An illustration of a robust human.
A panel of leading experts gathered this week at a sold-out event in downtown Manhattan to talk about the science and the ethics of enhancing human beings -- making people "better than well" through biomedical interventions. Here are the ten most memorable quotes from their lively discussion, which was organized by the New York Academy of Sciences, the Aspen Brain Institute, and the Hastings Center.
1) "It's okay for us to be enhanced relative to our ancestors; we are with the smallpox vaccine." —Dr. George Church, iconic genetics pioneer; professor at Harvard University and MIT
Church was more concerned with equitable access to enhancements than the morality of intervening in the first place. "We missed the last person with polio and now it's spread around the world again," he lamented.
Discussing how enhancements might become part of our species in the near-future, he mentioned the possibility of doctors slightly "overshooting" an intervention to reverse cognitive decline, for example; or younger people using such an intervention off-label. Another way might be through organ transplants, using organs that are engineered to not get cancer, or to be resistant to pain, pathogens, or senescence.
2) "All the technology we will need to fundamentally transform our species already exists. Humans are made of code, and that code is writable, readable and hackable." —Dr. Jamie Metzl, a technology futurist and geopolitical expert; Senior Fellow of the Atlantic Council, an international affairs think tank
The speed of change is on an exponential curve, and the world where we're going is changing at a much faster rate than we're used to, Metzl said. For example, a baby born 1000 years ago compared to one born today would be basically the same. But a baby born 1000 years in the future would seem like superman to us now, thanks to new capabilities that will become embedded in future people's genes over time. So how will we get from here to there?
"We will line up for small incremental benefits. By the time people are that changed, we will have adapted to a whole new set of social norms."
But, he asked, will well-meaning changes dangerously limit the diversity of our species?
3) "We are locked in a competitive arms race on both an individual and communal level, which will make it very difficult to put the brakes on. Everybody needs to be part of this conversation because it's a conversation about the future of our species." —Jamie Metzl
China, for one, plans to genetically sequence half of all newborns by 2020. In the U.S., it is standard to screen for 34 health conditions in newborns (though the exact number varies by state). There are no national guidelines for newborn genomic screening, though the National Institutes of Health is currently funding several research studies to explore the ethical concerns, potential benefits, and limitations of doing so on a large scale.
4) "I find freedom in not directing exactly how my child will be." —Josephine Johnston, Director of Research at the Hastings Center, the world's oldest bioethics research institute
Johnston cautioned against a full-throttled embrace of biomedical enhancements. Parents seeking to remake nature to serve their own purpose would be "like helicopter parenting on steroids," she said. "It could be a kind of felt obligation, something parents don't want to do but feel they must in order to compete." She warned this would be "one way to totally ruin the parenting experience altogether. I would hate to be the kind of parent who selects and controls her child's traits and talents."
Among other concerns, she worried about parents aiming to comply with social norms through technological intervention. Would a black mom, for example, feel pressure to make her child's skin paler to alleviate racial bias?
5) "We need to seriously consider the risks of a future if a handful of private companies own and monetize a map of our thoughts at any given moment." – Meredith Whittaker, Research Scientist, Open Research Lead at Google, and Co-Director of New York University's AI Now Institute, examining the social implications of artificial intelligence
The recent boom in AI research is the result of the consolidation of the tech industry's resources; only seven companies have the means to create artificial intelligence at scale, and one of the innovations on the horizon is brain-computer interfaces.
Facebook, for example, has a team of 60 engineers working on BCIs to let you type with your mind. Elon Musk's company Neuralink is working on technology that is aiming for "direct lag-free interactions between our brains and our devices."
But who will own this data? In the future, could the National Security Agency ask Neuralink, et al. for your thought log?
6) "The economic, political, and social contexts are as important as the tech itself. We need to look at power, who gets to define normal, and who falls outside of this category?" – Meredith Whittaker
Raising concerns about AI bias, Whittaker discussed how data is often coded by affluent white men from the Bay Area, potentially perpetuating discrimination against women and racial minorities.
Facial recognition, she said, is 30 percent less accurate for black women than for white men. And voice recognition systems don't hear women's voices as well as men's, among many other examples. The big question is: "Who gets to decide what's normal? And how do we ensure that different versions of normal can exist between cultures and communities? It is impossible not see the high stakes here, and how oppressive classifications of normal can marginalize people."
From left: George Church, Jamie Metzl, Josephine Johnston, Meredith Whittaker
7) "We might draw a red line at cloning or germline enhancements, but when you define those or think of specific cases, you realize you threw the baby out with the bathwater." —George Church, answering a question about whether society should agree on any red lines to prohibit certain interventions
"We should be focusing on outcomes," he suggested. "Could enhancement be a consequence of curing a disease like cognitive decline? That would concern me about drawing red lines."
8) "We have the technology to create Black Mirror. We could create a social credit score and it's terrifying." —Meredith Whittaker
In China, she said, the government is calculating scores to rank citizens based on aggregates of data like their educational history, their friend graphs, their employment and credit history, and their record of being critical of the government. These scores have already been used to bar 12 million people from travel.
"If we don't have the ability to make a choice," she said, "it could be a very frightening future."
9) "These tools will make all kinds of wonderful realities possible. Nobody looks at someone dying of cancer and says that's natural." —Jamie Metzl
Using biomedical interventions to restore health is an unequivocal moral good. But other experts questioned whether there should be a limit in how far these technologies are taken to achieve normalcy and beyond.
10) "Cancer's the easy one; what about deafness?" —Josephine Johnston, in retort
Could one person's disability be another person's desired state? "We should be so suspicious" of using technology to eradicate different ways of being in the world, she warned. Hubris has led us down the wrong path in the past, such as when homosexuality was considered a mental disorder.
"If we sometimes make mistakes about disease or dysfunction," she said, "we might make mistakes about what is a valid experience of the human condition."
AI was integral to creating Moderna's mRNA vaccine against COVID.
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.
