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.
The Next 100 Years of Scientific Progress Could Look Like This
From nanobots that kill cancer to carbon-neutral biofuels, we envisioned what the next century could bring.
In just 100 years, scientific breakthroughs could completely transform humanity and our planet for the better. Here's a glimpse at what our future may hold.
The Next 100 Years of Scientific Progress
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.
George Papanicolaou (1883-1962), Greek-born American physician developed a simple cytological test for cervical cancer in 1928.
For decades, women around the world have made the annual pilgrimage to their doctor for the dreaded but potentially life-saving Papanicolaou test, a gynecological exam to screen for cervical cancer named for Georgios Papanicolaou, the Greek immigrant who developed it.
The Pap smear, as it is commonly known, is credited for reducing cervical cancer mortality by 70% since the 1960s; the American Cancer Society (ACS) still ranks the Pap as the most successful screening test for preventing serious malignancies. Nonetheless, the agency, as well as other medical panels, including the US Preventive Services Task Force and the American College of Obstetrics and Gynecology are making a strong push to replace the Pap with the more sensitive high-risk HPV screening test for the human papillomavirus virus, which causes nearly all cases of cervical cancer.
So, how was the Pap developed and how did it become the gold standard of cervical cancer detection for more than 60 years?
Born on May 13, 1883, on the island of Euboea, Greece, Georgios Papanicolaou attended the University of Athens where he majored in music and the humanities before earning his medical degree in 1904 and PhD from the University of Munich six years later. In Europe, Papanicolaou was an assistant military surgeon during the Balkan War, a psychologist for an expedition of the Oceanographic Institute of Monaco and a caregiver for leprosy patients.
When he and his wife, Andromache Mavroyenous (Mary), arrived at Ellis Island on October 19, 1913, the young couple had scarcely more than the $250 minimum required to immigrate, spoke no English and had no job prospects. They worked a series of menial jobs--department store sales clerk, rug salesman, newspaper clerk, restaurant violinist--before Papanicolaou landed a position as an anatomy assistant at Cornell University and Mary was hired as his lab assistant, an arrangement that would last for the next 50 years.
Papanikolaou would later say the discovery "was one of the greatest thrills I ever experienced during my scientific career."
In his early research, Papanikolaou used guinea pigs to prove that gender is determined by the X and Y chromosomes. Using a pediatric nasal speculum, he collected and microscopically examined vaginal secretions of guinea pigs, which revealed distinct cell changes connected to the menstrual cycle. He moved on to study reproductive patterns in humans, beginning with his faithful wife, Mary, who not only endured his almost-daily cervical exams for decades, but also recruited friends as early research participants.
Writing in the medical journal Growth in 1920, the scientist outlined his theory that a microscopic smear of vaginal fluid could detect the presence of cancer cells in the uterus. Papanikolaou would later say the discovery "was one of the greatest thrills I ever experienced during my scientific career."
At this time, cervical cancer was the number one cancer killer of American women but physicians were skeptical of these new findings. They continued to rely on biopsy and curettage to diagnose and treat the disease until Papanicolaou's discovery was published in American Journal of Obstetrics and Gynecology. An inexpensive, easy-to-perform test that could detect cervical cancer, precancerous dysplasia and other cytological diseases was a sea change. Between 1975 and 2001, the cervical cancer rate was cut in half.
Papanicolaou became Emeritus Professor at Cornell University Medical College and received numerous awards, including the Albert Lasker Award for Clinical Medical Research and the Medal of Honor from the American Cancer Society. His image was featured on the Greek currency and the US Post Office issued a commemorative stamp in his honor. But international acclaim didn't lead to a more relaxed schedule. The researcher continued to work seven days a week and refused to take vacations.
After nearly 50 years, Papanicolaou left Cornell to head and develop the Cancer Institute of Miami. He died of a heart attack on February 19, 1962, just three months after his arrival. Mary continued to work in the renamed Papanicolaou Cancer Research Institute until her death 20 years later.
The annual pap smear was originally tied to renewing a birth control prescription. Canada began recommending Pap exams every three years in 1978. The United States followed suit in 2012, noting that it takes many years for cervical cancer to develop. In September 2020, the American Cancer Society recommended delaying the first gynecological pelvic exam until age 25 and replacing the Pap test completely with the more accurate human papillomavirus (HPV) test every five years as the technology becomes more widely available.
Not everyone agrees that it's time to do away with this proven screening method, though. The incidence rate of cervical cancer among Hispanic women is 28% higher than for white women, and Black women are more likely to die of cervical cancer than any other racial or ethnicities.
Whether the Pap is administered every year, every three years or not at all, Papanicolaou will always be known as the medical hero who saved countless women who would otherwise have succumbed to cervical cancer.