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.
The U.S. must fund more biotech innovation – or other countries will catch up faster than you think

In the coming years, U.S. market share in biotech will decline unless the federal government makes investments to improve the quality and quantity of U.S. research, writes the author.

Photo by Eric Prouzet on Unsplash

The U.S. has approximately 58 percent of the market share in the biotech sector, followed by China with 11 percent. However, this market share is the result of several years of previous research and development (R&D) – it is a present picture of what happened in the past. In the future, this market share will decline unless the federal government makes investments to improve the quality and quantity of U.S. research in biotech.

The effectiveness of current R&D can be evaluated in a variety of ways such as monies invested and the number of patents filed. According to the UNESCO Institute for Statistics, the U.S. spends approximately 2.7 percent of GDP on R&D ($476,459.0M), whereas China spends 2 percent ($346,266.3M). However, investment levels do not necessarily translate into goods that end up contributing to innovation.

Patents are a better indication of innovation. The biotech industry relies on patents to protect their investments, making patenting a key tool in the process of translating scientific discoveries that can ultimately benefit patients. In 2020, China filed 1,497,159 patents, a 6.9 percent increase in growth rate. In contrast, the U.S. filed 597,172, a 3.9 percent decline. When it comes to patents filed, China has approximately 45 percent of the world share compared to 18 percent for the U.S.

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Juan Pablo De Rivero Vaccari
Dr. de Rivero Vaccari is an Associate Professor of Neurosurgery at the University of Miami Miller School of Medicine and a Public Voices Fellow of The OpEd Project. His research focuses on understanding the molecular mechanisms of inflammation in the central nervous system after injury and disease with the goal of identifying therapeutic targets that can be used for the development of anti-inflammatory therapies.
New gene therapy helps patients with rare disease. One mother wouldn't have it any other way.

A biotech in Cambridge, Mass., is targeting a rare disease called cystinosis with gene therapy. It's been effective for five patients in a clinical trial that's still underway.

Cherqui Lab

Three years ago, Jordan Janz of Consort, Alberta, knew his gene therapy treatment for cystinosis was working when his hair started to darken. Pigmentation or melanin production is just one part of the body damaged by cystinosis.

“When you have cystinosis, you’re either a redhead or a blonde, and you are very pale,” attests Janz, 23, who was diagnosed with the disease just eight months after he was born. “After I got my new stem cells, my hair came back dark, dirty blonde, then it lightened a little bit, but before it was white blonde, almost bleach blonde.”

According to Cystinosis United, about 500 to 600 people have the rare genetic disease in the U.S.; an estimated 20 new cases are diagnosed each year.

Located in Cambridge, Mass., AVROBIO is a gene therapy company that targets cystinosis and other lysosomal storage disorders, in which toxic materials build up in the cells. Janz is one of five patients in AVROBIO’s ongoing Phase 1/2 clinical trial of a gene therapy for cystinosis called AVR-RD-04.

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Christopher Johnston
Christopher Johnston has published more than 3,500 articles in publications including American Theatre, Christian Science Monitor, History Magazine, and Scientific American. His book, Shattering Silences: Strategies to Prevent Sexual Assault, Heal Survivors, and Bring Assailants to Justice (Skyhorse) was published in May 2018. He is a member of the Board of the American Society of Journalists and Authors.