Your Digital Avatar May One Day Get Sick Before You Do
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.
A new injection is helping stave off RSV this season
In November 2021, Mickayla Wininger’s then one-month-old son, Malcolm, endured a terrifying bout with RSV, the respiratory syncytial (sin-SISH-uhl) virus—a common ailment that affects all age groups. Most people recover from mild, cold-like symptoms in a week or two, but RSV can be life-threatening in others, particularly infants.
Wininger, who lives in southern Illinois, was dressing Malcolm for bed when she noticed what seemed to be a minor irregularity with this breathing. She and her fiancé, Gavin McCullough, planned to take him to the hospital the next day. The matter became urgent when, in the morning, the boy’s breathing appeared to have stopped.
After they dialed 911, Malcolm started breathing again, but he ended up being hospitalized three times for RSV and defects in his heart. Eventually, he recovered fully from RSV, but “it was our worst nightmare coming to life,” Wininger recalled.
It’s a scenario that the federal government is taking steps to prevent. In July, the Food and Drug Administration approved a single-dose, long-acting injection to protect babies and toddlers. The injection, called Beyfortus, or nirsevimab, became available this October. It reduces the incidence of RSV in pre-term babies and other infants for their first RSV season. Children at highest risk for severe RSV are those who were born prematurely and have either chronic lung disease of prematurity or congenital heart disease. In those cases, RSV can progress to lower respiratory tract diseases such as pneumonia and bronchiolitis, or swelling of the lung’s small airway passages.
Each year, RSV is responsible for 2.1 million outpatient visits among children younger than five-years-old, 58,000 to 80,000 hospitalizations in this age group, and between 100 and 300 deaths, according to the Centers for Disease Control and Prevention. Transmitted through close contact with an infected person, the virus circulates on a seasonal basis in most regions of the country, typically emerging in the fall and peaking in the winter.
In August, however, the CDC issued a health advisory on a late-summer surge in severe cases of RSV among young children in Florida and Georgia. The agency predicts "increased RSV activity spreading north and west over the following two to three months.”
Infants are generally more susceptible to RSV than older people because their airways are very small, and their mechanisms to clear these passages are underdeveloped. RSV also causes mucus production and inflammation, which is more of a problem when the airway is smaller, said Jennifer Duchon, an associate professor of newborn medicine and pediatrics in the Icahn School of Medicine at Mount Sinai in New York.
In 2021 and 2022, RSV cases spiked, sending many to emergency departments. “RSV can cause serious disease in infants and some children and results in a large number of emergency department and physician office visits each year,” John Farley, director of the Office of Infectious Diseases in the FDA’s Center for Drug Evaluation and Research, said in a news release announcing the approval of the RSV drug. The decision “addresses the great need for products to help reduce the impact of RSV disease on children, families and the health care system.”
Sean O’Leary, chair of the committee on infectious diseases for the American Academy of Pediatrics, says that “we’ve never had a product like this for routine use in children, so this is very exciting news.” It is recommended for all kids under eight months old for their first RSV season. “I would encourage nirsevimab for all eligible children when it becomes available,” O’Leary said.
For those children at elevated risk of severe RSV and between the ages of 8 and 19 months, the CDC recommends one dose in their second RSV season.
The drug will be “really helpful to keep babies healthy and out of the hospital,” said O’Leary, a professor of pediatrics at the University of Colorado Anschutz Medical Campus/Children’s Hospital Colorado in Denver.
An antiviral drug called Synagis (palivizumab) has been an option to prevent serious RSV illness in high-risk infants since it was approved by the FDA in 1998. The injection must be given monthly during RSV season. However, its use is limited to “certain children considered at high risk for complications, does not help cure or treat children already suffering from serious RSV disease, and cannot prevent RSV infection,” according to the National Foundation for Infectious Diseases.
Until the approval this summer of the new monoclonal antibody, nirsevimab, there wasn’t a reliable method to prevent infection in most healthy infants.
Both nirsevimab and palivizumab are monoclonal antibodies that act against RSV. Monoclonal antibodies are lab-made proteins that mimic the immune system’s ability to fight off harmful pathogens such as viruses. A single intramuscular injection of nirsevimab preceding or during RSV season may provide protection.
The strategy with the new monoclonal antibody is “to extend protection to healthy infants who nonetheless are at risk because of their age, as well as infants with additional medical risk factors,” said Philippa Gordon, a pediatrician and infectious disease specialist in Brooklyn, New York, and medical adviser to Park Slope Parents, an online community support group.
No specific preventive measure is needed for older and healthier kids because they will develop active immunity, which is more durable. Meanwhile, older adults, who are also vulnerable to RSV, can receive one of two new vaccines. So can pregnant women, who pass on immunity to the fetus, Gordon said.
Until the approval this summer of the new monoclonal antibody, nirsevimab, there wasn’t a reliable method to prevent infection in most healthy infants, “nor is there any treatment other than giving oxygen or supportive care,” said Stanley Spinner, chief medical officer and vice president of Texas Children’s Pediatrics and Texas Children’s Urgent Care.
As with any virus, washing hands frequently and keeping infants and children away from sick people are the best defenses, Duchon said. This approach isn’t foolproof because viruses can run rampant in daycare centers, schools and parents’ workplaces, she added.
Mickayla Wininger, Malcolm’s mother, insists that family and friends wear masks, wash their hands and use hand sanitizer when they’re around her daughter and two sons. She doesn’t allow them to kiss or touch the children. Some people take it personally, but she would rather be safe than sorry.
Wininger recalls the severe anxiety caused by Malcolm's ordeal with RSV. After returning with her infant from his hospital stays, she was terrified to go to sleep. “My fiancé and I would trade shifts, so that someone was watching over our son 24 hours a day,” she said. “I was doing a night shift, so I would take caffeine pills to try and keep myself awake and would end up crashing early hours in the morning and wake up frantically thinking something happened to my son.”
Two years later, her anxiety has become more manageable, and Malcolm is doing well. “He is thriving now,” Wininger said. He recently had his second birthday and "is just the spunkiest boy you will ever meet. He looked death straight in the eyes and fought to be here today.”
Story by Big Think
For most of history, artificial intelligence (AI) has been relegated almost entirely to the realm of science fiction. Then, in late 2022, it burst into reality — seemingly out of nowhere — with the popular launch of ChatGPT, the generative AI chatbot that solves tricky problems, designs rockets, has deep conversations with users, and even aces the Bar exam.
But the truth is that before ChatGPT nabbed the public’s attention, AI was already here, and it was doing more important things than writing essays for lazy college students. Case in point: It was key to saving the lives of tens of millions of people.
AI-designed mRNA vaccines
As Dave Johnson, chief data and AI officer at Moderna, told MIT Technology Review‘s In Machines We Trust podcast in 2022, AI was integral to creating the company’s highly effective mRNA vaccine against COVID. Moderna and Pfizer/BioNTech’s mRNA vaccines collectively saved between 15 and 20 million lives, according to one estimate from 2022.
Johnson described how AI was hard at work at Moderna, well before COVID arose to infect billions. The pharmaceutical company focuses on finding mRNA therapies to fight off infectious disease, treat cancer, or thwart genetic illness, among other medical applications. Messenger RNA molecules are essentially molecular instructions for cells that tell them how to create specific proteins, which do everything from fighting infection, to catalyzing reactions, to relaying cellular messages.
Johnson and his team put AI and automated robots to work making lots of different mRNAs for scientists to experiment with. Moderna quickly went from making about 30 per month to more than one thousand. They then created AI algorithms to optimize mRNA to maximize protein production in the body — more bang for the biological buck.
For Johnson and his team’s next trick, they used AI to automate science, itself. Once Moderna’s scientists have an mRNA to experiment with, they do pre-clinical tests in the lab. They then pore over reams of data to see which mRNAs could progress to the next stage: animal trials. This process is long, repetitive, and soul-sucking — ill-suited to a creative scientist but great for a mindless AI algorithm. With scientists’ input, models were made to automate this tedious process.
“We don’t think about AI in the context of replacing humans,” says Dave Johnson, chief data and AI officer at Moderna. “We always think about it in terms of this human-machine collaboration, because they’re good at different things. Humans are really good at creativity and flexibility and insight, whereas machines are really good at precision and giving the exact same result every single time and doing it at scale and speed.”
All these AI systems were in put in place over the past decade. Then COVID showed up. So when the genome sequence of the coronavirus was made public in January 2020, Moderna was off to the races pumping out and testing mRNAs that would tell cells how to manufacture the coronavirus’s spike protein so that the body’s immune system would recognize and destroy it. Within 42 days, the company had an mRNA vaccine ready to be tested in humans. It eventually went into hundreds of millions of arms.
Biotech harnesses the power of AI
Moderna is now turning its attention to other ailments that could be solved with mRNA, and the company is continuing to lean on AI. Scientists are still coming to Johnson with automation requests, which he happily obliges.
“We don’t think about AI in the context of replacing humans,” he told the Me, Myself, and AI podcast. “We always think about it in terms of this human-machine collaboration, because they’re good at different things. Humans are really good at creativity and flexibility and insight, whereas machines are really good at precision and giving the exact same result every single time and doing it at scale and speed.”
Moderna, which was founded as a “digital biotech,” is undoubtedly the poster child of AI use in mRNA vaccines. Moderna recently signed a deal with IBM to use the company’s quantum computers as well as its proprietary generative AI, MoLFormer.
Moderna’s success is encouraging other companies to follow its example. In January, BioNTech, which partnered with Pfizer to make the other highly effective mRNA vaccine against COVID, acquired the company InstaDeep for $440 million to implement its machine learning AI across its mRNA medicine platform. And in May, Chinese technology giant Baidu announced an AI tool that designs super-optimized mRNA sequences in minutes. A nearly countless number of mRNA molecules can code for the same protein, but some are more stable and result in the production of more proteins. Baidu’s AI, called “LinearDesign,” finds these mRNAs. The company licensed the tool to French pharmaceutical company Sanofi.
Writing in the journal Accounts of Chemical Research in late 2021, Sebastian M. Castillo-Hair and Georg Seelig, computer engineers who focus on synthetic biology at the University of Washington, forecast that AI machine learning models will further accelerate the biotechnology research process, putting mRNA medicine into overdrive to the benefit of all.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.