Your Digital Avatar May One Day Get Sick Before You Do

Your Digital Avatar May One Day Get Sick Before You Do

Artificial neurons in a concept of artificial intelligence.

(© ktsdesign/Fotolia)



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.

Noah Davis
Noah Davis is a writer living in Brooklyn. Visit his website at http://www.noahedavis.com.
Staying well in the 21st century is like playing a game of chess

The control of infectious diseases was considered to be one of the “10 Great Public Health Achievements.” What we didn’t take into account was the very concept of evolution: as we built better protections, our enemies eventually boosted their attacking prowess, so soon enough we found ourselves on the defensive once again.

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This article originally appeared in One Health/One Planet, a single-issue magazine that explores how climate change and other environmental shifts are increasing vulnerabilities to infectious diseases by land and by sea. The magazine probes how scientists are making progress with leaders in other fields toward solutions that embrace diverse perspectives and the interconnectedness of all lifeforms and the planet.

On July 30, 1999, the Centers for Disease Control and Prevention published a report comparing data on the control of infectious disease from the beginning of the 20th century to the end. The data showed that deaths from infectious diseases declined markedly. In the early 1900s, pneumonia, tuberculosis and diarrheal diseases were the three leading killers, accounting for one-third of total deaths in the U.S.—with 40 percent being children under five.

Mass vaccinations, the discovery of antibiotics and overall sanitation and hygiene measures eventually eradicated smallpox, beat down polio, cured cholera, nearly rid the world of tuberculosis and extended the U.S. life expectancy by 25 years. By 1997, there was a shift in population health in the U.S. such that cancer, diabetes and heart disease were now the leading causes of death.

The control of infectious diseases is considered to be one of the “10 Great Public Health Achievements.” Yet on the brink of the 21st century, new trouble was already brewing. Hospitals were seeing periodic cases of antibiotic-resistant infections. Novel viruses, or those that previously didn’t afflict humans, began to emerge, causing outbreaks of West Nile, SARS, MERS or swine flu.

In the years that followed, tuberculosis made a comeback, at least in certain parts of the world. What we didn’t take into account was the very concept of evolution: as we built better protections, our enemies eventually boosted their attacking prowess, so soon enough we found ourselves on the defensive once again.

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Lina Zeldovich

Lina Zeldovich has written about science, medicine and technology for Popular Science, Smithsonian, National Geographic, Scientific American, Reader’s Digest, the New York Times and other major national and international publications. A Columbia J-School alumna, she has won several awards for her stories, including the ASJA Crisis Coverage Award for Covid reporting, and has been a contributing editor at Nautilus Magazine. In 2021, Zeldovich released her first book, The Other Dark Matter, published by the University of Chicago Press, about the science and business of turning waste into wealth and health. You can find her on http://linazeldovich.com/ and @linazeldovich.

Alzheimer’s prevention may be less about new drugs, more about income, zip code and education

(Left to right) Vickie Naylor, Bernadine Clay, and Donna Maxey read a memory prompt as they take part in the Sharing History through Active Reminiscence and Photo-Imagery (SHARP) study, September 20, 2017.

OHSU/Kristyna Wentz-Graff

That your risk of Alzheimer’s disease depends on your salary, what you ate as a child, or the block where you live may seem implausible. But researchers are discovering that social determinants of health (SDOH) play an outsized role in Alzheimer’s disease and related dementias, possibly more than age, and new strategies are emerging for how to address these factors.

At the 2022 Alzheimer’s Association International Conference, a series of presentations offered evidence that a string of socioeconomic factors—such as employment status, social support networks, education and home ownership—significantly affected dementia risk, even when adjusting data for genetic risk. What’s more, memory declined more rapidly in people who earned lower wages and slower in people who had parents of higher socioeconomic status.

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Eve Glicksman
Eve Glicksman is a freelance writer and editor in Silver Spring, MD. She writes for multiple media outlets and associations on health care, trends, culture, psychology, lifestyle, and travel. To see her work in the Washington Post, WebMD, and U.S. News & World Report, visit eveglicksman.com.