Technology is Redefining the Age of 'Older Mothers'
In October 2021, a woman from Gujarat, India, stunned the world when it was revealed she had her first child through in vitro fertilization (IVF) at age 70. She had actually been preceded by a compatriot of hers who, two years before, gave birth to twins at the age of 73, again with the help of IVF treatment. The oldest known mother to conceive naturally lived in the UK; in 1997, Dawn Brooke conceived a son at age 59.
These women may seem extreme outliers, almost freaks of nature; in the US, for example, the average age of first-time mothers is 26. A few decades from now, though, the sight of 70-year-old first-time mothers may not even raise eyebrows, say futurists.
“We could absolutely have more 70-year-old mothers because we are learning how to regulate the aging process better,” says Andrew Hessel, a microbiologist and geneticist, who cowrote "The Genesis Machine," a book about “rewriting life in the age of synthetic biology,” with Amy Webb, the futurist who recently wondered why 70-year-old women shouldn’t give birth.
Technically, we're already doing this, says Hessel, pointing to a technique known as in vitro gametogenesis (IVG). IVG refers to turning adult cells into sperm or egg cells. “You can think of it as the upgrade to IVF,” Hessel says. These vanguard stem cell research technologies can take even skin cells and turn them into induced pluripotent stem cells (iPSCs), which are basically master cells capable of maturing into any human cell, be it kidney cells, liver cells, brain cells or gametes, aka eggs and sperm, says Henry T. “Hank” Greely, a Stanford law professor who specializes in ethical, legal, and social issues in biosciences.
Mothers over 70 will be a minor blip, statistically speaking, Greely predicts.
In 2016, Greely wrote "The End of Sex," a book in which he described the science of making gametes out of iPSCs in detail. Greely says science will indeed enable us to see 70-year-old new mums fraternize with mothers several decades younger at kindergartens in the (not far) future. And it won’t be that big of a deal.
“An awful lot of children all around the world have been raised by grandmothers for millennia. To have 70-year-olds and 30-year-olds mingling in maternal roles is not new,” he says. That said, he doubts that many women will want to have a baby in the eighth decade of their life, even if science allows it. “Having a baby and raising a child is hard work. Even if 1% of all mothers are over 65, they aren’t going to change the world,” Greely says. Mothers over 70 will be a minor blip, statistically speaking, he predicts. But one thing is certain: the technology is here.
And more technologies for the same purpose could be on the way. In March 2021, researchers from Monash University in Melbourne, Australia, published research in Nature, where they successfully reprogrammed skin cells into a three-dimensional cellular structure that was morphologically and molecularly similar to a human embryo–the iBlastoid. In compliance with Australian law and international guidelines referencing the “primitive streak rule," which bans the use of embryos older than 14 days in scientific research, Monash scientists stopped growing their iBlastoids in vitro on day 11.
“The research was both cutting-edge and controversial, because it essentially created a new human life, not for the purpose of a patient who's wanting to conceive, but for basic research,” says Lindsay Wu, a senior lecturer in the School of Medical Sciences at the University of New South Wales (UNSW), in Kensington, Australia. If you really want to make sure what you are breeding is an embryo, you need to let it develop into a viable baby. “This is the real proof in the pudding,'' says Wu, who runs UNSW’s Laboratory for Ageing Research. Then you get to a stage where you decide for ethical purposes you have to abort it. “Fiddling here a bit too much?” he asks. Wu believes there are other approaches to tackling declining fertility due to older age that are less morally troubling.
He is actually working on them. Why would it be that women, who are at peak physical health in almost every other regard, in their mid- to late- thirties, have problems conceiving, asked Wu and his team in a research paper published in 2020 in Cell Reports. The simple answer is the egg cell. An average girl in puberty has between 300,000 and 400,000 eggs, while at around age 37, the same woman has only 25,000 eggs left. Things only go downhill from there. So, what torments the egg cells?
The UNSW team found that the levels of key molecules called NAD+ precursors, which are essential to the metabolism and genome stability of egg cells, decline with age. The team proceeded to add these vitamin-like substances back into the drinking water of reproductively aged, infertile lab mice, which then had babies.
“It's an important proof of concept,” says Wu. He is investigating how safe it is to replicate the experiment with humans in two ongoing studies. The ultimate goal is to restore the quality of egg cells that are left in patients in their late 30s and early- to mid-40s, says Wu. He sees the goal of getting pregnant for this age group as less ethically troubling, compared to 70-year-olds.
But what is ethical, anyway? “It is a tricky word,” says Hessel. He differentiates between ethics, which represent a personal position and may, thus, be more transient, and morality, longer lasting principles embraced across society such as, “Thou shalt not kill.” Unprecedented advances often bring out fear and antagonism until time passes and they just become…ordinary. When IVF pioneer Landrum Shettles tried to perform IVF in 1973, the chairman of Columbia’s College of Physicians and Surgeons interdicted the procedure at the last moment. Almost all countries in the world have IVF clinics today, and the global IVF services market is clearly a growth industry.
Besides, you don’t have a baby at 70 by accident: you really want it, Greely and Hessel agree. And by that age, mothers may be wiser and more financially secure, Hessel says (though he is quick to add that even the pregnancy of his own wife, who had her child at 40, was a high-risk one).
As a research question, figuring out whether older mothers are better than younger ones and vice-versa entails too many confounding variables, says Greely. And why should we focus on who’s the better mother anyway? “We've had 70-year-old and 80-year-old fathers forever–why should people have that much trouble getting used to mothers doing the same?” Greely wonders. For some women having a child at an old(er) age would be comforting; maybe that’s what matters.
And the technology to enable older women to have children is already here or coming very soon. That, perhaps, matters even more. Researchers have already created mice–and their offspring–entirely from scratch in the lab. “Doing this to produce human eggs is similar," says Hessel. "It is harder to collect tissues, and the inducing cocktails are different, but steady advances are being made." He predicts that the demand for fertility treatments will keep financing research and development in the area. He says that big leaps will be made if ethical concerns don’t block them: it is not far-fetched to believe that the first baby produced from lab-grown eggs will be born within the next decade.
In an op-ed in 2020 with Stat, Greely argued that we’ve already overcome the technical barrier for human cloning, but no one's really talking about it. Likewise, scientists are also working on enabling 70-year-old women to have babies, says Hessel, but most commentators are keeping really quiet about it. At least so far.
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.