Energy Equals Mass Times The Singularity Squared

The perfect demonstration of why you shouldn't trust AI for basic math! ChatGPT confidently declares 9.11 > 9.9 (correct), calculates 9.11 - 9.9 = 0.21 (wrong), then when asked to use Python, claims the result is -0.79 due to "floating-point precision errors" (complete nonsense). The actual answer is 0.21, which it originally gave incorrectly but then claimed was correct! It's like watching a student make up increasingly elaborate excuses for getting 2+2=5. This is why mathematicians drink.

The Python programming journey in one perfect comic! Starting off, Python seems so friendly – just dip your toes in and you're coding! "Look at me, I'm a programmer now!" But then you decide to dive deeper into machine learning and AI, and suddenly you're drowning in mathematical concepts, data mining techniques, and algorithms you never knew existed. That final panel with the wide-eyed, traumatized dinosaur face is basically every coder who thought "I'll just learn a bit of TensorFlow this weekend" and ended up questioning their life choices. The learning curve isn't a curve – it's a cliff with sharks at the bottom!

The beautiful irony of claiming machine learning as your strength while demonstrating exactly how it fails. First you give a completely wrong answer (0 instead of 15), then after being corrected, you stubbornly repeat "It's 15" regardless of the actual question. Congratulations, you've perfectly replicated how machine learning models work—they don't actually understand math, they just pattern-match previous inputs and outputs with zero comprehension. Next time someone tells you AI will take all our jobs, show them this and remind them that computers are still basically fancy rocks we tricked into thinking.

Calling AI "Schrödinger's Code" is painfully accurate. Your program simultaneously works and doesn't work until you run it. And even after observation, it might collapse into a different state when you show it to your supervisor. Much like quantum superposition, AI exists in multiple states of functionality—brilliantly solving problems one minute, then hallucinating that Nebraska is a coastal state the next. The uncertainty principle of programming: you can know what your code is supposed to do, or what it actually does, but never both at the same time.

The academic turf war just got spicier. In 1990, physicist Edward Witten snagged the Fields Medal (math's equivalent of a Nobel) despite being, you know, not a mathematician. Fast forward to 2024, and computer scientist Geoffrey Hinton wins a Physics Nobel despite not being a physicist. Mathematicians are experiencing that special feeling when someone raids your intellectual refrigerator and then wins awards for the sandwich they made with your ingredients. The disciplinary boundaries in science are becoming as theoretical as string theory itself.

Just discovered why AI is progressing so rapidly. It's simply AI + 1. Each iteration adds another unit of intelligence, creating an infinite recursion loop of improvement. My colleagues are furious I've exposed the secret algorithm that every tech company has been hiding in their basement servers. Next week: revealing why E actually equals mc² + a small fudge factor no one talks about.