The current frontiers in the description and simulation of advanced physical and biological phenomena observed within all scientific disciplines are pointing toward the importance of the development of robust mathematical descriptions that are error resilient. Complexity research is lacking deeper knowledge of the design methodology of processes that are capable to recreate the robustness, which is going to be studied on massive-parallel computations (MPCs) implemented by cellular automata (CA). A simple, two-state cellular automaton having an extremely simple updating rule, which was created by John H. Conway called the ’Game of Life’ (GoL) is applied to simulate and logic gate using emergents. This is followed by simulations of a robust, generalized GoL, which works with nine states instead of two, that is called R-GoL (open-source). extra states enable higher intercellular communication. The logic gate is simulated by the GoL. It is destroyed by injection of random faulty evaluations with even the smallest probability. The simulations of the R-GoL are initiated with random values. several types of emergent structures, which are robust to injection of random errors, are observed for different setups of the R-GoL rule. The GoL is not robust. The R-GoL is capable to create and maintain oscillating, emergent structures that are robust under constant injection of random, faulty evaluations with up to 1% of errors. The R-GoL express long-range synchronization, which is together with robustness facilitated by designed intercellular communication.

Science is currently becoming aware of the challenges in the understanding of the very root mechanisms of massively parallel computations that are observed in literally all scientific disciplines, ranging from cosmology to physics, chemistry, biochemistry, and biology. This leads us to the main motivation and simultaneously to the central thesis of this review: “Can we design artificial, massively parallel, self-organized, emergent, error-resilient computational environments?” The thesis is solely studied on cellular automata. Initially, an overview of the basic building blocks enabling us to reach this end goal is provided. Important information dealing with this topic is reviewed along with highly expressive animations generated by the open-source, Python, cellular automata software GoL-N24. A large number of simulations along with examples and counter-examples, finalized by a list of the future directions, are giving hints and partial answers to the main thesis. Together, these pose the crucial question of whether there is something deeper beyond the Turing machine theoretical description of massively parallel computing. The perspective, future directions, including applications in robotics and biology of this research, are discussed in the light of known information.

Man-made systems, including artificial intelligence (AI) and machine learning (ML) methods, are usually constructed using mechanistic approaches, which inevitably fail with a failure of any of their single constituting components. Contrary to them, biological systems are typically self-organizing emergent systems operating far-from-equilibrium and capable of self-repair. The outputs of research from experimental biology, behavior of insect swarms, morphological growth, limb regrowth, and other areas are confirming the above statement. This leads us to the central question of this chapter: “Can intelligence be achieved without the presence of neurons and brain structures?” That is why research on emergent information processing (EPI) is reviewed and deepened in this contribution. What are the constituting elements of the Life? According to this theoretical research, it is hypothesized that, using a certain level of abstraction, the Life is created by a set of microprocesses running above a matrix, which cease to exist along with the matrix and processes governing it. Let us see where it takes us using the open-source Python cellular automata simulating software GoL-N24 v1.4.