* This article originally appeared on Motherboard Germany. Since its original publication it has been updated with new information by Motherboard US.*

What do curable cancer, fair capitalism, and the perfect game of * Super Mario Bros*. all have in common? Per a mathematical theory, the solution to any one of these problems would allow us to quickly solve the others. All that’s needed are better algorithms to prove that complicated questions—such as protein folding, efficient marketplaces, and combinatorial analyses—are merely variations of simpler problems that supercomputers are already able to solve.

But how can one algorithm simplify extremely complicated problems? That depends on another question: What if complicated problems are really just simple problems in disguise? This riddle remains one of the biggest unsolved questions of modern mathematics, and is one of the seven Millennium Prize Problems, for which every accepted answer is rewarded with one million dollars.

Now, a German man named Norbert Blum has claimed to have solved the above riddle, which is properly known as the P vs NP problem. Unfortunately, his purported solution doesn’t bear good news. Blum, who is from the University of Bonn, claims in his recently published 38-page paper that P does not equal NP. In other words, complicated problems * are* fundamentally different than straightforward problems and it doesn’t look like our high-performance computers will be able to crack these most difficult problems anytime in the near future. And in the days since his paper was published, numerous mathematicians have begun to raise questions about whether Blum solved it at all.

## What exactly is the P versus NP Problem?

Most computer scientists tend to agree with Blum’s conclusion. Hard problems are hard, easy problems are easy.

In computer science, easy problems usually fall under the banner of P. This means that they can be solved in “polynomial time,” which is a lot like saying that they can be solved in a reasonable period of time. Much more difficult are the NP problems, which computers are unable to solve in a reasonable time frame. For practical purposes, NP problems might as well just be unsolvable by computers. (Note, however, that NP stands for “nondeterministic polynomial time” rather than “not polynomial” time.)

Here are some examples of NP problems:

** Protein folding:** The process through which proteins in a biological organism obtain their structure. Better insight into this process could help us recognize or even hinder mutations, which could cure certain forms of cancer.

** Optimized routefinding: **An optimized travel route through 15 different cities without visiting the same city twice? A hard nut to crack, even for a supercomputer, which is why this is also considered an NP problem in computer science.

** The perfect chess game:** A game of chess has infinite possible moves, such that even a supercomputer with incredible capacity cannot determine a perfect tactic. Many mathematicians consider this problem so difficult that they don’t deem it an NP problem, but rather consider it completely out of the realm of possibility.