Donna Shirley pictured at her home in Tulsa, with a model of the Sojourner rover she was in charge of that explored Mars.
"Periodically, I'll hear somebody say they got into the space program because of me, and that makes me feel really good," Shirley told Leaps.org. "I look at the mission control area, and there are a lot of women in there. I'm quite pleased I was able to break the glass ceiling."
Her $25-million, 25-pound microrover – powered by solar energy and designed to get rock samples and test soil chemistry for evidence of life – was named after Sojourner Truth, a 19th-century Black abolitionist and women's rights activist. Unlike Mars Pathfinder, Shirley didn't have to travel more than 131 million miles to reach her goal, but her path to scientific fame as a woman sometimes resembled an asteroid field.
The Sojourner Rover in 1997 on Mars.NASA/JPLAs a high-IQ tomboy growing up in Wynnewood, Oklahoma (pop. 2,300), Shirley yearned to escape. She decided to become an engineer at age 10 and took flying lessons at 15. Her extraterrestrial aspirations were fueled by Ray Bradbury's The Martian Chronicles and Arthur C. Clarke's The Sands of Mars. Yet when she entered the University of Oklahoma (OU) in 1958, her freshman academic advisor initially told her: "Girls can't be engineers." She ignored him.
Years later, Shirley would combat such archaic thinking, succeeding at JPL with her creative, collaborative management style. "If you look at the literature, you'll find that teams that are either led by or heavily involved with women do better than strictly male teams," she noted.
However, her career trajectory stalled at OU. Burned out by her course load and distracted by a broken engagement to marry a fellow student, she switched her major to professional writing. After graduation, she applied her aeronautical background as a McDonnell Aircraft technical writer, but her boss, she says, harassed her and she faced gender-based hostility from male co-workers.
Returning to OU, Shirley finished off her engineering degree and became a JPL aerodynamist in 1966 after answering an ad in the St. Louis Post-Dispatch. At first, she was the only female engineer among the research center's 2,000-odd engineers. She wore many hats, from designing planetary atmospheric entry vehicles to picking the launch date of November 4, 1973 for Mariner 10's mission to Venus and Mercury.
By the mid-1980's, she was managing teams that focused on robotics and Mars, delivering creative solutions when NASA budget cuts loomed. In 1989, the same year the Sojourner microrover concept was born, President George H.W. Bush announced his Space Exploration Initiative, including plans for a human mission to Mars by 2019.
That target, of course, wasn't attained, despite huge advances in technology and our understanding of the Martian environment. Today, Shirley believes humans could land on Mars by 2030. She became the founding director of the Science Fiction Museum and Hall of Fame in Seattle in 2004 after leaving NASA, and to this day, she enjoys checking out pop culture portrayals of Mars landings – even if they're not always accurate.
After the novel The Martian was published in 2011, which later was adapted into the hit film starring Matt Damon, Shirley phoned author Andy Weir: "You've got a major mistake in here. It says there's a storm that tries to blow the rocket over. But actually, the Mars atmosphere is so thin, it would never blow a rocket over!"
Fearlessly speaking her mind and seeking the stars helped Donna Shirley make history. However, a 2019 Washington Post story noted: "Women make up only about a third of NASA's workforce. They comprise just 28 percent of senior executive leadership positions and are only 16 percent of senior scientific employees." Whether it's traveling to Mars or trending toward gender equality, we've still got a long way to go.
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.
