Artificial Intelligence gets scarier, can calculate human life expectancy 78 per cent accurately

Chandigarh, December 22

No one, till date knew the day they will die. The last thing that humans wanted to know was the day they would die, and creepy as it many sound, an artificial intelligence algorithm would predict human life expectancy 78 per cent accurately.

Details about the project, conducted by researchers in Denmark and the US, were published this week in the Nature Computational Science online journal.

They created an AI machine-learning transformer model – somewhat akin to ChatGPT – although people can’t interact with it as they do with ChatGPT.

But the model, called life2vec, crunched data – age, health, education, jobs, income and other life events – on more than 6 million people from Denmark supplied by the country’s government, which collaborated on the research.

Lehmann, who is a professor of network and complex systems from the Technical University of Denmark along with the co-authors, in the report introduced the world to an algorithm called as “life2vec”, which has been using selective information from the life of an individual – which includes health history, residence, income and profession – to find their life expectancy, which they claim to be 78 per cent accurate.

“We use the fact that in a certain sense, human lives share a similarity with language,” said Lehmann. “Just like words follow each other in sentences, events follow each other in human lives,” the professor added.

The newly-built model life2vec is a little different from the famous ChatGPT, which is a bot that is being employed by tech wizards to help them find their dream jobs and even find the perfect outfit. This AI model is capable of computing the outcomes of life by closely examining a person’s past.

“This model can predict almost anything,” said Lehmann, while further emphasising that his research team has used specialised programmes to foretell the personalities of people.

“We predicted death because it’s something people have worked on for many years (for example, insurance companies), so we had a good sense of what was possible,” he added.