syn·site
1: AN ENTANGLED, NON-SINGULAR SITE. A site of simultaneity spanning disparate sites, marked by plurality, potentiality, and simultaneity.
2: ASSEMBLED BITS WITHIN THE ENTANGLED SITE. An ever-cleaving assemblage of bits within the aforementioned entangled site.
3: SYNTAX AS SYN-SITE. A verb-noun concatenation—process embedded in, affecting, and affected by site—with an assertive threshold calling attention to itself between the two. To understand this hybridity of site is to see the hyphenated construct itself as a tool—to see in its syllabic nodes and articulated connection a self-aware mirage.
< ORIGIN > The SITE/NON-SITE construct of Robert Smithson, sublated.
1: AN ENTANGLED, NON-SINGULAR SITE. A site of simultaneity spanning disparate sites, marked by plurality, potentiality, and simultaneity.
2: ASSEMBLED BITS WITHIN THE ENTANGLED SITE. An ever-cleaving assemblage of bits within the aforementioned entangled site.
3: SYNTAX AS SYN-SITE. A verb-noun concatenation—process embedded in, affecting, and affected by site—with an assertive threshold calling attention to itself between the two. To understand this hybridity of site is to see the hyphenated construct itself as a tool—to see in its syllabic nodes and articulated connection a self-aware mirage.
< ORIGIN > The SITE/NON-SITE construct of Robert Smithson, sublated.
SYN (along with, at the same time | from Greek SYN, with | ~SYNTHETIC) + SITE (N: point of event, occupied space, internet address; V: to place in position | from Latin SITUS, location, idleness, forgetfulness | ~WEBSITE ¬cite ¬sight), cf. SITE/NON-SITE (from Robert Smithson, A PROVISIONAL THEORY OF NONSITES, 1968)
Manifolds are the fundamental surfaces that data is found on. Once you have a manifold to describe your data, you can make predictions about the remaining space.
Manifolds are the fundamental surfaces that data is found on. Once you have a manifold to describe your data, you can make predictions about the remaining space.
Manifolds are the fundamental surfaces that data is found on. Once you have a manifold to describe your data, you can make predictions about the remaining space.
