Skip to main content

Drug enforcement agency turns to A.I. to help sniff out doping athletes

In the constant battle between doping athletes and the officials trying to catch them, the athletes have almost always remained ahead of the curve. They’re the better-funded side in the war and can stay ahead of the latest testing methods that regulatory agencies are using. Most notably, in cycling, it’s an open secret that many of the sport’s athletes are on some kind of doping regimen. Lance Armstrong, the disgraced American cyclist who became the sport’s most popular figure before admitting to years of doping, was able to pass drug tests throughout his career despite being the most tested athlete on the planet.

So, the testing agencies are in dire need of help. Hoping to narrow the divide between the cheaters and the testing methods is the World Anti-Doping Agency (WADA), which recently announced it would begin using artificial intelligence systems to help flag suspected cheaters across all sports.

Recommended Videos

“There’s a lot of data that is being collected in anti-doping — whether it is through the [athlete’s biological] passports, through the tests, through the results of the athletes,” WADA Director Olivier Niggli told iNews. “If you manage to create a system that will meaningfully use this data, I think you can create some very powerful tools.”

WADA has lamented that its lack of resoures (both in personnel and finances) makes its job of catching doping athletes difficult much of the time. With A.I., the agency would be able to sift through an exponetial amount of extra data and subsequently raise red flags when it catches suspect patterns in an athlete’s performance and/or testing results. While the A.I. would be able to highlight who might be using performance-enhancing drugs, it would not be considered evidence of cheating. Instead, that highlighted athlete might subsequently go through a more rigorous drug-testing regimen as a result of being flagged by the computer.

“Only sophisticated algorithms would be able to spot the differences, which would allow the anti-doping organizations to focus on the right individuals,” Niggli said.

There are not a whole lot of specifics on the program yet as WADA will be testing a few pilot programs in the coming years. It’s likely going to take time before WADA might see real results from its new efforts.

“I hope that in five years we will be much better at analyzing all this data that we already have and are already collected,” Niggli said.

Jacob May
Former Digital Trends Contributor
Jacob joined Digital Trends after beginning his career as a sports reporter and copy editor in the newspaper industry and…
Women with Byte: Vivienne Ming’s plan to solve ‘messy human problems’ with A.I.
women with byte vivienne ming adjusted

Building A.I. is one thing. Actually putting it to use and leveraging it for the betterment of humanity is entirely another. Vivienne Ming does both.

As a theoretical neuroscientist and A.I. expert, Ming is the founder of Socos Lab, an incubator that works to find solutions to what she calls “messy human problems” -- issues in fields like education, mental health, and other areas where problems don't always have clear-cut solutions.

Read more
Why teaching robots to play hide-and-seek could be the key to next-gen A.I.
AI2-Thor multi-agent

Artificial general intelligence, the idea of an intelligent A.I. agent that’s able to understand and learn any intellectual task that humans can do, has long been a component of science fiction. As A.I. gets smarter and smarter -- especially with breakthroughs in machine learning tools that are able to rewrite their code to learn from new experiences -- it’s increasingly widely a part of real artificial intelligence conversations as well.

But how do we measure AGI when it does arrive? Over the years, researchers have laid out a number of possibilities. The most famous remains the Turing Test, in which a human judge interacts, sight unseen, with both humans and a machine, and must try and guess which is which. Two others, Ben Goertzel’s Robot College Student Test and Nils J. Nilsson’s Employment Test, seek to practically test an A.I.’s abilities by seeing whether it could earn a college degree or carry out workplace jobs. Another, which I should personally love to discount, posits that intelligence may be measured by the successful ability to assemble Ikea-style flatpack furniture without problems.

Read more
The BigSleep A.I. is like Google Image Search for pictures that don’t exist yet
Eternity

In case you’re wondering, the picture above is "an intricate drawing of eternity." But it’s not the work of a human artist; it’s the creation of BigSleep, the latest amazing example of generative artificial intelligence (A.I.) in action.

A bit like a visual version of text-generating A.I. model GPT-3, BigSleep is capable of taking any text prompt and visualizing an image to fit the words. That could be something esoteric like eternity, or it could be a bowl of cherries, or a beautiful house (the latter of which can be seen below.) Think of it like a Google Images search -- only for pictures that have never previously existed.
How BigSleep works
“At a high level, BigSleep works by combining two neural networks: BigGAN and CLIP,” Ryan Murdock, BigSleep’s 23-year-old creator, a student studying cognitive neuroscience at the University of Utah, told Digital Trends.

Read more