plasma. These included a D-shaped cross section close to what will be used inside ITER (formerly the International Thermonuclear Experimental Reactor), the large-scale experimental tokamak under construction in France, and a snowflake configuration that could help dissipate the intense heat of the reaction more evenly around the vessel.
DeepMind’s neural network was able to manipulate the plasma inside a fusion reactor into a number of different shapes that fusion researchers have been exploring.Illustration: DeepMind & SPC/EPFL
DeepMind’s AI was able to autonomously figure out how to create these shapes by manipulating the magnetic coils in the right way—both in the simulation and when the scientists ran the same experiments for real inside the TCV tokamak to validate the simulation. It represents a “significant step,” says Fasoli, one that could influence the design of future tokamaks or even speed up the path to viable fusion reactors. “It’s a very positive result,” says Yasmin Andrew, a fusion specialist at Imperial College London, who was not involved in the research. “It will be interesting to see if they can transfer the technology to a larger tokamak.”
Fusion offered a particular challenge to DeepMind’s scientists because the process is both complex and continuous. Unlike a turn-based game like Go, which the company has famously conquered with its AlphaGo AI, the state of a plasma constantly changes. And to make things even harder, it can’t be continuously measured. It is what AI researchers call an “under–observed system.”
“Sometimes algorithms which are good at these discrete problems struggle with such continuous problems,” says Jonas Buchli, a research scientist at DeepMind. “This was a really big step forward for our algorithm, because we could show that this is doable. And we think this is definitely a very, very complex problem to be solved. It is a different kind of complexity than what you have in games.”
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In 2018, while the Argentine Congress was hotly debating whether to decriminalize abortion, the Ministry of Early Childhood in the northern province of Salta and the American tech giant Microsoft presented an algorithmic system to predict teenage pregnancy. They called it the Technology Platform for Social Intervention.
“With technology you can foresee five or six years in advance, with first name, last name, and address, which girl—future teenager—is 86 percent predestined to have an adolescent pregnancy,” Juan Manuel Urtubey, then the governor of the province, proudly declared on national television. The stated goal was to use the algorithm to predict which girls from low-income areas would become pregnant in the next five years. It was never made clear what would happen once a girl or young woman was labeled as “predestined” for motherhood or how this information would help prevent adolescent pregnancy. The social theories informing the AI system, like its algorithms, were opaque.
The system was based on data—including age, ethnicity, country of origin, disability, and whether the subject’s home had hot water in the bathroom—from 200,000 residents in the city of Salta, including 12,000 women and girls between the ages of 10 and 19. Though there is no official documentation, from reviewing media articles and two technical reviews, we know that “territorial agents” visited the houses of the girls and women in question, asked survey questions, took photos, and recorded GPS locations. What did those subjected to this intimate surveillance have in common? They were poor, some were migrants from Bolivia and other countries in South America, and others were from Indigenous Wichí, Qulla, and Guaraní communities.
Although Microsoft spokespersons proudly announced that the technology in Salta was “one of the pioneering cases in the use of AI data” in state programs, it presents little that is new. Instead, it is an extension of a long Argentine tradition: controlling the population through surveillance and force. And the reaction to it shows how grassroots Argentine feminists were able to take on this misuse of artificial intelligence.
In the 19th and early 20th centuries, successive Argentine governments carried out a genocide of Indigenous communities and promoted immigration policies based on ideologies designed to attract European settlement, all in hopes of blanquismo, or “whitening” the country. Over time, a national identity was constructed along social, cultural, and most of all racial lines.
This type of eugenic thinking has a propensity to shapeshift and adapt to new scientific paradigms and political circumstances, according to historian Marisa Miranda, who tracks Argentina’s attempts to control the population through science and technology. Take the case of immigration. Throughout Argentina’s history, opinion has oscillated between celebrating immigration as a means of “improving” the population and considering immigrants to be undesirable and a political threat to be carefully watched and managed.
More recently, the Argentine military dictatorship between 1976 and 1983 controlled the population through systematic political violence. During the dictatorship, women had the “patriotic task” of populating the country, and contraception was prohibited by a 1977 law. The cruelest expression of the dictatorship’s interest in motherhood was the practice of kidnapping pregnant women considered politically subversive. Most women were murdered after giving birth and many of their children were illegally adopted by the military to be raised by “patriotic, Catholic families.”
While Salta’s AI system to “predict pregnancy” was hailed as futuristic, it can only be understood in light of this long history, particularly, in Miranda’s words, the persistent eugenic impulse that always “contains a reference to the future” and assumes that reproduction “should be managed by the powerful.”
Due to the complete lack of national AI regulation, the Technology Platform for Social Intervention was never subject to formal review and no assessment of its impacts on girls and women has been made. There has been no official data published on its accuracy or outcomes. Like most AI systems all over the world, including those used in sensitive contexts, it lacks transparency and accountability.
Though it is unclear whether the technology program was ultimately suspended, everything we know about the system comes from the efforts of feminist activists and journalists who led what amounted to a grassroots audit of a flawed and harmful AI system. By quickly activating a well-oiled machine of community organizing, these activists brought national media attention to how an untested, unregulated technology was being used to violate the rights of girls and women.
“The idea that algorithms can predict teenage pregnancy before it happens is the perfect excuse for anti-women and anti-sexual and reproductive rights activists to declare abortion laws unnecessary,” wrote feminist scholars Paz Peña and Joana Varon at the time. Indeed, it was soon revealed that an Argentine nonprofit called the Conin Foundation, run by doctor Abel Albino, a vocal opponent of abortion rights, was behind the technology, along with Microsoft.
The character of conflict between nations has fundamentally changed. Governments and militaries now fight on our behalf in the “gray zone,” where the boundaries between peace and war are blurred. They must navigate a complex web of ambiguous and deeply interconnected challenges, ranging from political destabilization and disinformation campaigns to cyberattacks, assassinations, proxy operations, election meddling, or perhaps even human-made pandemics. Add to this list the existential threat of climate change (and its geopolitical ramifications) and it is clear that the description of what now constitutes a national security issue has broadened, each crisis straining or degrading the fabric of national resilience.
Traditional analysis tools are poorly equipped to predict and respond to these blurred and intertwined threats. Instead, in 2022 governments and militaries will use sophisticated and credible real-life simulations, putting software at the heart of their decision-making and operating processes. The UK Ministry of Defence, for example, is developing what it calls a military Digital Backbone. This will incorporate cloud computing, modern networks, and a new transformative capability called a Single Synthetic Environment, or SSE.
This SSE will combine artificial intelligence, machine learning, computational modeling, and modern distributed systems with trusted data sets from multiple sources to support detailed, credible simulations of the real world. This data will be owned by critical institutions, but will also be sourced via an ecosystem of trusted partners, such as the Alan Turing Institute.
An SSE offers a multilayered simulation of a city, region, or country, including high-quality mapping and information about critical national infrastructure, such as power, water, transport networks, and telecommunications. This can then be overlaid with other information, such as smart-city data, information about military deployment, or data gleaned from social listening. From this, models can be constructed that give a rich, detailed picture of how a region or city might react to a given event: a disaster, epidemic, or cyberattack or a combination of such events organized by state enemies.
Defense synthetics are not a new concept. However, previous solutions have been built in a standalone way that limits reuse, longevity, choice, and—crucially—the speed of insight needed to effectively counteract gray-zone threats.
National security officials will be able to use SSEs to identify threats early, understand them better, explore their response options, and analyze the likely consequences of different actions. They will even be able to use them to train, rehearse, and implement their plans. By running thousands of simulated futures, senior leaders will be able to grapple with complex questions, refining policies and complex plans in a virtual world before implementing them in the real one.
One key question that will only grow in importance in 2022 is how countries can best secure their populations and supply chains against dramatic weather events coming from climate change. SSEs will be able to help answer this by pulling together regional infrastructure, networks, roads, and population data, with meteorological models to see how and when events might unfold.
The future has a history. The good news is that it’s one from which we can learn; the bad news is that we very rarely do. That’s because the clearest lesson from the history of the future is that knowing the future isn’t necessarily very useful. But that has yet to stop humans from trying.
Take Peter Turchin’s famed prediction for 2020. In 2010 he developed a quantitative analysis of history, known as cliodynamics, that allowed him to predict that the West would experience political chaos a decade later. Unfortunately, no one was able to act on that prophecy in order to prevent damage to US democracy. And of course, if they had, Turchin’s prediction would have been relegated to the ranks of failed futures. This situation is not an aberration.
Rulers from Mesopotamia to Manhattan have sought knowledge of the future in order to obtain strategic advantages—but time and again, they have failed to interpret it correctly, or they have failed to grasp either the political motives or the speculative limitations of those who proffer it. More often than not, they have also chosen to ignore futures that force them to face uncomfortable truths. Even the technological innovations of the 21st century have failed to change these basic problems—the results of computer programs are, after all, only as accurate as their data input.
There is an assumption that the more scientific the approach to predictions, the more accurate forecasts will be. But this belief causes more problems than it solves, not least because it often either ignores or excludes the lived diversity of human experience. Despite the promise of more accurate and intelligent technology, there is little reason to think the increased deployment of AI in forecasting will make prognostication any more useful than it has been throughout human history.
People have long tried to find out more about the shape of things to come. These efforts, while aimed at the same goal, have differed across time and space in several significant ways, with the most obvious being methodology—that is, how predictions were made and interpreted. Since the earliest civilizations, the most important distinction in this practice has been between individuals who have an intrinsic gift or ability to predict the future, and systems that provide rules for calculating futures. The predictions of oracles, shamans, and prophets, for example, depended on the capacity of these individuals to access other planes of being and receive divine inspiration. Strategies of divination such as astrology, palmistry, numerology, and Tarot, however, depend on the practitioner’s mastery of a complex theoretical rule-based (and sometimes highly mathematical) system, and their ability to interpret and apply it to particular cases. Interpreting dreams or the practice of necromancy might lie somewhere between these two extremes, depending partly on innate ability, partly on acquired expertise. And there are plenty of examples, in the past and present, that involve both strategies for predicting the future. Any internet search on “dream interpretation” or “horoscope calculation” will throw up millions of hits.
In the last century, technology legitimized the latter approach, as developments in IT (predicted, at least to some extent, by Moore’s law) provided more powerful tools and systems for forecasting. In the 1940s, the analog computer MONIAC had to use actual tanks and pipes of colored water to model the UK economy. By the 1970s, the Club of Rome could turn to the World3 computer simulation to model the flow of energy through human and natural systems via key variables such as industrialization, environmental loss, and population growth. Its report, Limits to Growth, became a best seller, despite the sustained criticism it received for the assumptions at the core of the model and the quality of the data that was fed into it.
At the same time, rather than depending on technological advances, other forecasters have turned to the strategy of crowdsourcing predictions of the future. Polling public and private opinions, for example, depends on something very simple—asking people what they intend to do or what they think will happen. It then requires careful interpretation, whether based in quantitative (like polls of voter intention) or qualitative (like the Rand corporation’s DELPHI technique) analysis. The latter strategy harnesses the wisdom of highly specific crowds. Assembling a panel of experts to discuss a given topic, the thinking goes, is likely to be more accurate than individual prognostication.
For decades, doctors and hospitals saw kidney patients differently based on their race. A standard equation for estimating kidney function applied a correction for Black patients that made their health appear rosier, inhibiting access to transplants and other treatments.
On Thursday, a task force assembled by two leading kidney care societies said the practice is unfair and should end.
The group, a collaboration between the National Kidney Foundation and the American Society of Nephrology, recommended use of a new formula that does not factor in a patient’s race. In a statement, Paul Palevsky, the foundation’s president, urged “all laboratories and health care systems nationwide to adopt this new approach as rapidly as possible.” That call is significant because recommendations and guidelines from professional medical societies play a powerful role in shaping how specialists care for patients.
A study published in 2020 that reviewed records for 57,000 people in Massachusetts found that one-third of Black patients would have had their disease classified as more severe if they had been assessed using the same version of the formula as white patients. The traditional kidney calculation was an example of a class of medical algorithms and calculators that have recently come under fire for conditioning patient care based on race, which is a social category not biological one.
A review published last year listed more than a dozen such tools, in areas such as cardiology and cancer care. It helped prompt a surge of activism against the practice from diverse groups, including medical students and lawmakers such as Senator Elizabeth Warren (D-Massachusetts) and the chair of the House Ways and Means Committee, Richard Neal (D-Massachusetts).
Recently there are signs the tide is turning. The University of Washington dropped the use of race in kidney calculations last year after student protests led to a reconsideration of the practice. Mass General Brigham and Vanderbilt hospitals also abandoned the practice in 2020.
In May, a tool used to predict the chance a woman who previously had a cesarean section could safely give birth via vaginal delivery was updated to no longer automatically assign lower scores to Black and Hispanic women. A calculator that estimates the chances a child has a urinary tract infection was updated to no longer slash the scores for patients who are Black.
The prior formula for assessing kidney disease, known as CKD-EPI, was introduced in 2009, updating a 1999 formula that used race in a similar way. It converts the level of a waste product called creatinine in a person’s blood into a measure of overall kidney function called estimated glomerular filtration rate, or eGFR. Doctors use eGFR to help classify the severity of a person’s illness and determine if they qualify for various treatments, including transplants. Healthy kidneys produce higher scores.
The equation’s design factored in a person’s age and sex but also boosted the score of any patient classified as Black by 15.9 percent. That feature was included to account for statistical patterns seen in the patient data used to inform the design of CKD-EPI, which included relatively few people who were Black or from other racial minorities. But it meant a person’s perceived race could shift how their disease was measured or treated. A person with both Black and white heritage, for example, could flip a health system’s classification of their illness depending on how their doctor saw them or how they identified.
Nwamaka Eneanya, an assistant professor at University of Pennsylvania and a member of the task force behind Thursday’s recommendation, says she knows of one biracial patient with severe kidney disease who after learning about how the equation worked requested that she be classified as white to increase her chances of being listed for advanced care. Eneanya says a shift away from the established equation is long overdue. “Using someone’s skin color to guide their clinical pathway is wholeheartedly wrong—you introduce racial bias into medical care when you do that,” she says.
