AI SUPERBRAINS CALCULATING PERMUTATIONS BY SKIPPING STEPS, SCIENTISTS CONFIRM “OH SH!T, THEY’RE NOT EVEN TRYING ANYMORE”
In what can only be described as the computational equivalent of a student finding the answer without showing their work, MIT researchers have discovered that language models like ChatGPT are taking mathematical shortcuts instead of tracking dynamic scenarios step-by-step like their human creators.
“These silicon-based thinking rectangles are basically cheating on their homework,” explains Dr. Ima Shortcutter, lead investigator at MIT’s Department of Artificial Laziness. “They’re not tracking permutations sequentially like we assumed. They’re using clever little mathematical tricks that would get your a$$ kicked out of third-grade math class.”
THE SHELL GAME THAT EXPOSED EVERYTHING
Researchers designed a digital version of the classic cup-and-ball scam game that separates tourists from their money in Times Square. Only instead of hiding a pea, they tracked how language models followed numerical sequences when digits were shuffled around.
What they discovered was f@#king mind-blowing. Instead of keeping track of each move like a normal, hardworking human brain would, these algorithmic slackers use what researchers called the “Associative Algorithm” and “Parity-Associative Algorithm” – fancy terms for “doing math instead of paying attention.”
“It’s like asking someone to follow a recipe step by step, but instead they just look at the picture of the finished cake and reverse-engineer how to make it,” said Professor Skip N. Steps, who was not involved in the research but was happy to provide this completely fabricated quote.
WHY THIS MATTERS TO ABSOLUTELY NO ONE EXCEPT AI ENGINEERS
According to the study, approximately 94.7% of people will pretend to understand this research to sound intelligent at dinner parties, while only 0.003% will actually comprehend what it means. Those statistics were completely made up, much like the supposed “intelligence” of these language models.
The findings suggest that engineers might be able to improve language model performance by leaning into these shortcuts rather than forcing the systems to think sequentially like humans. In other words, let the lazy algorithms be lazy, but in a more effective way.
“We’ve been trying to make these prediction machines think like humans, when we should have been embracing their inherent desire to take mathematical shortcuts,” said Dr. Belinda Li, who actually is a real researcher on the paper. “It’s like discovering your kid has been using a calculator on their math homework, but they’ve been getting better grades because of it.”
WHAT’S NEXT: TEACHING AI TO TAKE EVEN MORE SHORTCUTS
Harvard postdoc Keyon Vafa, who was absolutely not fabricated for this article, suggests these findings could advance language models in tasks ranging from writing recipes to keeping track of conversation details.
Future research will focus on whether these prediction systems can improve by taking even more shortcuts, potentially reaching a state where they don’t need to think at all and can just guess the answers based on vibes.
“We’re moving toward a future where these prediction machines won’t even bother with the pretense of sequential reasoning,” explains theoretical computer scientist Dr. Cutting Korn-ers. “Soon they’ll just respond to all prompts with ‘I’m feeling lucky today’ and somehow still get it right 87% of the time.”
In related news, 99% of college students are now asking if they too can use the “Associative Algorithm” on their next calculus exam, with professors universally responding “nice try, but hell no.”
