You don’t have to go to a fortune teller with a crystal ball, tarot cards, or a psychic with alleged “extrasensory perception” to predict your personal future. Artificial intelligence developed to model written language can now be used to predict events in people's lives.
Research from the University of Copenhagen in Denmark and Northeastern University in Boston shows that if you use large amounts of data about people’s lives and train so-called “transformer models” like ChatGPT that are used to process language, they can systematically organize the data and predict what will happen in a person’s life and even estimate the time of death.
A transformer model is an AI, deep-learning data architecture used to learn about language and other tasks. The models can be trained to understand and generate language.
The transformer model is designed to be faster and more efficient than previous models and is often used to train large language models on large datasets.
In a new scientific article published in the prestigious journal Nature Computational Science entitled “Using Sequences of Life-events to Predict Human Lives,” the team analyzed health data and attachment to the labor market for six million Danes in a model dubbed “life2vec.” They accomplished this by drawing on a comprehensive registry dataset that has been available in Denmark for several years.
After the model was trained in an initial phase, learning the patterns in the data, it was shown to outperform other advanced neural networks and predict outcomes such as personality and time of death with high accuracy.
“We used the model to address the fundamental question: to what extent can we predict events in your future based on conditions and events in your past?
Scientifically, what is exciting for us is not so much the prediction itself, but the aspects of data that enable the model to provide such precise answers,” said Danish Prof. Sune Lehmann, and first author of the article.
The predictions from Life2vec are answers to general questions such as “'death within four years?”
When the researchers analyzed the model’s responses, the results were consistent with existing findings within the social sciences; for example, all things being equal, individuals in a leadership position or with a high income are more likely to survive, while being male, skilled or having a mental diagnosis is associated with a higher risk of dying.
Life2vec encodes the data in a large system of vectors, a mathematical structure that organizes the different data. The model decides where to place data on the time of birth, schooling, education, salary, housing, and health.
“What’s exciting is to consider human life as a long sequence of events, similar to how a sentence in a language consists of a series of words,” they explained.
“This is usually the type of task for which transformer models in AI are used, but in our experiments, we used them to analyze what we call life sequences – events that have happened in human life,” Lehmann said.
Data protection concerns
The researchers pointed out that ethical questions such as protecting sensitive data, privacy, and the role of bias in data surround the life2vec model. These challenges must be understood more deeply before the model can be used, for example, to assess a person’s risk of contracting a disease or other preventable life events.
“The model opens up important positive and negative perspectives to discuss and address politically. Similar technologies for predicting life events and human behavior are already used today inside tech companies that, for example, track our behavior on social networks, profile us extremely accurately, and use these profiles to predict our behavior and influence us.
This discussion needs to be part of the democratic conversation so that we consider where technology is taking us and whether this is a development we want,” Lehmann continued.
According to the researchers, the next step would be to incorporate other types of information, such as text and images or information about our social connections. This use of data, they suggested, opens up a whole new interaction between social and health sciences.