[spectre] New 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.

[Radio Web MACBA]

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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