In 1938, Harvard began an in-depth study of the secrets to happiness. It's still going, and in today's podcast episode, the study's director, Bob Waldinger, tells Leaps.org about the keys to a satisfying life, based on 85 years of research.
What makes for a good life? Such a simple question, yet we don't have great answers. Most of us try to figure it out as we go along, and many end up feeling like they never got to the bottom of it.
Shouldn't something so important be approached with more scientific rigor? In 1938, Harvard researchers began a study to fill this gap. Since then, they’ve followed hundreds of people over the course of their lives, hoping to identify which factors are key to long-term satisfaction.
Eighty-five years later, the Harvard Study of Adult Development is still going. And today, its directors, the psychiatrists Bob Waldinger and Marc Shulz, have published a book that pulls together the study’s most important findings. It’s called The Good Life: Lessons from the World’s Longest Scientific Study of Happiness.
In this podcast episode, I talked with Dr. Waldinger about life lessons that we can mine from the Harvard study and his new book.
Listen on Apple | Listen on Spotify | Listen on Stitcher | Listen on Amazon | Listen on Google
More background on the study
Back in the 1930s, the research began with 724 people. Some were first-year Harvard students paying full tuition, others were freshmen who needed financial help, and the rest were 14-year-old boys from inner city Boston – white males only. Fortunately, the study team realized the error of their ways and expanded their sample to include the wives and daughters of the first participants. And Waldinger’s book focuses on the Harvard study findings that can be corroborated by evidence from additional research on the lives of people of different races and other minorities.
The study now includes over 1,300 relatives of the original participants, spanning three generations. Every two years, the participants have sent the researchers a filled-out questionnaire, reporting how their lives are going. At five-year intervals, the research team takes a peek their health records and, every 15 years, the psychologists meet their subjects in-person to check out their appearance and behavior.
But they don’t stop there. No, the researchers factor in multiple blood samples, DNA, images from body scans, and even the donated brains of 25 participants.
Robert Waldinger, director of the Harvard Study of Adult Development.
Katherine Taylor
Dr. Waldinger is Clinical Professor of Psychiatry at Harvard Medical School, in addition to being Director of the Harvard Study of Adult Development. He got his M.D. from Harvard Medical School and has published numerous scientific papers he’s a practicing psychiatrist and psychoanalyst, he teaches Harvard medical students, and since that is clearly not enough to keep him busy, he’s also a Zen priest.
His book is a must-read if you’re looking for scientific evidence on how to design your life for more satisfaction so someday in the future you can look back on it without regret, and this episode was an amazing conversation in which Dr. Waldinger breaks down many of the cliches about the good life, making his advice real and tangible. We also get into what he calls “side-by-side” relationships, personality traits for the good life, and the downsides of being too strict about work-life balance.
Show links
- Bob Waldinger
- Waldinger's book, The Good Life: Lessons from the World's Longest Scientific Study of Happiness
- The Harvard Study of Adult Development
- Waldinger's Ted Talk
- Gallup report finding that people with good friends at work have higher engagement with their jobs
- The link between relationships and well-being
- Those with social connections live longer
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.
