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Harvard-developed AI identifies shortest path to human happiness

colorful human brain illustration

Researchers have created a digital model of psychology aimed at improving mental health. The system offers superior customization and identifies the shortest path to a mental stability group for any individual.

Deep Longevity, in collaboration with Harvard Medical School, presents a deep learning approach to mental health.

Deep Longevity published an article in Aging-US describing 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 numerical models of human psychology based on data from the Midlife in the United States study.

The first model is a set of deep neural networks that predicts respondents’ chronological age and psychological well-being 10 years from now using information from a psychological survey. This model describes the trajectories of the human mind as it ages. It also demonstrates that the ability to form meaningful bonds, as well as mental autonomy and mastery of the environment, develop with age. It also suggests that the focus on personal progress is steadily declining, but that the sense of having a purpose in life only fades after age 40-50. These findings add to the growing body of knowledge about socio-emotional selectivity and hedonic adaptation in the context of adult personality development.

AI-based recommendation engine

The article describes an AI-based recommendation engine that can estimate a person’s psychological age and future well-being based on a constructed psychological survey. AI uses a respondent’s information to place them on a 2D map of all possible psychological profiles and deduce ways to improve their long-term well-being. This model of human psychology can be used in digital self-help applications and in therapist sessions. Credit: Michelle Keller

The second model is a self-organizing map that was created to serve as the basis for a recommendation engine for mental health apps. This unsupervised learning algorithm divides all respondents into groups based on their likelihood of developing depression and determines the shortest path to a mental stability group for any individual. Alex Zhavoronkov, director of longevity at Deep Longevity, says, “Existing mental health apps offer generic advice that applies to everyone but is not suitable for anyone. We’ve built a system that’s scientifically sound and offers superior customization. »

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

FuturSelf is a free online mental health service that offers advice based on an AI psychological profile assessment. The heart of FuturSelf is represented by a self-organized map that ranks respondents and identifies the most appropriate ways to improve their well-being. Credit: Fedor Galkin

A leading expert in biogerontology, Professor Vadim Gladyshev of Harvard Medical School, comments on the potential of FuturSelf:

“This study offers interesting insight into psychological age, future well-being, and risk of depression, and demonstrates a novel application of machine learning approaches to psychological health issues. It also broadens our view of 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.

Reference: “Optimizing Future Wellbeing with Artificial Intelligence: Self-Organizing Maps (SOM) for Identifying Islands of Emotional Stability” by Fedor Galkin, Kirill Kochetov, Michelle Keller, Alex Zhavoronkov, and Nancy Etcoff, June 20 2022, Aging-United States.
DOI: 10.18632/aging.204061

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