A research project from DTU, University of Copenhagen, ITU, and Northeastern University in the US shows that if you use large amounts of data about people's lives and train so-called 'transformer models', which (like ChatGPT) 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. In a new scientific article, 'Using Sequences of Life-events to Predict Human Lives', published in Nature Computational Science, researchers have analyzed health data and attachment to the labour market for 6 million Danes in a model dubbed life2vec.
After the model has been trained in an initial phase, i.e., learned the patterns in the data, it has been shown to outperform other advanced neural networks (see fact box) and predict outcomes such as personality and time of death with high accuracy.
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