Month: January 2021

Self-Organizing Intelligent Matter: A blueprint for an AI generating algorithm

Karol Gregor, Frederic Besse

We propose an artificial life framework aimed at facilitating the emergence of intelligent organisms. In this framework there is no explicit notion of an agent: instead there is an environment made of atomic elements. These elements contain neural operations and interact through exchanges of information and through physics-like rules contained in the environment. We discuss how an evolutionary process can lead to the emergence of different organisms made of many such atomic elements which can coexist and thrive in the environment. We discuss how this forms the basis of a general AI generating algorithm. We provide a simplified implementation of such system and discuss what advances need to be made to scale it up further.

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Evolving an Ecological Perspective


In addressing the challenges facing humanity, much can be learned from the evolution of ecological systems. Natural selection has led to mechanisms that confer robustness, or else organisms would not survive to reproduce. At the system level, tight interdependencies have similarly been selected; but the process of transformational evolution, as elucidated by Richard Lewontin (1977), or what Tim Lenton and collaborators (2018) have termed sequential selection, can serve as a filter that ultimately produces more robust systems.

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Association between population distribution and urban GDP scaling

Ribeiro HV, Oehlers M, Moreno-Monroy AI, Kropp JP, Rybski D (2021) Association between population distribution and urban GDP scaling. PLoS ONE 16(1): e0245771.

Urban scaling and Zipf’s law are two fundamental paradigms for the science of cities. These laws have mostly been investigated independently and are often perceived as disassociated matters. Here we present a large scale investigation about the connection between these two laws using population and GDP data from almost five thousand consistently-defined cities in 96 countries. We empirically demonstrate that both laws are tied to each other and derive an expression relating the urban scaling and Zipf exponents. This expression captures the average tendency of the empirical relation between both exponents, and simulations yield very similar results to the real data after accounting for random variations. We find that while the vast majority of countries exhibit increasing returns to scale of urban GDP, this effect is less pronounced in countries with fewer small cities and more metropolises (small Zipf exponent) than in countries with a more uneven number of small and large cities (large Zipf exponent). Our research puts forward the idea that urban scaling does not solely emerge from intra-city processes, as population distribution and scaling of urban GDP are correlated to each other.

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CSS2020 full videos

62 videos total are included:

* Plenary talks are recordings as individual  videos (8).
* Lightning talks are recordings of the 2 separate days (2).
* Invited and contributed talks are 6 parallel, x 2 per day, x 4 days (48).
* Special sessions (4).
You can use the search feature to look for an author by name, keyword in the title of the presentation, etc. These are all listed at the bottom of each video.

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Effective connectivity determines the critical dynamics of biochemical networks

Santosh Manicka, Manuel Marques-Pita, Luis M. Rocha

Living systems operate in a critical dynamical regime — between order and chaos — where they are both resilient to perturbation, and flexible enough to evolve. To characterize such critical dynamics, the established ‘structural theory’ of criticality uses automata network connectivity and node bias (to be on or off) as tuning parameters. This parsimony in the number of parameters needed sometimes leads to uncertain predictions about the dynamical regime of both random and systems biology models of biochemical regulation. We derive a more accurate theory of criticality by accounting for canalization, the existence of redundancy that buffers automata response to inputs — a known mechanism that buffers the expression of traits, keeping them close to optimal states despite genetic and environmental perturbations. The new ‘canalization theory’ of criticality is based on a measure of effective connectivity. It contributes to resolving the problem of finding precise ways to design or control network models of biochemical regulation for desired dynamical behavior. Our analyses reveal that effective connectivity significantly improves the prediction of critical behavior in random automata network ensembles. We also show that the average effective connectivity of a large battery of systems biology models is much lower than the connectivity of their original interaction structure. This suggests that canalization has been selected to dynamically reduce and homogenize the seemingly heterogeneous connectivity of biochemical networks.

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