How thousands of first- and second-graders saved the world from a deadly disease
Exactly 67 years ago, in 1955, a group of scientists and reporters gathered at the University of Michigan and waited with bated breath for Dr. Thomas Francis Jr., director of the school’s Poliomyelitis Vaccine Evaluation Center, to approach the podium. The group had gathered to hear the news that seemingly everyone in the country had been anticipating for the past two years – whether the vaccine for poliomyelitis, developed by Francis’s former student Jonas Salk, was effective in preventing the disease.
Polio, at that point, had become a household name. As the highly contagious virus swept through the United States, cities closed their schools, movie theaters, swimming pools, and even churches to stop the spread. For most, polio presented as a mild illness, and was usually completely asymptomatic – but for an unlucky few, the virus took hold of the central nervous system and caused permanent paralysis of muscles in the legs, arms, and even people’s diaphragms, rendering the person unable to walk and breathe. It wasn’t uncommon to hear reports of people – mostly children – who fell sick with a flu-like virus and then, just days later, were relegated to spend the rest of their lives in an iron lung.
For two years, researchers had been testing a vaccine that would hopefully be able to stop the spread of the virus and prevent the 45,000 infections each year that were keeping the nation in a chokehold. At the podium, Francis greeted the crowd and then proceeded to change the course of human history: The vaccine, he reported, was “safe, effective, and potent.” Widespread vaccination could begin in just a few weeks. The nightmare was over.
The road to success
Jonas Salk, a medical researcher and virologist who developed the vaccine with his own research team, would rightfully go down in history as the man who eradicated polio. (Today, wild poliovirus circulates in just two countries, Afghanistan and Pakistan – with only 140 cases reported in 2020.) But many people today forget that the widespread vaccination campaign that effectively ended wild polio across the globe would have never been possible without the human clinical trials that preceded it.
As with the COVID-19 vaccine, skepticism and misinformation around the polio vaccine abounded. But even more pervasive than the skepticism was fear. The consequences of polio had arguably never been more visible.
The road to human clinical trials – and the resulting vaccine – was a long one. In 1938, President Franklin Delano Roosevelt launched the National Foundation for Infantile Paralysis in order to raise funding for research and development of a polio vaccine. (Today, we know this organization as the March of Dimes.) A polio survivor himself, Roosevelt elevated awareness and prevention into the national spotlight, even more so than it had been previously. Raising funds for a safe and effective polio vaccine became a cornerstone of his presidency – and the funds raked in by his foundation went primarily to Salk to fund his research.
The Trials Begin
Salk’s vaccine, which included an inactivated (killed) polio virus, was promising – but now the researchers needed test subjects to make global vaccination a possibility. Because the aim of the vaccine was to prevent paralytic polio, researchers decided that they had to test the vaccine in the population that was most vulnerable to paralysis – young children. And, because the rate of paralysis was so low even among children, the team required many children to collect enough data. Francis, who led the trial to evaluate Salk’s vaccine, began the process of recruiting more than one million school-aged children between the ages of six and nine in 272 counties that had the highest incidence of the disease. The participants were nicknamed the “Polio Pioneers.”
Double-blind, placebo-based trials were considered the “gold standard” of epidemiological research back in Francis's day - and they remain the best approach we have today. These rigorous scientific studies are designed with two participant groups in mind. One group, called the test group, receives the experimental treatment (such as a vaccine); the other group, called the control, receives an inactive treatment known as a placebo. The researchers then compare the effects of the active treatment against the effects of the placebo, and every researcher is “blinded” as to which participants receive what treatment. That way, the results aren’t tainted by any possible biases.
But the study was controversial in that only some of the individual field trials at the county and state levels had a placebo group. Researchers described this as a “calculated risk,” meaning that while there were risks involved in giving the vaccine to a large number of children, the bigger risk was the potential paralysis or death that could come with being infected by polio. In all, just 200,000 children across the US received a placebo treatment, while an additional 725,000 children acted as observational controls – in other words, researchers monitored them for signs of infection, but did not give them any treatment.
As with the COVID-19 vaccine, skepticism and misinformation around the polio vaccine abounded. But even more pervasive than the skepticism was fear. President Roosevelt, who had made many public and televised appearances in a wheelchair, served as a perpetual reminder of the consequences of polio, as an infection at age 39 had rendered him permanently unable to walk. The consequences of polio had arguably never been more visible, and parents signed up their children in droves to participate in the study and offer them protection.
The Polio Pioneer Legacy
In a little less than a year, roughly half a million children received a dose of Salk’s polio vaccine. While plenty of children were hesitant to get the shot, many former participants still remember the fear surrounding the disease. One former participant, a Polio Pioneer named Debbie LaCrosse, writes of her experience: “There was no discussion, no listing of pros and cons. No amount of concern over possible side effects or other unknowns associated with a new vaccine could compare to the terrifying threat of polio.” For their participation, each kid received a certificate – and sometimes a pin – with the words “Polio Pioneer” emblazoned across the front.
When Francis announced the results of the trial on April 12, 1955, people did more than just breathe a sigh of relief – they openly celebrated, ringing church bells and flooding into the streets to embrace. Salk, who had become the face of the vaccine at that point, was instantly hailed as a national hero – and teachers around the country had their students to write him ‘thank you’ notes for his years of diligent work.
But while Salk went on to win national acclaim – even accepting the Presidential Medal of Freedom for his work on the polio vaccine in 1977 – his success was due in no small part to the children (and their parents) who took a risk in order to advance medical science. And that risk paid off: By the early 1960s, the yearly cases of polio in the United States had gone down to just 910. Where before the vaccine polio had caused around 15,000 cases of paralysis each year, only ten cases of paralysis were recorded in the entire country throughout the 1970s. And in 1979, the virus that once shuttered entire towns was declared officially eradicated in this country. Thanks to the efforts of these brave pioneers, the nation – along with the majority of the world – remains free of polio even today.
Researchers probe extreme gene therapy for severe alcoholism
Story by Freethink
A single shot — a gene therapy injected into the brain — dramatically reduced alcohol consumption in monkeys that previously drank heavily. If the therapy is safe and effective in people, it might one day be a permanent treatment for alcoholism for people with no other options.
The challenge: Alcohol use disorder (AUD) means a person has trouble controlling their alcohol consumption, even when it is negatively affecting their life, job, or health.
In the U.S., more than 10 percent of people over the age of 12 are estimated to have AUD, and while medications, counseling, or sheer willpower can help some stop drinking, staying sober can be a huge struggle — an estimated 40-60 percent of people relapse at least once.
A team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
According to the CDC, more than 140,000 Americans are dying each year from alcohol-related causes, and the rate of deaths has been rising for years, especially during the pandemic.
The idea: For occasional drinkers, alcohol causes the brain to release more dopamine, a chemical that makes you feel good. Chronic alcohol use, however, causes the brain to produce, and process, less dopamine, and this persistent dopamine deficit has been linked to alcohol relapse.
There is currently no way to reverse the changes in the brain brought about by AUD, but a team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
To find out, they tested it in heavy-drinking monkeys — and the animals’ alcohol consumption dropped by 90% over the course of a year.
How it works: The treatment centers on the protein GDNF (“glial cell line-derived neurotrophic factor”), which supports the survival of certain neurons, including ones linked to dopamine.
For the new study, a harmless virus was used to deliver the gene that codes for GDNF into the brains of four monkeys that, when they had the option, drank heavily — the amount of ethanol-infused water they consumed would be equivalent to a person having nine drinks per day.
“We targeted the cell bodies that produce dopamine with this gene to increase dopamine synthesis, thereby replenishing or restoring what chronic drinking has taken away,” said co-lead researcher Kathleen Grant.
To serve as controls, another four heavy-drinking monkeys underwent the same procedure, but with a saline solution delivered instead of the gene therapy.
The results: All of the monkeys had their access to alcohol removed for two months following the surgery. When it was then reintroduced for four weeks, the heavy drinkers consumed 50 percent less compared to the control group.
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
The researchers then took the alcohol away for another four weeks, before giving it back for four. They repeated this cycle for a year, and by the end of it, the treated monkeys’ consumption had fallen by more than 90 percent compared to the controls.
“Drinking went down to almost zero,” said Grant. “For months on end, these animals would choose to drink water and just avoid drinking alcohol altogether. They decreased their drinking to the point that it was so low we didn’t record a blood-alcohol level.”
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
Looking ahead: Dopamine is involved in a lot more than addiction, so more research is needed to not only see if the results translate to people but whether the gene therapy leads to any unwanted changes to mood or behavior.
Because the therapy requires invasive brain surgery and is likely irreversible, it’s unlikely to ever become a common treatment for alcoholism — but it could one day be the only thing standing between people with severe AUD and death.
“[The treatment] would be most appropriate for people who have already shown that all our normal therapeutic approaches do not work for them,” said Grant. “They are likely to create severe harm or kill themselves or others due to their drinking.”
This article originally appeared on Freethink, home of the brightest minds and biggest ideas of all time.
Massive benefits of AI come with environmental and human costs. Can AI itself be part of the solution?
The recent explosion of generative artificial intelligence tools like ChatGPT and Dall-E enabled anyone with internet access to harness AI’s power for enhanced productivity, creativity, and problem-solving. With their ever-improving capabilities and expanding user base, these tools proved useful across disciplines, from the creative to the scientific.
But beneath the technological wonders of human-like conversation and creative expression lies a dirty secret—an alarming environmental and human cost. AI has an immense carbon footprint. Systems like ChatGPT take months to train in high-powered data centers, which demand huge amounts of electricity, much of which is still generated with fossil fuels, as well as water for cooling. “One of the reasons why Open AI needs investments [to the tune of] $10 billion from Microsoft is because they need to pay for all of that computation,” says Kentaro Toyama, a computer scientist at the University of Michigan. There’s also an ecological toll from mining rare minerals required for hardware and infrastructure. This environmental exploitation pollutes land, triggers natural disasters and causes large-scale human displacement. Finally, for data labeling needed to train and correct AI algorithms, the Big Data industry employs cheap and exploitative labor, often from the Global South.
Generative AI tools are based on large language models (LLMs), with most well-known being various versions of GPT. LLMs can perform natural language processing, including translating, summarizing and answering questions. They use artificial neural networks, called deep learning or machine learning. Inspired by the human brain, neural networks are made of millions of artificial neurons. “The basic principles of neural networks were known even in the 1950s and 1960s,” Toyama says, “but it’s only now, with the tremendous amount of compute power that we have, as well as huge amounts of data, that it’s become possible to train generative AI models.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries.
In recent months, much attention has gone to the transformative benefits of these technologies. But it’s important to consider that these remarkable advances may come at a price.
AI’s carbon footprint
In their latest annual report, 2023 Landscape: Confronting Tech Power, the AI Now Institute, an independent policy research entity focusing on the concentration of power in the tech industry, says: “The constant push for scale in artificial intelligence has led Big Tech firms to develop hugely energy-intensive computational models that optimize for ‘accuracy’—through increasingly large datasets and computationally intensive model training—over more efficient and sustainable alternatives.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries. In 2019, Emma Strubell, then a graduate researcher at the University of Massachusetts Amherst, estimated that training a single LLM resulted in over 280,000 kg in CO2 emissions—an equivalent of driving almost 1.2 million km in a gas-powered car. A couple of years later, David Patterson, a computer scientist from the University of California Berkeley, and colleagues, estimated GPT-3’s carbon footprint at over 550,000 kg of CO2 In 2022, the tech company Hugging Face, estimated the carbon footprint of its own language model, BLOOM, as 25,000 kg in CO2 emissions. (BLOOM’s footprint is lower because Hugging Face uses renewable energy, but it doubled when other life-cycle processes like hardware manufacturing and use were added.)
Luckily, despite the growing size and numbers of data centers, their increasing energy demands and emissions have not kept pace proportionately—thanks to renewable energy sources and energy-efficient hardware.
But emissions don’t tell the full story.
AI’s hidden human cost
“If historical colonialism annexed territories, their resources, and the bodies that worked on them, data colonialism’s power grab is both simpler and deeper: the capture and control of human life itself through appropriating the data that can be extracted from it for profit.” So write Nick Couldry and Ulises Mejias, authors of the book The Costs of Connection.
The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
Technologies we use daily inexorably gather our data. “Human experience, potentially every layer and aspect of it, is becoming the target of profitable extraction,” Couldry and Meijas say. This feeds data capitalism, the economic model built on the extraction and commodification of data. While we are being dispossessed of our data, Big Tech commodifies it for their own benefit. This results in consolidation of power structures that reinforce existing race, gender, class and other inequalities.
“The political economy around tech and tech companies, and the development in advances in AI contribute to massive displacement and pollution, and significantly changes the built environment,” says technologist and activist Yeshi Milner, who founded Data For Black Lives (D4BL) to create measurable change in Black people’s lives using data. The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
AI’s recent explosive growth spiked the demand for manual, behind-the-scenes tasks, creating an industry described by Mary Gray and Siddharth Suri as “ghost work” in their book. This invisible human workforce that lies behind the “magic” of AI, is overworked and underpaid, and very often based in the Global South. For example, workers in Kenya who made less than $2 an hour, were the behind the mechanism that trained ChatGPT to properly talk about violence, hate speech and sexual abuse. And, according to an article in Analytics India Magazine, in some cases these workers may not have been paid at all, a case for wage theft. An exposé by the Washington Post describes “digital sweatshops” in the Philippines, where thousands of workers experience low wages, delays in payment, and wage theft by Remotasks, a platform owned by Scale AI, a $7 billion dollar American startup. Rights groups and labor researchers have flagged Scale AI as one company that flouts basic labor standards for workers abroad.
It is possible to draw a parallel with chattel slavery—the most significant economic event that continues to shape the modern world—to see the business structures that allow for the massive exploitation of people, Milner says. Back then, people got chocolate, sugar, cotton; today, they get generative AI tools. “What’s invisible through distance—because [tech companies] also control what we see—is the massive exploitation,” Milner says.
“At Data for Black Lives, we are less concerned with whether AI will become human…[W]e’re more concerned with the growing power of AI to decide who’s human and who’s not,” Milner says. As a decision-making force, AI becomes a “justifying factor for policies, practices, rules that not just reinforce, but are currently turning the clock back generations years on people’s civil and human rights.”
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement.
Nuria Oliver, a computer scientist, and co-founder and vice-president of the European Laboratory of Learning and Intelligent Systems (ELLIS), says that instead of focusing on the hypothetical existential risks of today’s AI, we should talk about its real, tangible risks.
“Because AI is a transverse discipline that you can apply to any field [from education, journalism, medicine, to transportation and energy], it has a transformative power…and an exponential impact,” she says.
AI's accountability
“At the core of what we were arguing about data capitalism [is] a call to action to abolish Big Data,” says Milner. “Not to abolish data itself, but the power structures that concentrate [its] power in the hands of very few actors.”
A comprehensive AI Act currently negotiated in the European Parliament aims to rein Big Tech in. It plans to introduce a rating of AI tools based on the harms caused to humans, while being as technology-neutral as possible. That sets standards for safe, transparent, traceable, non-discriminatory, and environmentally friendly AI systems, overseen by people, not automation. The regulations also ask for transparency in the content used to train generative AIs, particularly with copyrighted data, and also disclosing that the content is AI-generated. “This European regulation is setting the example for other regions and countries in the world,” Oliver says. But, she adds, such transparencies are hard to achieve.
Google, for example, recently updated its privacy policy to say that anything on the public internet will be used as training data. “Obviously, technology companies have to respond to their economic interests, so their decisions are not necessarily going to be the best for society and for the environment,” Oliver says. “And that’s why we need strong research institutions and civil society institutions to push for actions.” ELLIS also advocates for data centers to be built in locations where the energy can be produced sustainably.
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement. “The only way to make sense of this data is using machine learning methods,” Oliver says.
Milner believes that the best way to expose AI-caused systemic inequalities is through people's stories. “In these last five years, so much of our work [at D4BL] has been creating new datasets, new data tools, bringing the data to life. To show the harms but also to continue to reclaim it as a tool for social change and for political change.” This change, she adds, will depend on whose hands it is in.