Berggruen Institute Launches Chinese-language Magazine Cui Ling

 

New publication creates a reflective space between traditional academic writing and fragmented online content 

The Berggruen Institute has begun publication of Cui Ling(萃嶺), a new Chinese-language sibling magazine to Noema. The release of Cui Ling’s inaugural print edition coincides with the launch of an online content publishing platform.

“Cui Ling” means “green hill with lush grass and trees” in Chinese, which complements the English translation of the German “Berggruen.” The magazine is published by the Berggruen Institute and curated, edited, and presented by the Berggruen Institute China Center.

The publication will focus on global events, issues, and reflections on new ideas in an era of change. Bringing together innovative ideas from East and West, it will publish interdisciplinary articles, interviews, art, and literature on philosophy, ethics, new technologies, geo-civilization, ecological crisis, science fiction literature, contemporary art, and more.

The work will be based on the Berggruen Institute China Center’s extensive academic foundation, wide-ranging expertise in the humanities, and expansive perspective on the intellectual frontier. The magazine’s aim is to create a space for deep reading and thinking in the gap between traditional academic research and contemporary digital, high-quality content.

“Planetary Wisdom” is the theme of the inaugural print issue of Cui Ling, focused on planetary thinking and philosophy that has been a focus of the Berggruen Institute’s work over the past year.

Many of the formulations, theoretical bases, and ideas of this interdisciplinary effort to promote a shift in human conceptions are still in their early stages of exploration. The publication will further the organization’s mission to discover, cultivate, refine, and disseminate new ideas and thinking, publishing preliminary results and stimulating more and deeper discussion.

Cui Ling is published online throughout the year, with intermittent print editions

Please click here for more information about Cui Ling.

 

composed by Arswain
machine learning consultation by Anna Tskhovrebov
commissioned by the Berggruen Institute
premiered at the Bradbury Building
downtown Los Angeles
april 22, 2022

Human perception of what sounds “beautiful” is necessarily biased and exclusive. If we are to truly expand our hearing apparatus, and thus our notion of beauty, we must not only shed preconceived sonic associations but also invite creative participation from beings non-human and non-living. We must also begin to cede creative control away from ourselves and toward such beings by encouraging them to exercise their own standards of beauty and collaborate with each other.

Movement I: Alarm Call
‘Alarm Call’ is a long-form composition and sound collage that juxtaposes, combines, and manipulates alarm calls from various human, non-human, and non-living beings. Evolutionary biologists understand the alarm call to be an altruistic behavior between species, who, by warning others of danger, place themselves by instinct in a broader system of belonging. The piece poses the question: how might we hear better to broaden and enhance our sense of belonging in the universe? Might we behave more altruistically if we better heed the calls of – and call out to – non-human beings?

Using granular synthesis, biofeedback, and algorithmic modulation, I fold the human alarm call – the siren – into non-human alarm calls, generating novel “inter-being” sonic collaborations with increasing sophistication and complexity. 

Movement II: A.I.-Truism
A synthesizer piece co-written with an AI in the style of Vangelis’s Blade Runner score, to pay homage to the space of the Bradbury Building.

Movement III: Alarmism
A machine learning model “learns” A.I.Truism and recreates Alarm Call, generating an original fusion of the two.

Movement IV: A.I. Call
A machine learning model “learns” Alarm Call and recreates A.I.Truism, generating an original fusion of the two.

RAVE (IRCAM 2021) https://github.com/acids-ircam/RAVE