Danish researchers reveal that they have successfully predicted certain aspects of human life, such as an individual’s likelihood of dying young, using sophisticated machine learning algorithms.
Their study, published this week in the journal in Nature Computational Science, details how a machine-learning algorithm model called life2vec predicted the outcome of a person’s life and their actions when presented with highly specific data about them.
With that data, “we can make any kind of prediction,” said Sune Lehmann, the study’s lead author and a professor at the Technical University of Denmark. However, the researchers note that it is a “research prototype” and cannot perform any “real-world tasks” in its current state.
Lehmann and his co-authors used data from a national register in Denmark detailing a diverse grouping of 6 million people. They included information from 2008 to 2016 related to major aspects of life such as education, health, income and occupation.
The researchers adapted language processing techniques and generated a vocabulary for life events so life2vec could interpret sentences based on the data, such as “In September 2012, Francisco received twenty thousand Danish kroner as a guard at a castle in Elsinore” or “During her third year at secondary boarding school, Hermione followed five elective classes.”
The algorithm then learned from that data, Lehmann says, and was able to make predictions about certain aspects of people’s lives, including how they might think, feel and behave, and even whether the person might die in the next few years.
