New podcast: Ramon Amaro <https://rwm.macba.cat/en/sonia/ramon_amaro-tag/capsula> introduces the basics of machine learning, its criteria for assigning value, the collision between blackness and the artificial, its flaws, and the problem of impunity that all too often accompanies them.
Link: https://rwm.macba.cat/en/sonia/ramon_amaro-tag/capsula *In collaboration with Sonic Acts. Music by NYZ, taken from 'CCD'.* Ramon Amaro <https://rwm.macba.cat/en/sonia/ramon_amaro-tag/capsula>is a lecturer in the Department of Visual Cultures at Goldsmiths, London, and also in the Centre for Research Architecture. His work revolves around speculative articulations in machine learning, philosophies of being, mathematics, engineering, and black ontology. In his essay "As If", Amaro takes a critical, provocative look at computer vision and machine perception. "By prioritising cohesion," he says, "algorithmic processes erode the potential for human difference and self-actualization." Given the mirroring of the social world in the realm of the artificial, which mimics its logics and systemic violence (racism, exclusion), Amaro rejects efforts to correct machine perception by including himself in the system. "To merely include a representational object into a computational milieu that has already positioned the white object as the prototypical characteristic catalyzes disruption on the level of superficiality," he writes in a text published by e-flux. Instead, Amaro advocates the many possibilities opened by what he calls the black technical object and machinic non-existence: "a self that is continually taking shape, as blackness has always done, in its exploration of infinite halls of possibility." In this podcast, Ramon Amaro introduces the basics of machine learning, its criteria for assigning value, the collision between blackness and the artificial, its flaws, and the problem of impunity that all too often accompanies them. He also calls for a techno-resistance that would require us to sacrifice our current view of the world and of ourselves. *Timeline* *03:14* The basics of machine learning *05:41* Structured and unstructured data in machine learning. The bjectives of machine learning are heavily dependent upon context. *10:50* Machine learning is a language in itself *15:15* We are in coparticipation with machine learning *21:57* An artificial system which is mirroring a violence *26:19* Imagine if we let it completely loose... *37:13* The violences of machine learning *41:18* Accelerating the loss of due process *44:22* Technoresistance *51:27* Blackness is that which can not be described or contained *56:32* Living through the duress of the moment *TECHNO-RESIST!*
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