How Did the Eukaryotes Evolve?

Further illusions: On key evolutionary mechanisms that could never fit with Modern Synthesis

In the light of illusions of the Modern Synthesis (MS) listed by Noble (2021a), its key concept, that gradual accumulation of gene mutations within microevolution leads to macroevolution, requires reexamination. In this article, additional illusions of the MS are identified as being caused by the absence of system information and correct history. First, the MS lacks distinction among the two basic types of information: genome-defined system and gene-defined parts-information, as its treatment was based mostly on gene information. In contrast, it is argued here that system information is maintained by species-specific karyotype code, and macroevolution is based on the whole genome information package rather than on specific genes. Linking the origin of species with system information shows that the creation and accumulation of the latter in evolution is the fundamental question omitted from the MS. Second, modern evidence eliminates the MS's preferred theory that present evolutionary events can be linearly extrapolated to the past to reconstruct Life's history, wrongly assuming that most of the fossil record supports the gradual change while ignoring the true karyotype/genome patterns. Furthermore, stasis, as the most prominent pattern of the deep history of Life, remains a puzzle to the MS, but can be explained by the mechanism of karyotype-preservation-via-sex. Consequently, the concept of system-information is smoothly integrated into the two-phased evolutionary model that paleontology requires (Eldredge and Gould, 1972). Finally, research on genome-level causation of evolution, which does not fit the MS, is summarized. The availability of alternative concepts further illustrates that it is time to depart from the MS.

Living systems are smarter bots: Slime mold semiosis versus AI symbol manipulation

Although machines may be good at mimicking, they are not currently able, as organisms are, to act creatively. We offer an understanding of the emergent qualities of biological sign processing in terms of generalization, association, and encryption. We use slime mold as a model of minimal cognition and compare it to deep-learning video game bots, which some claim have evolved beyond their merely quantitative algorithms. We find that these discrete Turing machine bots are not able to make productive, yet unanticipated, "errors"-necessary for biological learning-which, based on the physicality of signs, their relatively similar shapes, and relative physical positions spatially and temporally, lead to emergent effects and make learning and evolution possible. In organisms, stochastic resonance at the local level can be leveraged for self-organization at the global level. We contrast all this to the symbolic processing of today's machine learning, whereby each logic node and memory state is discrete. Computer codes are produced by external operators, whereas biological symbols are evolved through an internal encryption process.