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.
How Roadside Safety Signs Backfire—and Why Policymakers Don’t Notice

Interventions in health and safety often yield results that are the opposite of what policymakers were hoping for. Officials can take a science-based approach by measuring what really works instead of relying on gut intuitions.

nudgesYou are driving along the highway and see an electronic sign that reads: “3,238 traffic deaths this year.” Do you think this reminder of roadside mortality would change how you drive? According to a recent, peer-reviewed study in Science, seeing that sign would make you more likely to crash. That’s ironic, given that the sign’s creators assumed it would make you safer.

The study, led by a pair of economists at the University of Toronto and University of Minnesota, examined seven years of traffic accident data from 880 electric highway sign locations in Texas, which experienced 4,480 fatalities in 2021. For one week of each month, the Texas Department of Transportation posts the latest fatality messages on signs along select traffic corridors as part of a safety campaign. Their logic is simple: Tell people to drive with care by reminding them of the dangers on the road.

But when the researchers looked at the data, they found that the number of crashes increased by 1.52 percent within three miles of these signs when compared with the same locations during the same month in previous years when signs did not show fatality information. That impact is similar to raising the speed limit by four miles or decreasing the number of highway troopers by 10 percent.

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Gleb Tsipursky
Dr. Gleb Tsipursky is an internationally recognized thought leader on a mission to protect leaders from dangerous judgment errors known as cognitive biases by developing the most effective decision-making strategies. A best-selling author, he wrote Resilience: Adapt and Plan for the New Abnormal of the COVID-19 Coronavirus Pandemic and Pro Truth: A Practical Plan for Putting Truth Back Into Politics. His expertise comes from over 20 years of consulting, coaching, and speaking and training as the CEO of Disaster Avoidance Experts, and over 15 years in academia as a behavioral economist and cognitive neuroscientist. He co-founded the Pro-Truth Pledge project.
Why we should put insects on the menu

Insects for sale at a market in Cambodia.

David Waltner-Toews

I walked through the Dong Makkhai forest-products market, just outside of Vientiane, the laid-back capital of the Lao Peoples Democratic Republic or Lao PDR. Piled on rough display tables were varieties of six-legged wildlife–grasshoppers, small white crickets, house crickets, mole crickets, wasps, wasp eggs and larvae, dragonflies, and dung beetles. Some were roasted or fried, but in a few cases, still alive and scrabbling at the bottom of deep plastic bowls. I crunched on some fried crickets and larvae.

One stall offered Giant Asian hornets, both babies and adults. I suppressed my inner squirm and, in the interests of world food security and equity, accepted an offer of the soft, velvety larva; they were smooth on the tongue and of a pleasantly cool, buttery-custard consistency. Because the seller had already given me a free sample, I felt obliged to buy a chunk of the nest with larvae and some dead adults, which the seller mixed with kaffir lime leaves.

The year was 2016 and I was in Lao PDR because Veterinarians without Borders/Vétérinaires sans Frontières-Canada had initiated a project on small-scale cricket farming. The intent was to organize and encourage rural women to grow crickets as a source of supplementary protein and sell them at the market for cash. As a veterinary epidemiologist, I had been trained to exterminate disease spreading insects—Lyme disease-carrying ticks, kissing bugs that carry American Sleeping Sickness and mosquitoes carrying malaria, West Nile and Zika. Now, as part of a global wave promoting insects as a sustainable food source, I was being asked to view arthropods as micro-livestock, and devise management methods to keep them alive and healthy. It was a bit of a mind-bender.

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David Waltner-Toews
David Waltner-Toews is a veterinary epidemiologist and author of more than twenty books of poetry, fiction, and science. His most recent books are On Pandemics: deadly diseases from bubonic plague to coronavirus (Greystone Books, 2020); Eat the Beetles: an exploration into our conflicted relationship with insects (ECW Press, 2017) and The Origin of Feces: what excrement tells us about evolution, ecology and a sustainable society (ECW Press, 2013).