Technology
A study published in Nature Computational Science on the 18th describes a machine-based Xi method that can accurately ** human life from different aspects, including the likelihood of early death and subtle differences in personality. The model may provide quantitative insights into human behavior.
Social scientists have different views on the question of whether human life can be **. Although sociodemographic factors play an important role, it has not been possible to accurately determine life outcomes**.
Using data on education, health, income, occupation, and other life events of about 6 million people in the Danish National Registry, the research team at the Technical University of Denmark devised a machine Xi method to construct an individual's human life trajectory. The team adapted language processing techniques to express human life in a way similar to the language in the model. This approach generates a glossary of life events in a similar way that a language model captures the complex relationships between words. Their proposed model, called "life2vec", identifies the complex relationships between health-related diagnoses, places of residence, income levels, and other concepts, and uses a compression vector to encode an individual's life as the basis for life outcomes.
The research team demonstrated that the model can achieve the premature mortality rate, which is the probability that individuals aged 35 to 65 years will survive for 4 years from January 1, 2016. In addition, its ability to capture subtle personality differences surpassed current state-of-the-art models and baseline standards, improving performance by at least 11%.
The findings suggest that by characterizing the complex associations between social outcomes and health outcomes, it may be possible to accurately ** life outcomes. But the team also stressed that their research was only an exploration of possibilities and should only be used in the real world under regulation that ensures that individual rights are protected.