The authors of the new booklet argue that although radical change is needed, it faces key obstacles. Much of human society is locked into a high-consumption culture, energy-intensive infrastructure, unequal power relations, and an economic system dominated by finance that fails the poorest and takes infinite growth for granted.
Other barriers are more in people’s mindsets and attitudes towards change. Opponents of radical change argue that it is impossible because of powerful incumbent interests, high costs, the lack of a detailed blueprint, or the unwillingness of governments or citizens to act. Others pin their hopes on a smart, technological fix to environmental problems.
Despite these barriers, there are examples of change that might give us hope. History is full of examples of rapid transition in the face of new challenges. Society shows a brilliantly adaptive ability to change and still meet its needs.
via How Did We Do That? The Possibility of Rapid Transition | P2P Foundation
The extreme weather events of the summer of 2018 are not just symptoms of climate breakdown. They are early stage warnings of a protracted process of civilisational collapse as industrial societies face some of the opening symptoms of having already breached the limits of a safe climate. These events are a taste of things to come on a business-as-usual trajectory. They elicit a sense of how industrial civilisational systems are vulnerable to collapse due to escalating climate impacts. And they highlight the urgent necessity of communities everywhere undertaking steps to achieve a systemic civilisational transition toward post-capitalist systems which can survive and prosper after fossil fuels.
via Global heatwave is symptom of early stage cycle of civilisational collapse
The term Artificial Intelligence is often cited in popular press as well as in art and philosophy circles as an alchemic talisman whose functioning is rarely explained. The hegemonic paradigm to date (also crucial to the automation of labor) is not based on GOFAI (Good Old-Fashioned Artificial Intelligence that never succeeded at automating symbolic deduction), but on the neural networks designed by Frank Rosenblatt back in 1958 to automate statistical induction. The text highlights the role of logic gates in the distributed architecture of neural networks, in which a generalized control loop affects each node of computation to perform pattern recognition. In this distributed and adaptive architecture of logic gates, rather than applying logic to information top-down, information turns into logic, that is, a representation of the world becomes a new function in the same world description. This basic formulation is suggested as a more accurate definition of learning to challenge the idealistic definition of (artificial) intelligence. If pattern recognition via statistical induction is the most accurate descriptor of what is popularly termed Artificial Intelligence, the distorting effects of statistical induction on collective perception, intelligence and governance (over-fitting, apophenia, algorithmic bias, “deep dreaming,” etc.) are yet to be fully understood.
More in general, this text advances the hypothesis that new machines enrich and destabilize the mathematical and logical categories that helped to design them. Any machine is always a machine of cognition, a product of the human intellect and unruly component of the gears of extended cognition. Thanks to machines, the human intellect crosses new landscapes of logic in a materialistic way—that is, under the influence of historical artifacts rather than Idealism. As, for instance, the thermal engine prompted the science of thermodynamics (rather than the other way around), computing machines can be expected to cast a new light on the philosophy of the mind and logic itself. When Alan Turing came up with the idea of a universal computing machine, he aimed at the simplest machination to calculate all possible functions. The efficiency of the universal computer catalyzed in Turing the alchemic project for the automation of human intelligence. However, it would be a sweet paradox to see the Turing machine that was born as Gedankenexperiment to demonstrate the incompleteness of mathematics aspiring to describe an exhaustive paradigm of intelligence (as the Turing test is often understood).
via Machines that Morph Logic: Neural Networks and the Distorted Automation of Intelligence as Statistical Inference — Glass Bead
We are already in the midst of a mass extinction event. The regularity with which new or threatened extinctions are announced – from the white rhinoceros to the lemur – is staggering. The background rate of extinction is 150-200 species a day. This is “biological annihilation”. Mass extinction is not new, but its speed is. The last such event took around 60,000 years. Now it’s happening in real time.
And there are accelerators built in to this crisis. The Arctic is already gone. By 2040, the ice will have melted for good. That entails the loss of species, not least of the polar bear. But it also means less solar radiation deflected, further warming the planet.
This is one reason why the crisis is far worse than we think. Paleoclimatologists have shown that past warming episodes show that there are mechanisms which magnify its effects, not represented in current climate models from the Intergovernmental Panel on Climate Change to the Paris Accords. The agreed “carbon budget”, even if anyone was adhering to it, will not keep temperatures within two degrees of the pre-industrial average.
via The apocalyptic tone of heatwave-reporting doesn’t go far enough – not when the issue is human extinction | The Independent