Cognitive Chemistry, P vs NP, and Artificial Intelligence Walk into a Bar…

When you think of artificial intelligence (AI) you may think of Ava from Ex Machina or some sort of super smart robot-human hybrid. However, in computational complexity theory, AI is defined as a system that perceives its environment and takes actions that maximize its chance of success at a specific goal. In other words, AI is a cognitive system that has the capability to make decisions by solving a set of optimization problems and have a real-time response to its environmental signals.

Many AI optimization problems fall into Nondeterministic Polynomial time (NP) problems. Though easy to understand and check the correctness of a solution; NP problems are difficult to solve due to their exponential complexity. No efficient solution has been found to date. Indeed, P vs NP is one of the 7 unsolved Millennium Problems as defined by Clay Mathematics Institute. A very known NP problem is the Traveling Salesman Problem, first introduced in 1800s, the Travelling Salesman Problem describes a salesman who must travel between N cities. The order of cities doesn’t matter, as long as he visits each one during his trip, and finishes where he started.

While processing of NP problems cannot be done in logical time by current silicon based computing systems, hundreds of thousands of NP problems are running through real-time molecular operations in a living cell. Unlike current computing systems that utilize a sequential binary or quantum coding; biological coding in living systems can execute billions of parallel operations simultaneously. This massive parallelism comes from the huge number of coding-capable molecules that chemically interact in a small volume.

Information storage property of DNA molecules has been a major focus for AI researchers with hopes to create a biologically inspired coding system. However, unlike DNA computing, data processing in biological systems is not limited to a single layer of coding. Deep understanding of mechanisms of coding and data processing in biological systems reveals multiple layers of coding including coding languages of DNA, mRNA, amino acids, and proteins.

Simulation of data processing mechanisms in biological systems at the molecular level provides promising tools for next generation cognitive systems with the capability of real-time responses to environmental signals. Generation of these super intelligent systems can revolutionize different branches of science.

In the next posts, I’ll share my vision for creation of a biologically inspired coding systems capable of solving NP problems, and my passion for integration of life, computer and material sciences.