EightOS is a practice of finding a certain fluid state in the body, which is resilient, has integrity, and is adaptive at the same time.
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This fluid state can then have certain types of dynamics, which can be measured through feeding several different datapoints tracking both the movement and the various body parameters
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The ideas are always breathing. They expand and they contract all the time.
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When the ideas expand they disperse, they they contract, they focus. The intention of EightOS as a practice is to establish a habit - dynamics that allows for all these different states to emerge one after the other. And to make the body not be stuck in only one of them.
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The machine learning algorithm proposed is learning from the body how this dynamics can be instilled.
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So it is tracking all the datapoint and learning about the correlations. Then we study the results, find some possible "ideal" state (if it exists) and then try to create a feedback mechanism model that could tell any system (not only the body) when it is deviating from that logic.
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How can a machine be actually trained by the body. How can a body be trained by the machine. Software.
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The notion of fluid and confluent entity is probably related more to the actual reading that we can get at every point of time. How does that translate into datapoints also.
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Fluid both in terms of the long-term dynamics (not getting stuck) and also in terms of the short-term sensitivity (being a substance, a medium)
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A model that is learning when is the time to contact, when is the time to expand, when is the time to flow.
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And also how the dynamics occurs over time, what are the fluctuation periods.
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+ ⤓ ?The topics are the nodes (words) that tend to co-occur together in the same context (next to each other).
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We use a combination of clustering and graph community detection algorithm (Blondel et al based on Louvain) to identify the groups of nodes are more densely connected together than with the rest of the network. They are aligned closer to each other on the graph and are given a distinct color.
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The most influential nodes are either the ones with the highest betweenness centrality — appearing most often on the shortest path between any two randomly chosen nodes (i.e. linking the different distinct communities) — or the ones with the highest degree.
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Structural Gap
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?A structural gap shows the two distinct communities (clusters of words) in this graph that are important, but not yet connected. That's where the new potential and innovative ideas may reside.
This measure is based on a combination of the graph's connectivity and community structure, selecting the groups of nodes that would either make the graph more connected if it's too dispersed or that would help maintain diversity if it's too connected.
This measure is based on a combination of the graph's connectivity and community structure, selecting the groups of nodes that would either make the graph more connected if it's too dispersed or that would help maintain diversity if it's too connected.
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?These are the latent brokers between the topics: the nodes that have an unusually high rate of influence (betweenness centrality) to their freqency — meaning they may appear not as often as the most influential nodes but they are important narrative shifting points.
These are usually brokers between different clusters / communities of nodes, playing not easily noticed and yet important role in this network, like the "grey cardinals" of sorts.
These are usually brokers between different clusters / communities of nodes, playing not easily noticed and yet important role in this network, like the "grey cardinals" of sorts.
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