Your Digital Avatar May One Day Get Sick Before You Do
Artificial neurons in a concept of artificial intelligence.
Artificial intelligence is everywhere, just not in the way you think it is.
These networks, loosely designed after the human brain, are interconnected computers that have the ability to "learn."
"There's the perception of AI in the glossy magazines," says Anders Kofod-Petersen, a professor of Artificial Intelligence at the Norwegian University of Science and Technology. "That's the sci-fi version. It resembles the small guy in the movie AI. It might be benevolent or it might be evil, but it's generally intelligent and conscious."
"And this is, of course, as far from the truth as you can possibly get."
What Exactly Is Artificial Intelligence, Anyway?
Let's start with how you got to this piece. You likely came to it through social media. Your Facebook account, Twitter feed, or perhaps a Google search. AI influences all of those things, machine learning helping to run the algorithms that decide what you see, when, and where. AI isn't the little humanoid figure; it's the system that controls the figure.
"AI is being confused with robotics," Eleonore Pauwels, Director of the Anticipatory Intelligence Lab with the Science and Technology Innovation Program at the Wilson Center, says. "What AI is right now is a data optimization system, a very powerful data optimization system."
The revolution in recent years hasn't come from the method scientists and other researchers use. The general ideas and philosophies have been around since the late 1960s. Instead, the big change has been the dramatic increase in computing power, primarily due to the development of neural networks. These networks, loosely designed after the human brain, are interconnected computers that have the ability to "learn." An AI, for example, can be taught to spot a picture of a cat by looking at hundreds of thousands of pictures that have been labeled "cat" and "learning" what a cat looks like. Or an AI can beat a human at Go, an achievement that just five years ago Kofod-Petersen thought wouldn't be accomplished for decades.
"It's very difficult to argue that something is intelligent if it can't learn, and these algorithms are getting pretty good at learning stuff. What they are not good at is learning how to learn."
Medicine is the field where this expertise in perception tasks might have the most influence. It's already having an impact as iPhones use AI to detect cancer, Apple watches alert the wearer to a heart problem, AI spots tuberculosis and the spread of breast cancer with a higher accuracy than human doctors, and more. Every few months, another study demonstrates more possibility. (The New Yorker published an article about medicine and AI last year, so you know it's a serious topic.)
But this is only the beginning. "I personally think genomics and precision medicine is where AI is going to be the biggest game-changer," Pauwels says. "It's going to completely change how we think about health, our genomes, and how we think about our relationship between our genotype and phenotype."
The Fundamental Breakthrough That Must Be Solved
To get there, however, researchers will need to make another breakthrough, and there's debate about how long that will take. Kofod-Petersen explains: "If we want to move from this narrow intelligence to this broader intelligence, that's a very difficult problem. It basically boils down to that we haven't got a clue about what intelligence actually is. We don't know what intelligence means in a biological sense. We think we might recognize it but we're not completely sure. There isn't a working definition. We kind of agree with the biologists that learning is an aspect of it. It's very difficult to argue that something is intelligent if it can't learn, and these algorithms are getting pretty good at learning stuff. What they are not good at is learning how to learn. They can learn specific tasks but we haven't approached how to teach them to learn to learn."
In other words, current AI is very, very good at identifying that a picture of a cat is, in fact, a cat – and getting better at doing so at an incredibly rapid pace – but the system only knows what a "cat" is because that's what a programmer told it a furry thing with whiskers and two pointy ears is called. If the programmer instead decided to label the training images as "dogs," the AI wouldn't say "no, that's a cat." Instead, it would simply call a furry thing with whiskers and two pointy ears a dog. AI systems lack the explicit inference that humans do effortlessly, almost without thinking.
Pauwels believes that the next step is for AI to transition from supervised to unsupervised learning. The latter means that the AI isn't answering questions that a programmer asks it ("Is this a cat?"). Instead, it's almost like it's looking at the data it has, coming up with its own questions and hypothesis, and answering them or putting them to the test. Combining this ability with the frankly insane processing power of the computer system could result in game-changing discoveries.
In the not-too-distant future, a doctor could run diagnostics on a digital avatar, watching which medical conditions present themselves before the person gets sick in real life.
One company in China plans to develop a way to create a digital avatar of an individual person, then simulate that person's health and medical information into the future. In the not-too-distant future, a doctor could run diagnostics on a digital avatar, watching which medical conditions presented themselves – cancer or a heart condition or anything, really – and help the real-life version prevent those conditions from beginning or treating them before they became a life-threatening issue.
That, obviously, would be an incredibly powerful technology, and it's just one of the many possibilities that unsupervised AI presents. It's also terrifying in the potential for misuse. Even the term "unsupervised AI" brings to mind a dystopian landscape where AI takes over and enslaves humanity. (Pick your favorite movie. There are dozens.) This is a concern, something for developers, programmers, and scientists to consider as they build the systems of the future.
The Ethical Problem That Deserves More Attention
But the more immediate concern about AI is much more mundane. We think of AI as an unbiased system. That's incorrect. Algorithms, after all, are designed by someone or a team, and those people have explicit or implicit biases. Intentionally, or more likely not, they introduce these biases into the very code that forms the basis for the AI. Current systems have a bias against people of color. Facebook tried to rectify the situation and failed. These are two small examples of a larger, potentially systemic problem.
It's vital and necessary for the people developing AI today to be aware of these issues. And, yes, avoid sending us to the brink of a James Cameron movie. But AI is too powerful a tool to ignore. Today, it's identifying cats and on the verge of detecting cancer. In not too many tomorrows, it will be on the forefront of medical innovation. If we are careful, aware, and smart, it will help simulate results, create designer drugs, and revolutionize individualize medicine. "AI is the only way to get there," Pauwels says.
Podcast: A Nasal Spray COVID Booster Shot, With Dr. Akiko Iwasaki
Dr. Akiko Iwasaki, an immunologist at Yale, is working on creating a nasal spray booster after the primary mRNA shots.
The "Making Sense of Science" podcast features interviews with leading medical and scientific experts about the latest developments and the big ethical and societal questions they raise. This monthly podcast is hosted by journalist Kira Peikoff, founding editor of the award-winning science outlet Leaps.org.
Real-world data shows that protection against Covid-19 infection wanes a few months after two or three shots of mRNA vaccines (while protection against severe disease remains high). But what if there was another kind of booster that could shore up the immune response in your nose, the "door" to your body? Like bouncers at a club, a better prepared nasal defense system could stop the virus in its tracks -- mitigating illnesses as well as community spread. Dr. Akiko Iwasaki, an immunologist at Yale, is working on such a booster, with fantastic results recently reported in mice. In this episode, she shares the details of this important work.
Listen to episode
Kira Peikoff was the editor-in-chief of Leaps.org from 2017 to 2021. As a journalist, her work has appeared in The New York Times, Newsweek, Nautilus, Popular Mechanics, The New York Academy of Sciences, and other outlets. She is also the author of four suspense novels that explore controversial issues arising from scientific innovation: Living Proof, No Time to Die, Die Again Tomorrow, and Mother Knows Best. Peikoff holds a B.A. in Journalism from New York University and an M.S. in Bioethics from Columbia University. She lives in New Jersey with her husband and two young sons. Follow her on Twitter @KiraPeikoff.
Technology is Redefining the Age of 'Older Mothers'
Scientists are working on technologies that would enable more 70-year-old women to have babies.
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.