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.
Six Questions about the Kids' COVID Vaccine, Answered by an Infectious Disease Doctor

The author, an infectious disease physician, pictured with his two daughters who are getting vaccinated against COVID-19.

Courtesy of Chin-Hong

I enthusiastically support the vaccination against COVID for children aged 5-11 years old. As an infectious disease doctor who took care of hundreds of COVID-19 patients over the past 20 months, I have seen the immediate and long-term consequences of COVID-19 on patients – and on their families. As a father of two daughters, I have lived through the fear and anxiety of protecting my kids at all cost from the scourges of the pandemic and worried constantly about bringing the virus home from work.

It is imperative that we vaccinate as many children in the community as possible. There are several reasons why. First children do get sick from COVID-19. Over the course of the pandemic in the U.S, more than 2 million children aged 5-11 have become infected, more than 8000 have been hospitalized, and more than 100 have died, making COVID one of the top 10 causes of pediatric deaths in this age group over the past year. Children are also susceptible to chronic consequences of COVID such as long COVID and multisystem inflammatory syndrome in children (MIS-C). Most studies demonstrate that 10-30% of children will develop chronic symptoms following COVID-19. These include complaints of brain fog, fatigue, trouble breathing, fever, headache, muscle and joint pains, abdominal pain, mood swings and even psychiatric disorders. Symptoms typically last from 4-8 weeks in children, with some reporting symptoms that persist for many months.

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Peter Chin-Hong
Dr. Peter Chin-Hong is Associate Dean for Regional Campuses and professor of medicine at UCSF School of Medicine. He is a medical educator who specializes in treating infectious diseases, particularly infections that develop in patients who have suppressed immune systems, such as solid organ and hematopoietic stem cell transplant recipients and HIV+ organ transplant recipients. He directs the immunocompromised host infectious diseases program at UCSF. His research focuses on donor derived infections in transplant recipients and molecular diagnostics of infectious diseases in patients with suppressed immune systems. He earned his undergraduate and medical degrees from Brown University, before completing an internal medicine residency and infectious diseases fellowship at UCSF, where he is Professor of Medicine and Director of the Yearlong Inquiry Program in the School of Medicine. He was the inaugural holder of the Academy of Medical Educators Endowed Chair for Innovation in Teaching.
Food Poisoning Sickens Millions a Year. Now, a Surprising Weapon Is Helping Protect Against Contamination.

Phages, which are harmless viruses that destroy specific bacteria, are becoming useful tools to protect our food supply.

Every year, one in seven people in America comes down with a foodborne illness, typically caused by a bacterial pathogen, including E.Coli, listeria, salmonella, or campylobacter. That adds up to 48 million people, of which 120,000 are hospitalized and 3000 die, according to the Centers for Disease Control. And the variety of foods that can be contaminated with bacterial pathogens is growing too. In the 20th century, E.Coli and listeria lurked primarily within meat. Now they find their way into lettuce, spinach, and other leafy greens, causing periodic consumer scares and product recalls. Onions are the most recent suspected culprit of a nationwide salmonella outbreak.

Some of these incidents are almost inevitable because of how Mother Nature works, explains Divya Jaroni, associate professor of animal and food sciences at Oklahoma State University. These common foodborne pathogens come from the cattle's intestines when the animals shed them in their manure—and then they get washed into rivers and lakes, especially in heavy rains. When this water is later used to irrigate produce farms, the bugs end up on salad greens. Plus, many small farms do both—herd cattle and grow produce.

"Unfortunately for us, these pathogens are part of the microflora of the cows' intestinal tract," Jaroni says. "Some farmers may have an acre or two of cattle pastures, and an acre of a produce farm nearby, so it's easy for this water to contaminate the crops."

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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.