Canadian researchers have developed a dreadful artificial intelligence (AI) that predicts what designer drugs will hit the streets before they’re even invented — in order to assist cops in cracking down on them. They created a model that can predict future designer drugs before they hit the market. But don’t get too excited: the researchers aren’t looking to create the next big thing. Instead, they want the cops to be the first to find it.
Medical researchers at the University of British Columbia announced that they had fed a machine learning algorithm a database of information on “known psychoactive substances,” with the goal of predicting new designer drugs before the chemists and dealers who sell them even have a chance to make them real.
Worse, UBC not only boasts that it is creating a “‘Minority Report’ for new designer drugs,” with no apparent understanding that “Minority Report” was a cautionary tale, but the press release associated with the project also makes it clear that they are doing so to assist cops in fighting the drug war — which, of course, has been a dismal failure.
Rather than receiving traditional training in a meth lab, the neural network was trained on a database of known psychoactive substances. The model advanced to creating its own concoctions after meticulously studying the structures of these drugs. The inexperienced chemist proved to be a quick study. It generated structures for a whopping 8.9 million potential designer drugs in total. While others retired to a life of experimental inebriation, the hardworking researchers had more work to do.
One of the senior authors of a paper on the UBC neural network published in the journal Nature Machine Intelligence joined the ranks of those who completely misread cautionary speculative fiction when touting this dangerous new AI, according to an actual quote from the press release.
“The fact that we can predict what designer drugs will appear on the market before they actually appear is a bit like the 2002 sci-fi film, ‘Minority Report,’ where foreknowledge about criminal activities about to take place helped significantly reduce crime in a future world,” computer scientist David Wishart boasted. “Essentially, our software gives law enforcement and public health programs a head start on the clandestine chemists and tells them what to look for.”
While this type of machine learning appears to have the potential to fail in the same way that so many others have, it appears that police departments in Europe and the United States are already using it. So far, UBC has boasted about its use by the US Drug Enforcement Agency, the European Union’s Monitoring Centre for Drugs and Drug Addiction, and even the United Nations Office on Drugs and Crime.
Law enforcement agencies are racing to identify and regulate new versions of dangerous psychoactive drugs like bath salts and synthetic opioids, even as covert chemists work to synthesize and distribute new molecules with the same psychoactive effects as traditional drugs of abuse.
It can take months to identify these so-called “legal highs” within seized pills or powders, during which thousands of people may have already used a new designer drug. However, new research is already assisting law enforcement agencies worldwide in reducing identification time from months to days, which is critical in the race to identify and regulate new versions of dangerous psychoactive drugs.
Again, it’s worth noting that the stated goal of the drug war — eradicating the harms of drug addiction and bringing to justice the undeniable bad guys who steal and kill in the service of said drugs — has been a massive failure thus far. And if its new frontier is based on a film based on the book that popularized the term “pre-crime,” we’re in big trouble.