Scientists invent doom calculator that can predict when you’ll DIE and how much money you’ll earn with 78% accuracy

• The algorithm works like ChatGPT, making predictions based on sentences
• It can predict, with 78-percent accuracy, a person’s earnings and time of death
• READ MORE: The ‘startlingly accurate’ AI that can tell you when you’ll DIE

Scientists have developed an algorithm that uses the story of a person’s life to predict how they will live and when they will die.

According to a new study, the model called ‘life2vec,’ is accurate about 78 percent of the time which puts it on par with other algorithms designed to predict similar life outcomes.

But unlike other models, it works like a chatbot, using existing details to predict what comes next.

It was built by scientists in Denmark and the US who trained a machine learning algorithm on a massive pool of Danish data, feeding it all sorts of information on over six million real people, including income, profession, place of residence, injuries, and pregnancy history. 

Death clock? The new tool called life2vec can predict the likelihood that someone will die within a certain period of time, but don’t worry – its data are not available to the public

Their end result was a model that can process plain language and generate predictions about a person’s likelihood of dying early, or their income over the lifespan.

Some of the factors that can lead to earlier death include being male, having a mental health diagnosis, or being in a skilled profession. Things linked to longer life include higher income or being in a leadership role. 

Considering each part of your life as if they were words in a sentence, life2vec predicts where the story will go based on what has been written so far. 

Just like ChatGPT users ask it to write a song, poem, or essay, scientists can ask life2vec simple questions like ‘death within four years?’ for a certain person.

The model was trained on data from 2008 to 2016.

Based on their population data, it correctly predicted who had died by 2020 more than three-quarters of the time. 

The research appeared in Nature Computational Science. 

To protect the personal information of the people whose data were used to train the system, though, it is not available for the general public – or companies – to use, lead researcher Sune Lehmann told DailyMail.com.

Based on data on mi

‘We are actively working on ways to share some of the results more openly, but this requires further research to be done in a way that can guarantee the privacy of the people in the study,’ said Lehmann, professor of networks and complex systems at Technical University of Denmark.

Even when the model is finally available to the public, Danish privacy laws would make it illegal to use life2vec to make decisions about individuals – like writing insurance policies or making hiring decisions.

In much the same way that ChatGPT and other large language models have been trained on troves of existing written works, life2vec was taught by data from people’s lives, written out as a series of data-rich sentences. 

These include sentences like ‘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.’

Lehmann and his team assigned different tokens to each piece of information, and these pieces of data were all mapped out in relation to each other. 

Categories in people’s life stories run the whole range of human experiences: a forearm fracture is represented as S52; working in a tobacco shop is coded as IND4726, income is represented by 100 different digital tokens; and ‘pospartum hemorrhage’ is O72.

Many of these relationships are intuitive, like profession and income – certain jobs make more money.

But what life2vec does is map the huge constellation of factors that make up an individual’s life, allowing someone to ask it to make a prediction based on millions of other people and many many factors.

It can also make predictions about people’s personality.

To do this, Lehmann and his team trained the model to predict people’s answers to questions on a personality test.

The test asks respondents to rate 10 items based on how much they agree, items such as ‘The first thing that I always do in a new place is to make friends,’ or I rarely express my opinions in group meetings.’

It’s important to note, Lehmann said, that the data were all from Denmark, so these predictions may not hold true for people living in other places – besides the fact that most people probably don’t actually want to know when they will die. 

‘The model opens up important positive and negative perspectives to discuss and address politically,’ Lehmann told Newswise.

‘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.’  

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