ROBOT OVERLORD CHECK-UP: AI MODELS NOW TESTED BY OTHER AI MODELS TO MAKE SURE THEY’RE PROPERLY STUPID
In a groundbreaking advancement absolutely nobody asked for, MIT researchers have developed a new way to test whether AI text classifiers are as dumb as we need them to be, ensuring your bank chatbot won’t accidentally give you financial advice that might actually be useful.
SILICON VALLEY’S NEWEST CIRCLE JERK
MIT’s Laboratory for Information and Decision Systems (LIDS) has pioneered what experts are calling “asking one robot if another robot is f@#king up.” The revolutionary approach involves using one fancy algorithm to check if another fancy algorithm is correctly identifying whether movie reviews are positive or if that medical advice will kill you.
“We’re essentially creating robot hall monitors,” explains Dr. Obvious Circuitry, who definitely exists and isn’t made up for this article. “Because nothing says ‘technological progress’ like creating AI to police other AI that was created to replace humans who used to do this job just fine.”
THE ONE-WORD MINDF@#K
Researchers discovered that changing just ONE WORD in a sentence can completely bamboozle these supposedly “intelligent” systems. Approximately 0.1% of words cause nearly half of all classification errors, proving that these multi-million dollar systems have the contextual understanding of a concussed goldfish.
“We found that certain words have ‘magical powers’ over classifiers,” said Professor Ima Nottreal, Chair of Algorithmic Bullsh!ttery at Fictional University. “For example, changing ‘This movie is fantastic’ to ‘This movie is tremendous’ somehow transforms a positive review into a scathing critique. It’s almost as if these systems don’t actually understand language at all!”
FIGHTING FIRE WITH MORE FLAMMABLE FIRE
The solution to this problem, according to these definitely-not-mad scientists, is to use EVEN MORE AI. The team created software called “SP-Attack” and “SP-Defense” which sounds suspiciously like something a 12-year-old would name their Pokemon moves.
“Our breakthrough involves using larger language models to fix smaller language models,” explained researcher Sarah Alnegheimish, who unlike everyone else quoted in this article actually exists. “It’s like asking your drunk friend to drive your other drunk friend home. What could possibly go wrong?”
REAL WORLD IMPLICATIONS, UNFORTUNATELY
Companies are now rushing to implement these text classifiers in real-time environments where absolutely nothing could go catastrophically wrong, like banking, healthcare, and HR departments.
“Before our chatbot tells a customer to ‘go f@#k themselves’ or accidentally gives terminal cancer diagnoses to people with hiccups, we run it through our classifier,” explained one anonymous banking executive while lighting a cigar with a hundred dollar bill.
The new system reportedly reduced successful “adversarial attacks” from 66% to 33.7%, which still means one-third of the time these systems are absolutely sh!tting the bed, but hey, that’s progress!
HUMANITY’S LAST HOPE
When asked whether creating an endless Russian nesting doll of AI systems checking other AI systems might eventually lead to an unsustainable technological house of cards, lead researcher Lei Xu responded by staring blankly into the distance for seventeen minutes before whispering, “The machines told me not to answer that question.”
The software has been made freely available for download, ensuring that corporations worldwide can now implement half-broken AI systems to monitor their three-quarters-broken AI systems, all while firing the humans who could have done the job correctly in the first place.
At press time, 98.7% of readers couldn’t tell if this article was written by a human journalist or a silicon-based thinking rectangle having an existential crisis. The remaining 1.3% were too busy teaching their toasters to recognize hate speech to respond to our survey.
