Curved neural networks enable AI memory recall through geometric design The critical take-out from this is they discovered geometry is an intrinsic aspect of memory, and how we can make memory work more efficiently. This is the same as my own work, and has implications for quantum computing. Already, the folds in our brains have been correlated to the individual's personality and, in a Singularity, everything inevitably transforms into geometry. Combining the right geometry with a collection of pattern matching neural networks, empowers them to sometimes be 125% efficient. Efficiency has no demonstrable meaning, outside of a specific context, and every context must have its own geometry.
In classical physics, the smaller and more hollow anything becomes, the more efficient. In other words, the less substance anything has, the more efficient it becomes, yet a torus is the most efficient of all shapes in quantum mechanics. That's because bubbles can be up to 100% efficient, while quantum mechanics can be 125% efficient. The toroidal shape can be attributed to a universal recursion in the principle of identity, in which everything in the universe appears to have difficulty deciding whether to explode or implode, explaining the four forces. What's required is a multifractal equation, that I'm still working on, but its almost done. They're still attempting to put a square peg in a round hole, using abstract mathematics, when our universe is metaphorical, requiring linguistic analysis. Memory requires a past, yet time can flow backwards, giving the Three Stooges nightmares.
I bet you solve that simultaneously with finding the last digit in Pi. Thus launching the singularity. Sarcastic value=?
Pi has turned out to obey a multifractal equation, it isn't random. You've been fed too much bullshit, by academics.
The Peter Principle is the only constant that applies to both people and inanimate objects, and reality is for those who watched the wrong cartoons.
