In today's podcast episode, law professor Gaia Bernstein talks about the challenges of keeping control over our thoughts and actions, even when some powerful forces are pushing in the other direction.
Each afternoon, kids walk through my neighborhood, on their way back home from school, and almost all of them are walking alone, staring down at their phones. It's a troubling site. This daily parade of the zombie children just can’t bode well for the future.
That’s one reason I felt like Gaia Bernstein’s new book was talking directly to me. A law professor at Seton Hall, Gaia makes a strong argument that people are so addicted to tech at this point, we need some big, system level changes to social media platforms and other addictive technologies, instead of just blaming the individual and expecting them to fix these issues.
Gaia’s book is called Unwired: Gaining Control Over Addictive Technologies. It’s fascinating and I had a chance to talk with her about it for today’s podcast. At its heart, our conversation is really about how and whether we can maintain control over our thoughts and actions, even when some powerful forces are pushing in the other direction.
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We discuss the idea that, in certain situations, maybe it's not reasonable to expect that we’ll be able to enjoy personal freedom and autonomy. We also talk about how to be a good parent when it sometimes seems like our kids prefer to be raised by their iPads; so-called educational video games that actually don’t have anything to do with education; the root causes of tech addictions for people of all ages; and what kinds of changes we should be supporting.
Gaia is Seton’s Hall’s Technology, Privacy and Policy Professor of Law, as well as Co-Director of the Institute for Privacy Protection, and Co-Director of the Gibbons Institute of Law Science and Technology. She’s the founding director of the Institute for Privacy Protection. She created and spearheaded the Institute’s nationally recognized Outreach Program, which educated parents and students about technology overuse and privacy.
Professor Bernstein's scholarship has been published in leading law reviews including the law reviews of Vanderbilt, Boston College, Boston University, and U.C. Davis. Her work has been selected to the Stanford-Yale Junior Faculty Forum and received extensive media coverage. Gaia joined Seton Hall's faculty in 2004. Before that, she was a fellow at the Engelberg Center of Innovation Law & Policy and at the Information Law Institute of the New York University School of Law. She holds a J.S.D. from the New York University School of Law, an LL.M. from Harvard Law School, and a J.D. from Boston University.
Gaia’s work on this topic is groundbreaking I hope you’ll listen to the conversation and then consider pre-ordering her new book. It comes out on March 28.
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.
