Harvard Developed AI Identifies the Shortest Path to Human Happiness

August 19, 2022
Science
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Deep Longevity has published a paper in Aging-US outlining a machine learning approach to human psychology in collaboration with Nancy Etcoff, Ph.D., Harvard Medical School, an authority on happiness and beauty.

The authors created two digital models of human psychology based on data from the Midlife in the United States study.

The first model is an ensemble of deep neural networks that predicts respondents’ chronological age and psychological well-being in 10 years using information from a psychological survey. This model depicts the trajectories of the human mind as it ages. It also demonstrates that the capacity to form meaningful connections, as well as mental autonomy and environmental mastery, develops with age. It also suggests that the emphasis on personal progress is constantly declining, but the sense of having a purpose in life only fades after 40-50 years. These results add to the growing body of knowledge on socioemotional selectivity and hedonic adaptation in the context of adult personality development.

The second model is a self-organizing map that was created to serve as the foundation for a recommendation engine for mental health applications. This unsupervised learning algorithm splits all respondents into clusters depending on their likelihood of developing depression and determines the shortest path toward a cluster of mental stability for any individual. Alex Zhavoronkov, the chief longevity officer of Deep Longevity, elaborates, “Existing mental health applications offer generic advice that applies to everyone yet fits no one. We have built a system that is scientifically sound and offers superior personalization.”

To demonstrate this system’s potential, Deep Longevity has released a web service FuturSelf, a free online application that lets users take the psychological test described in the original publication. At the end of the assessment, users receive a report with insights aimed at improving their long-term mental well-being and can enroll in a guidance program that provides them with a steady flow of AI-chosen recommendations. Data obtained on FuturSelf will be used to further develop Deep Longevity’s digital approach to mental health.

“This study offers an interesting perspective on psychological age, future well-being, and risk of depression, and demonstrates a novel application of machine learning approaches to the issues of psychological health. It also broadens how we view aging and transitions through life stages and emotional states.”

The authors plan to continue studying human psychology in the context of aging and long-term well-being. They are working on a follow-up study on the effect of happiness on physiological measures of aging.

The study was funded by the National Institute on Aging.

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