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.
Podcast: The Friday Five Weekly Roundup in Health Research
The Friday Five covers five stories in research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on scientific creativity and progress to give you a therapeutic dose of inspiration headed into the weekend.
Here are the promising studies covered in this week's Friday Five:
- A new mask can detect Covid and send an alert to your phone
- More promising research for a breakthrough drug to treat schizophrenia
- AI tool can create new proteins
- Connections between an unhealthy gut and breast cancer
- Progress on the longevity drug, rapamycin
And an honorable mention this week: Certain exercises may benefit some types of memory more than others
Life is Emerging: Review of Siddhartha Mukherjee’s Song of the Cell
The DNA double helix is often the image spiraling at the center of 21st century advances in biomedicine and the growing bioeconomy. And yet, DNA is molecularly inert. DNA, the code for genes, is not alive and is not strictly necessary for life. Ought life be at the center of our communication of living systems? Is not the Cell a superior symbol of life and our manipulation of living systems?
A code for life isn’t a code without the life that instantiates it. A code for life must be translated. The cell is the basic unit of that translation. The cell is the minimal viable package of life as we know it. Therefore, cell biology is at the center of biomedicine’s greatest transformations, suggests Pulitzer-winning physician-scientist Siddhartha Mukherjee in his latest book, The Song of the Cell: The Exploration of Medicine and the New Human.
The Song of the Cell begins with the discovery of cells and of germ theory, featuring characters such as Louis Pasteur and Robert Koch, who brought the cell “into intimate contact with pathology and medicine.” This intercourse would transform biomedicine, leading to the insight that we can treat disease by thinking at the cellular level. The slightest rearrangement of sick cells might be the path toward alleviating suffering for the organism: eroding the cell walls of a bacterium while sparing our human cells; inventing a medium that coaxes sperm and egg to dance into cellular union for in vitro fertilization (IVF); designing molecular missiles that home to the receptors decorating the exterior of cancer cells; teaching adult skin cells to remember their embryonic state for regenerative medicines.
Mukherjee uses the bulk of the book to elucidate key cell types in the human body, along with their “connective relationships” that enable key organs and organ systems to function. This includes the immune system, the heart, the brain, and so on. Mukherjee’s distinctive style features compelling anecdotes and human stories that animate the scientific (and unscientific) processes that have led to our current state of understanding. In his chapter on neurons and the brain, for example, he integrates Santiago Ramon y Cajal’s meticulous black ink sketches of neurons into Mukherjee’s own personal encounter with clinical depression. In one lucid section, he interviews Dr. Helen Mayberg, a pioneering neurologist who takes seriously the descriptive power of her patients’ metaphors, as they suffer from “caves,” “holes,” “voids,” and “force fields” that render their lives gray. Dr. Mayberg aims to stimulate patients’ neuronal cells in a manner that brings back the color.
Beyond exposing the insight and inventiveness that has arisen out of cell-based thinking, it seems that Mukherjee’s bigger project is an epistemological one. The early chapters of The Song of the Cell continually hint at the potential for redefining the basic unit of biology as the cell rather than the gene. The choice to center biomedicine around cells is, above all, a conspicuous choice not to center it around genes (the subject of Mukherjee’s previous book, The Gene), because genes dominate popular science communication.
This choice of cells over genes is most welcome. Cells are alive. Genes are not. Letters—such as the As, Cs, Gs, and Ts that represent the nucleotides of DNA, which make up our genes—must be synthesized into a word or poem or song that offers a glimpse into deeper truths. A key idea embedded in this thinking is that of emergence. Whether in ancient myth or modern art, creation tends to be an emergent process, not a linearly coded script. The cell is our current best guess for the basic unit of life’s emergence, turning a finite set of chemical building blocks—nucleic acids, proteins, sugars, fats—into a replicative, evolving system for fighting stasis and entropy. The cell’s song is one for our times, for it is the song of biology’s emergence out of chemistry and physics, into the “frenetically active process” of homeostasis.
Re-centering our view of biology has practical consequences, too, for how we think about diagnosing and treating disease, and for inventing new medicines. Centering cells presents a challenge: which type of cell to place at the center? Rather than default to the apparent simplicity of DNA as a symbol because it represents the one master code for life, the tension in defining the diversity of cells—a mapping process still far from complete in cutting-edge biology laboratories—can help to create a more thoughtful library of cellular metaphors to shape both the practice and communication of biology.
Further, effective problem solving is often about operating at the right level, or the right scale. The cell feels like appropriate level at which to interrogate many of the diseases that ail us, because the senses that guide our own perceptions of sickness and health—the smoldering pain of inflammation, the tunnel vision of a migraine, the dizziness of a fluttering heart—are emergent.
This, unfortunately, is sort of where Mukherjee leaves the reader, under-exploring the consequences of a biology of emergence. Many practical and profound questions have to do with the ways that each scale of life feeds back on the others. In a tome on Cells and “the future human” I wished that Mukherjee had created more space for seeking the ways that cells will shape and be shaped by the future, of humanity and otherwise.
We are entering a phase of real-world bioengineering that features the modularization of cellular parts within cells, of cells within organs, of organs within bodies, and of bodies within ecosystems. In this reality, we would be unwise to assume that any whole is the mere sum of its parts.
For example, when discussing the regenerative power of pluripotent stem cells, Mukherjee raises the philosophical thought experiment of the Delphic boat, also known as the Ship of Theseus. The boat is made of many pieces of wood, each of which is replaced for repairs over the years, with the boat’s structure unchanged. Eventually none of the boat’s original wood remains: Is it the same boat?
Mukherjee raises the Delphic boat in one paragraph at the end of the chapter on stem cells, as a metaphor related to the possibility of stem cell-enabled regeneration in perpetuity. He does not follow any of the threads of potential answers. Given the current state of cellular engineering, about which Mukherjee is a world expert from his work as a physician-scientist, this book could have used an entire section dedicated to probing this question and, importantly, the ways this thought experiment falls apart.
We are entering a phase of real-world bioengineering that features the modularization of cellular parts within cells, of cells within organs, of organs within bodies, and of bodies within ecosystems. In this reality, we would be unwise to assume that any whole is the mere sum of its parts. Wholeness at any one of these scales of life—organelle, cell, organ, body, ecosystem—is what is at stake if we allow biological reductionism to assume away the relation between those scales.
In other words, Mukherjee succeeds in providing a masterful and compelling narrative of the lives of many of the cells that emerge to enliven us. Like his previous books, it is a worthwhile read for anyone curious about the role of cells in disease and in health. And yet, he fails to offer the broader context of The Song of the Cell.
As leading agronomist and essayist Wes Jackson has written, “The sequence of amino acids that is at home in the human cell, when produced inside the bacterial cell, does not fold quite right. Something about the E. coli internal environment affects the tertiary structure of the protein and makes it inactive. The whole in this case, the E. coli cell, affects the part—the newly made protein. Where is the priority of part now?” [1]
Beyond the ways that different kingdoms of life translate the same genetic code, the practical situation for humanity today relates to the ways that the different disciplines of modern life use values and culture to influence our genes, cells, bodies, and environment. It may be that humans will soon become a bit like the Delphic boat, infused with the buzz of fresh cells to repopulate different niches within our bodies, for healthier, longer lives. But in biology, as in writing, a mixed metaphor can cause something of a cacophony. For we are not boats with parts to be replaced piecemeal. And nor are whales, nor alpine forests, nor topsoil. Life isn’t a sum of parts, and neither is a song that rings true.
[1] Wes Jackson, "Visions and Assumptions," in Nature as Measure (p. 52-53).