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Harvard-developed AI identifies the shortest path to human happiness

The researchers created a digital model of psychology to improve mental health. The system provides superior personalization and identifies the shortest path to a cluster of mental stability for each individual.

Deep Longevity, in collaboration with Harvard Medical School, presents an in-depth learning approach to mental health.

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 United States Midlife study.

The first model is an ensemble of deep neural networks that predicts the chronological age and psychological well-being of respondents over 10 years using information from a psychological survey. This model shows the trajectories of the human mind as it ages. It also shows that the ability to form meaningful connections, as well as mental autonomy and environmental control, develops with age. It also suggests that the emphasis on personal progress is constantly decreasing, but the sense of purpose in life only fades after 40-50 years. These results contribute to the growing body of knowledge about social-emotional selectivity and hedonic adjustment 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. The AI ​​uses a respondent’s information to put it 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 during therapist sessions. Credit: Michelle Keller

The second model is a self-organizing map created to serve as the basis for a recommendation engine for mental health applications. This unsupervised learning algorithm divides all respondents into clusters depending on their likelihood of developing depression and determines the shortest path to a cluster of mental stability for each individual. Alex Zhavoronkov, the chief longevity officer of Deep Longevity, explains: “Existing mental health applications provide common advice that applies to everyone, but is suitable for no one. We built a system that is scientifically sound and offers superior personalization.”

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

FuturSelf is a free online mental health service that provides guidance based on a psychological profile assessment by AI. The core of FuturSelf is represented by a self-organizing map that classifies respondents and identifies the most appropriate ways to improve one’s well-being. Credit: Fedor Galkin

A leading biogerontology expert, Professor Vadim Gladyshev of Harvard Medical School, says of FuturSelf’s potential:

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

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

The study was funded by the National Institute on Aging.

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

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