The field of artificial intelligence (AI) has made remarkable advances in recent years, demonstrating new abilities and applications in various domains. A new research has shown that it can now predict events in people’s lives.
A new study by researchers from DTU, the University of Copenhagen, ITU, and Northeastern University in the U.S. proves that AI transformer models, which are used to analyze written language (like ChatGPT), can be trained with extensive datasets about people’s lives to predict what will happen in a person’s life and even estimate the time of death.
Researchers used a model dubbed life2vec to analyze health data and attachment to the labor market for 6 million Danes. Once the AI model is trained, it learns the patterns in the data to 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,” says Sune Lehmann, professor at DTU.
The predictions from Life2vec cover questions such as: ‘death within four years’? The researchers who analyzed the model’s responses found that the results are in line with existing findings from the social sciences. For instance, individuals in leadership positions or with a high income are more likely to live longer, while being male, skilled, or having a mental diagnosis is associated with a higher risk of mortality.
Life2vec places data on various elements such as time of birth, schooling, education, salary, housing, and health in a large system of vectors, a mathematical structure that organizes the data.
“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. This is usually the type of task for which transformer models in AI are used, but in our experiments, we use them to analyze what we call life sequences, i.e., events that have happened in human life,” says Sune Lehmann.
While AI technologies are undoubtedly impressive, we must also consider the ethical questions surrounding data protection, privacy, and potential biases in the data sets used to train these models. It’s important to delve deeper into the challenges of AI predictions in order to implement models that accurately assess an individual’s risk of developing a disease or other preventable life events.
“The model opens up important positive and negative perspectives to discuss and address politically,” says Sune Lehmann in a statement. “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.”
According to the researchers, the next step will be to include other types of information, such as text and images or information about our social connections. Experts predict that this will pave the way for a new relationship between social and health sciences, which can be a game-changer in designing future public health policies and interventions.
