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Artificial intelligence could help us better understand the effects of psychedelic drugs, by analysing narrative reports written by people who are using them.

ÒÁÈ˾þÃs barely understand how existing psychedelic drugs work to alter perception and intensify emotions, let alone keep pace with new ones flooding the market – often sold as “bath salts” or “herbal incense”.

Enter artificial intelligence. of the University of Chicago and colleagues used machine-learning algorithms – a type of artificial intelligence that can learn about a given subject by analysing massive amounts of data – to examine 1000 reports uploaded to the website by people who had taken mind-altering drugs.

They found that the frequency with which certain words appeared could identify the drug taken with 51 per cent accuracy on average – compared with 10 per cent by chance. MDMA (ecstasy) usage was identified with an accuracy of 87 per cent.

The drug DMT (N,N-dimethyltryptamine) acts on the brain in different ways from the drug Salvia (Salvia divinorum), but the algorithms inferred that both elicit a similar response. This might be because both are typically smoked and so enter the bloodstream quickly, says Baggott. “Smoked psychedelic drugs may ‘hit’ people hard and fast in a similar way.”

Baggott hopes the work will aid research into the effects of new and existing drugs. “You need to start with some theories about the effects of a drug,” he says. “Machine learning can help us form those theories.”

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