Complexity science has always been ambivalent about the possibility to foretell the future. On one side, one of its leitmotivs has aimed at highlighting the intrinsic uncertainty and unpredictability of ‘multi-scale,’ ‘non-linear’ and ‘evolutionary’ systems, because of their sensitivity to initial conditions. On the other side, some complex systems specialists tackle the future almost daily but show different attitudes toward it. For example, some scholars find easier to foretell the long run evolution of a system, but only in a coarse-grained (...) manner, while others propose to follow and predict the agents of a modeled system almost individually, thanks to their fine-grained computer simulations. In this article, five anticipation communities within complexity science are described by studying their research, their normative views, their extra-academic activities, and their anticipation activity. To expound these groups in a coherent fashion, I will use and readapt for my study the concept of ‘anticipation communities’ introduced by French sociologist Céline Granjou. The five groups described here are named as follows: the ‘high-frequency prediction,’ the ‘open-ended evolution,’ the ‘decentralized prediction,’ the ‘future co-construction. (shrink)

The environmental costs and energy constraints have become emerging issues for the future development of Machine Learning (ML) and Artificial Intelligence (AI). So far, the discussion on environmental impacts of ML/AI lacks a perspective reaching beyond quantitative measurements of the energy-related research costs. Building on the foundations laid down by Schwartz et al., 2019 in the GreenAI initiative, our argument considers two interlinked phenomena, the gratuitous generalisation capability and the future where ML/AI performs the majority of quantifiable inductive inferences. The (...) gratuitous generalisation capability refers to a discrepancy between the cognitive demands of a task to be accomplished and the performance (accuracy) of a used ML/AI model. If the latter exceeds the former because the model was optimised to achieve the best possible accuracy, it becomes inefficient and its operation harmful to the environment. The future dominated by the non-anthropic induction describes a use of ML/AI so all-pervasive that most of the inductive inferences become furnished by ML/AI generalisations. The paper argues that the present debate deserves an expansion connecting the environmental costs of research and ineffective ML/AI uses (the issue of gratuitous generalisation capability) with the (near) future marked by the all-pervasive Human-Artificial Intelligence Nexus. (shrink)

As we look beyond our terrestrial boundary to a multi-planetary future for humankind, it becomes paramount to anticipate the challenges of various human factors on the most likely scenario for this future: permanent human settlement of Mars. Even if technical hurdles are circumvented to provide adequate resources for basic physiological and psychological needs, Homo sapiens will not survive on an alien planet if a dysfunctional psyche prohibits the utilization of these resources. No matter how far we soar into the stars, (...) our psychologies for future generations will be forever tethered to the totality of our surroundings. By shaping our environment toward survival and welfare during the voyage to Mars and in a Martian colony, we indirectly shape our psyches and prepare them for a mission of unprecedented alienation and duration. Once on Mars, human factors such as leadership structure, social organization and code of conduct, group size, gender balance, developmental cycle, mobility, length of stay and the ecological settings and type and manner of subsistence, will create a novel Martian culture. The degree that settlers are severed from the Earth will affect how radically foreign this culture will be when compared with cultures on Earth. (shrink)