Abstract
Darwin’s insight that species are mutable, and descent, and origin by means of natural selection is one of the most widely acknowledged strategies for the origin of species and their survival in nature. In his famous contribution, however, Darwin also writes that he is convinced that “... Natural Selection has been the main but not exclusive means of modification” (Darwin in The origin of species. Oxford Univeristy Press, Oxford, p. 7, 1996). This research suggests robustness as another fundamental strategy for survival in nature. The paper does not contradict the popular view, which usually sees robustness as a feature making systems fault-tolerant, thereby focusing on the identification of strategies and techniques for making systems robust (i.e., how to achieve robustness). The paper rather extends this view with an interpretation resting on the question—WHY is robustness omnipresent in the world around us? From this point of view, robustness is interpreted as a fundamental mechanism that is in place because of another fundamental feature in nature—the design and use of sub-optimal systems. The paper argues that, in a sense, nature under-specifies systems but compensates for this by providing systems with various degrees of robustness. We believe that this interpretation may lead to fundamentally new design approaches and insights in several fields.
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Notes
The paper explores a range of topics, and the feeling resides that much more could, and perhaps should, be said about several of these topics, and that several other topics may have deserved a mentioning here, too. For the sake of brevity, however, most sections concentrate on a few selected papers only considered relevant to this work. Apologies therefore to those readers whose favorite topics are not included in this text.
A common feature in this text is going to be the use of concepts of which no universally agreed definition exists (e.g., intelligence, mind). Whenever required, the paper attempts to provide an adequate interpretation for any of these concepts.
Forthcoming sections are going to discuss the term “reign” in more detail. The terms brain and mind are also going to be subjected to some scrutiny.
It is important to note that even among experts there is no consensus for a generally acknowledged definition for the term “gene”. Although a general view is that a gene is a unit of inheritance that usually is directly responsible for one trait or character, other long-standing views such as the “one-gene, one-protein” rule—see later in this section—are currently changing (e.g., Parra et al. 2006). There is however large-scale projects to provide a controlled vocabulary to describe gene and gene product attributes in any organism (e.g., The Gene Ontology Project spearheaded by the Gene Ontology Consortium, http://www.geneontology.org/).
The timing of switch activation is a fundamental challenge in the field. A full treatment of this important problem may be beyond the scope of this work, but it is interesting to know that the problem reappears in “Biological Neural Networks” when synchronization in the brain raises similar questions. It is also interesting to mention that although Carroll’s work (Carroll et al. 2008) reports more on an individual gene level, the phenomenon of an orchestrated organization of hundreds of proteins and genes also occurs on higher levels. Santos and Ferrell (2008), for instance, report on the complex dynamic of molecular signalling in a cell cycle (i.e., the production of two cells from one single cell).
Intelligence can be an emotional topic. Although, arguably, many animals are intelligent to some degree, by intelligence this text assumes the same scope of intelligence as can be seen in general purpose human action.
It is clear that there are (and rightly so) many passionate views (e.g., physical symbol system hypothesis, Searle’s Chinese room, the symbol grounding problem, or situatedness) in the camps of philosophy, strong, weak, nouvelle AI, etc. This paper deeply appreciates these discussions but also has a somewhat relaxed attitude by viewing AI as a field of science with a multiple agenda where a rich body of knowledge and experience has been acquired over the years. The paper also takes into account that modern research projects are often large-scale, inherently inter-disciplinary, collaborative, and global. If any such project eventually attempts the creation of a mind—or diverts into this goal from a different outset—then it would be foolish not to draw on the rich experience acquired by AI. Undertakings where this could happen could be the Blue Brain Project; other candidates may be projects related to brain-computer interfaces, artificial synthetic life, or the intelligent Web.
See for example, http://www.world.honda.com/ASIMO/.
The rather casual use of the terms brain and mind in this text should not be misleading. There are of course several fundamental open questions related to both notions. Understanding whether the mind already exists in newborn human beings (and if so, in which state) or whether a mind emerges or evolves over time, is important. The exact relationship between the brain and the mind carries a similar weight (e.g., is the brain or does the brain act as a facilitator for the mind, and if so, in which way does this facilitation happen?). The exact location of the mind is not clear either as the long-lasting philosophical debate on the body and mind conundrum demonstrates (e.g., see Feser 2006). This text does not postulate a particular position, most of the features of robustness discussed in this section relate to tangible biological phenomena, but it is necessary to emphasize the wider scope of issues involved when robustness is discussed in situations involving a degree of uncertainty or lack of general understanding.
Various human brain projects including the Blue Brain Project, for instance, illustrate the magnitude of the challenge well. Inaugurated in July 2005, the Blue Brain Project aims for the creation of a biologically accurate, functional model of the brain using IBM’s Blue Gene supercomputer. The vision of the project parallels that of the Human Genome Project and it is envisaged that the Blue Brain Project will provide an equally massive step in the understanding of the brain as did the Human Genome Project for the understanding of the genomic makeup of human beings (e.g., see http://www.bluebrain.epfl.ch/).
It is necessary to understand that the discussion in this section includes several unknowns. The universe itself holds several open questions (e.g., is it open or closed?; how old is it?; is it a computer and if so what does it compute?; is the standard model of particle physics correct?; etc.). For the brain, a similar discussion has to be equally cautious (e.g., there is quite some uncertainty around the information capacity and the computational power of the brain; is it a Turing machine?; is it a quantum computer?; etc.).
The scope of this paper is quite wide in general. It is, however, not possible to discuss all topics and terms in great detail and the reader may apologize if some terms (e.g., understanding, creation, or recognition) are not discussed or explained in great detail in this paper.
The system boundary problem presents itself again. Over time, this problem has appeared in several forms in science. The so-called “Laplace demon”, for instance, contemplates that if it where possible to know precisely the exact location and momentum of every atom in the universe at one point in time, then it is possible to describe the universe in its entirety, i.e., its past, present, and future, by the laws of Newton. Chaos theory, pioneered by the recently deceased Edward N. Lorenz, proposes an alternative view to this demonic, predetermined world. This exciting theory examines the world of sensitive nonlinear dynamic systems where the butterfly effect and other effects create the strange fractal beauty of nature. Neither is the boundary problem alien to AI, where it came to be known, rather quickly, as the “frame” problem, which nowadays may translate to scalability and robustness. In this text, if humans are responsible for global warming, and if the propagation of an organism in Fig. 4 has some dependency on the world climate, then it is not possible to speak about “pure” natural selection. It may be possible to use the somewhat weaker expression “assumed unintentional human intervention”. Although one problem involves to specify, exactly, the degree of inclusion of humans in nature, the question of what nature actually is, also appears to be problematic (e.g., what is the boundary of nature?; is the system nature equivalent to universe?; if yes, is the system nature open or closed, if no, how can its boundary be defined?). On the surface, the issues just mentioned appear to be similar perhaps to the body/brain and mind conundrum mentioned earlier, and so may be the subject of philosophical debate elsewhere.
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Schuster, A., Yamaguchi, Y. The Survival of the Fittest and the Reign of the Most Robust: In Biology and Elsewhere. Minds & Machines 19, 361–389 (2009). https://doi.org/10.1007/s11023-009-9156-4
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DOI: https://doi.org/10.1007/s11023-009-9156-4