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.
In November 2020, messenger RNA catapulted into the public consciousness when the first COVID-19 vaccines were authorized for emergency use. Around the same time, an equally groundbreaking yet relatively unheralded application of mRNA technology was taking place at a London hospital.
Over the past two decades, there's been increasing interest in harnessing mRNA — molecules present in all of our cells that act like digital tape recorders, copying instructions from DNA in the cell nucleus and carrying them to the protein-making structures — to create a whole new class of therapeutics.
Scientists realized that artificial mRNA, designed in the lab, could be used to instruct our cells to produce certain antibodies, turning our bodies into vaccine-making factories, or to recognize and attack tumors. More recently, researchers recognized that mRNA could also be used to make another groundbreaking technology far more accessible to more patients: gene editing. The gene-editing tool CRISPR has generated plenty of hype for its potential to cure inherited diseases. But delivering CRISPR to the body is complicated and costly.
"Most gene editing involves taking cells out of the patient, treating them and then giving them back, which is an extremely expensive process," explains Drew Weissman, professor of medicine at the University of Pennsylvania, who was involved in developing the mRNA technology behind the COVID-19 vaccines.
But last November, a Massachusetts-based biotech company called Intellia Therapeutics showed it was possible to use mRNA to make the CRISPR system inside the body, eliminating the need to extract cells out of the body and edit them in a lab. Just as mRNA can instruct our cells to produce antibodies against a viral infection, it can also teach them to produce the two molecular components that make up CRISPR — a guide molecule and a cutting protein — to snip out a problem gene.
"The pandemic has really shown that not only are mRNA approaches viable, they could in certain circumstances be vastly superior to more traditional technologies."
In Intellia's London-based clinical trial, the company applied this for the first time in a patient with a rare inherited liver disease known as hereditary transthyretin amyloidosis with polyneuropathy. The disease causes a toxic protein to build up in a person's organs and is typically fatal. In a company press release, Intellia's president and CEO John Leonard swiftly declared that its mRNA-based CRISPR therapy could usher in a "new era of potential genome editing cures."
Weissman predicts that turning CRISPR into an affordable therapy will become the next major frontier for mRNA over the coming decade. His lab is currently working on an mRNA-based CRISPR treatment for sickle cell disease. More than 300,000 babies are born with sickle cell every year, mainly in lower income nations.
"There is a FDA-approved cure, but it involves taking the bone marrow out of the person, and then giving it back which is prohibitively expensive," he says. It also requires a patient to have a matched bone marrow done. "We give an intravenous injection of mRNA lipid nanoparticles that target CRISPR to the bone marrow stem cells in the patient, which is easy, and much less expensive."
Meanwhile, the overwhelming success of the COVID-19 vaccines has focused attention on other ways of using mRNA to bolster the immune system against threats ranging from other infectious diseases to cancer.
The practicality of mRNA vaccines – relatively small quantities are required to induce an antibody response – coupled with their adaptable design, mean companies like Moderna are now targeting pathogens like Zika, chikungunya and cytomegalovirus, or CMV, which previously considered commercially unviable for vaccine developers. This is because outbreaks have been relatively sporadic, and these viruses mainly affect people in low-income nations who can't afford to pay premium prices for a vaccine. But mRNA technology means that jabs could be produced on a flexible basis, when required, at relatively low cost.
Other scientists suggest that mRNA could even provide a means of developing a universal influenza vaccine, a goal that's long been the Holy Grail for vaccinologists around the world.
"The mRNA technology allows you to pick out bits of the virus that you want to induce immunity to," says Michael Mulqueen, vice president of business development at eTheRNA, a Belgium-based biotech that's developing mRNA-based vaccines for malaria and HIV, as well as various forms of cancer. "This means you can get the immune system primed to the bits of the virus that don't vary so much between strains. So you could actually have a single vaccine that protects against a whole raft of different variants of the same virus, offering more universal coverage."
Before mRNA became synonymous with vaccines, its biggest potential was for cancer treatments. BioNTech, the German biotech company that collaborated with Pfizer to develop the first authorized COVID-19 vaccine, was initially founded to utilize mRNA for personalized cancer treatments, and the company remains interested in cancers ranging from melanoma to breast cancer.
One of the major hurdles in treating cancer has been the fact that tumors can look very different from one person to the next. It's why conventional approaches, such as chemotherapy or radiation, don't work for every patient. But weaponizing mRNA against cancer primes the immune cells with the tumor's specific genetic sequence, training the patient's body to attack their own unique type of cancer.
"It means you're able to think about personalizing cancer treatments down to specific subgroups of patients," says Mulqueen. "For example, eTheRNA are developing a renal cell carcinoma treatment which will be targeted at around 20% of these patients, who have specific tumor types. We're hoping to take that to human trials next year, but the challenge is trying to identify the right patients for the treatment at an early stage."
Repairing Damaged mRNA
While hopes are high that mRNA could usher in new cancer treatments and make CRISPR more accessible, a growing number of companies are also exploring an alternative to gene editing, known as RNA editing.
In genetic disorders, the mRNA in certain cells is impaired due to a rogue gene defect, and so the body ceases to produce a particular vital protein. Instead of permanently deleting the problem gene with CRISPR, the idea behind RNA editing is to inject small pieces of synthetic mRNA to repair the existing mRNA. Scientists think this approach will allow normal protein production to resume.
Over the past few years, this approach has gathered momentum, as some researchers have recognized that it holds certain key advantages over CRISPR. Companies from Belgium to Japan are now looking at RNA editing to treat all kinds of disorders, from Huntingdon's disease, to amyotrophic lateral sclerosis, or ALS, and certain types of cancer.
"With RNA editing, you don't need to make any changes to the DNA," explains Daniel de Boer, CEO of Dutch biotech ProQR, which is looking to treat rare genetic disorders that cause blindness. "Changes to the DNA are permanent, so if something goes wrong, that may not be desirable. With RNA editing, it's a temporary change, so we dose patients with our drugs once or twice a year."
Last month, ProQR reported a landmark case study, in which a patient with a rare form of blindness called Leber congenital amaurosis, which affects the retina at the back of the eye, recovered vision after three months of treatment.
"We have seen that this RNA therapy restores vision in people that were completely blind for a year or so," says de Boer. "They were able to see again, to read again. We think there are a large number of other genetic diseases we could go after with this technology. There are thousands of different mutations that can lead to blindness, and we think this technology can target approximately 25% of them."
Ultimately, there's likely to be a role for both RNA editing and CRISPR, depending on the disease. "I think CRISPR is ideally suited for illnesses where you would like to permanently correct a genetic defect," says Joshua Rosenthal of the Marine Biology Laboratory in Chicago. "Whereas RNA editing could be used to treat things like pain, where you might want to reset a neural circuit temporarily over a shorter period of time."
Much of this research has been accelerated by the COVID-19 pandemic, which has played a major role in bringing mRNA to the forefront of people's minds as a therapeutic.
"The pandemic has really shown that not only are mRNA approaches viable, they could in certain circumstances be vastly superior to more traditional technologies," says Mulqueen. "In the future, I would not be surprised if many of the top pharma products are mRNA derived."
"Making Sense of Science" is a monthly podcast that features interviews with leading medical and scientific experts about the latest developments and the big ethical and societal questions they raise. This episode is hosted by science and biotech journalist Emily Mullin, summer editor of the award-winning science outlet Leaps.org.