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.
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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
Giving robots self-awareness as they move through space - and maybe even providing them with gene-like methods for storing rules of behavior - could be important steps toward creating more intelligent machines.
Now roboticists are going a step further, training their creations to do even better: understand their own image in space and interact with humans like humans do. Today, there are already robot-teachers like KeeKo, robot-pets like Moffin, robot-babysitters like iPal, and robotic companions for the elderly like Pepper.
But even these reasonably intelligent creations still have huge limitations, some scientists think. “There are niche applications for the current generations of robots,” says professor Anthony Zador at Cold Spring Harbor Laboratory—but they are not “generalists” who can do varied tasks all on their own, as they mostly lack the abilities to improvise, make decisions based on a multitude of facts or emotions, and adjust to rapidly changing circumstances. “We don’t have general purpose robots that can interact with the world. We’re ages away from that.”
Robotic spatial self-awareness – the achievement by the team at Columbia – is an important step toward creating more intelligent machines. Hod Lipson, professor of mechanical engineering who runs the Columbia lab, says that future robots will need this ability to assist humans better. Knowing how you look and where in space your parts are, decreases the need for human oversight. It also helps the robot to detect and compensate for damage and keep up with its own wear-and-tear. And it allows robots to realize when something is wrong with them or their parts. “We want our robots to learn and continue to grow their minds and bodies on their own,” Chen says. That’s what Zador wants too—and on a much grander level. “I want a robot who can drive my car, take my dog for a walk and have a conversation with me.”
Columbia scientists have trained a robot to become aware of its own "body," so it can map the right path to touch a ball without running into an obstacle, in this case a square.
Jane Nisselson and Yinuo Qin/ Columbia Engineering
Today’s technological advances are making some of these leaps of progress possible. One of them is the so-called Deep Learning—a method that trains artificial intelligence systems to learn and use information similar to how humans do it. Described as a machine learning method based on neural network architectures with multiple layers of processing units, Deep Learning has been used to successfully teach machines to recognize images, understand speech and even write text.
Trained by Google, one of these language machine learning geniuses, BERT, can finish sentences. Another one called GPT3, designed by San Francisco-based company OpenAI, can write little stories. Yet, both of them still make funny mistakes in their linguistic exercises that even a child wouldn’t. According to a paper published by Stanford’s Center for Research on Foundational Models, BERT seems to not understand the word “not.” When asked to fill in the word after “A robin is a __” it correctly answers “bird.” But try inserting the word “not” into that sentence (“A robin is not a __”) and BERT still completes it the same way. Similarly, in one of its stories, GPT3 wrote that if you mix a spoonful of grape juice into your cranberry juice and drink the concoction, you die. It seems that robots, and artificial intelligence systems in general, are still missing some rudimentary facts of life that humans and animals grasp naturally and effortlessly.
How does one give robots a genome? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create.
It's not exactly the robots’ fault. Compared to humans, and all other organisms that have been around for thousands or millions of years, robots are very new. They are missing out on eons of evolutionary data-building. Animals and humans are born with the ability to do certain things because they are pre-wired in them. Flies know how to fly, fish knows how to swim, cats know how to meow, and babies know how to cry. Yet, flies don’t really learn to fly, fish doesn’t learn to swim, cats don’t learn to meow, and babies don’t learn to cry—they are born able to execute such behaviors because they’re preprogrammed to do so. All that happens thanks to the millions of years of evolutions wired into their respective genomes, which give rise to the brain’s neural networks responsible for these behaviors. Robots are the newbies, missing out on that trove of information, Zador argues.
A neuroscience professor who studies how brain circuitry generates various behaviors, Zador has a different approach to developing the robotic mind. Until their creators figure out a way to imbue the bots with that information, robots will remain quite limited in their abilities. Each model will only be able to do certain things it was programmed to do, but it will never go above and beyond its original code. So Zador argues that we have to start giving robots a genome.
How does one do that? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create. Genomes lay out rules for brain development. Specifically, the genome encodes blueprints for wiring up our nervous system—the details of which neurons are connected, the strength of those connections and other specs that will later hold the information learned throughout life. “Our genomes serve as blueprints for building our nervous system and these blueprints give rise to a human brain, which contains about 100 billion neurons,” Zador says.
If you think what a genome is, he explains, it is essentially a very compact and compressed form of information storage. Conceptually, genomes are similar to CliffsNotes and other study guides. When students read these short summaries, they know about what happened in a book, without actually reading that book. And that’s how we should be designing the next generation of robots if we ever want them to act like humans, Zador says. “We should give them a set of behavioral CliffsNotes, which they can then unwrap into brain-like structures.” Robots that have such brain-like structures will acquire a set of basic rules to generate basic behaviors and use them to learn more complex ones.
Currently Zador is in the process of developing algorithms that function like simple rules that generate such behaviors. “My algorithms would write these CliffsNotes, outlining how to solve a particular problem,” he explains. “And then, the neural networks will use these CliffsNotes to figure out which ones are useful and use them in their behaviors.” That’s how all living beings operate. They use the pre-programmed info from their genetics to adapt to their changing environments and learn what’s necessary to survive and thrive in these settings.
For example, a robot’s neural network could draw from CliffsNotes with “genetic” instructions for how to be aware of its own body or learn to adjust its movements. And other, different sets of CliffsNotes may imbue it with the basics of physical safety or the fundamentals of speech.
At the moment, Zador is working on algorithms that are trying to mimic neuronal blueprints for very simple organisms—such as earthworms, which have only 302 neurons and about 7000 synapses compared to the millions we have. That’s how evolution worked, too—expanding the brains from simple creatures to more complex to the Homo Sapiens. But if it took millions of years to arrive at modern humans, how long would it take scientists to forge a robot with human intelligence? That’s a billion-dollar question. Yet, Zador is optimistic. “My hypotheses is that if you can build simple organisms that can interact with the world, then the higher level functions will not be nearly as challenging as they currently are.”
