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machine:machine learning
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Your Project Notes
Interpret graph data, save ideas, AI content, and analytics reports. Auto-Generate from Analytics
Top keywords (global influence):
Top topics (local contexts):
Explore the main topics and terms outlined above or see them in the excerpts from this text below.
See the relevant data in context: click here to show the excerpts from this text that contain these topics below.
Tip: use the form below to save the most relevant keywords for this search query. Or start writing your content and see how it relates to the existing search queries and results.
Tip: here are the keyword queries that people search for but don't actually find in the search results.

Magic and Happiness by Giorgio Agamben

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Walter Benjamin once said that a child’s first experience of the world is not his realization that "adults are stronger but rather that he cannot make magic,” The statement was made under the influence of a twenty-milligram dose of mescaline, but that does not make it any less salient. It is, in fact, quite likely that the invincible sadness that sometimes overwhelms children is born precisely of their awareness that they are incapable of magic. What ever we can achieve through merit and effort, cannot make us truly happy. Only magic can do that. This did not escape the childlike genius of Mozart, who clearly indicated the secret solidarity between magic and happiness in a letter to Jopseph Bullinger: "To live respectably and to live happily are two very different things, the latter will not be possible for me without some kind of magic; for this something truly supernatural would have to happen.”

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Like creatures in fables, children know that in order to be happy it is necessary to keep the genie in the bottle at one’s side, and have the donkey that craps gold coins or the hen that lays golden eggs in one’s house. And no matter what the situation, it is much more important to know the exact place and the right words to say than to take the trouble to reach a goal by honest means. Magic means that precisely no one can be worthy of happiness and that, as the ancients knew, any happiness commensurate with man is always hubris; it is always the result of arrogance and excess. But if someone succeeds in influencing fortune through trickery, if happiness depends not on what one is but on a magic walnut or an "Open sesame!” — then and only then can one consider oneself to be truly and blessedly happy.

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This childlike wisdom affirms that happiness is not something that can be deserved, has always been met with the objections of official morality. Take the words of Kant, the philosopher who was least capable of capable of understanding the difference between living with dignity and living happily: "That in you which strives towards happiness is inclination, that which then limits this inclination to the condition of your first being worthy of happiness is your reason.” But we (or the child within us) wouldn’t know what to do with happiness of which we were worthy. What a disaster if a woman loved you because you deserved it! And how boring to receive happiness as the reward of work well done.

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That the bond linking magic and happiness is not simply immoral, that it can indeed testify to a higher ethics, is shown in the ancient maxim that whoever realizes he is happy has already ceased to be so. This means that happiness has a paradoxical relationship with its subject. Someone who is happy cannot know that he is; the subject of happiness is not a subject per se and does not obtain the form of consciousness or of a conscience, not even a good one. Here magic appears as an exception, the only one that allows someone to be happy and to know that he is. Whoever enjoys something through en- enchantment escapes from the hubris implicit in the consciousness of happiness, since, in a certain sense, the happiness that he knows he possesses is not his. Thus when Zeus assumes the likeness of Amphitryon and unites with beautiful Alcmene, he does not enjoy her as Zeus, nor even despite his appearances, as Amphitryon. His enjoyment lies entirely in enchantment, and only what has been

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obtained through the crooked paths of magic can be enjoyed consciously and purely. Only someone who is enchanted can say "I” with a smile, and the only happiness that is truly deserved is the one we could never dream of deserving.

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That is the ultimate reason for the precept that there is only one way to achieve happiness on this earth: to believe in the divine and not to aspire to reach it (there is an ironic variation of this in a conversation between Franz Kafka and Gustav Janouch, when Kafka affirms that there is plenty of hope— but not for us). This apparently ascetic thesis becomes intelligible only if we understand the meaning of this "not for us.” It means not that happiness is reserved only for others (happiness is precisely, for us) but that it awaits only at the point where it was not destined for us. That is: happiness can be ours only though magic. At that point, when we have wrenched it away from fate, happiness coincides entirely with our knowing ourselves to be capable of magic, with the gesture we use to banish that childhood sadness once and for all.

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If this is so, there is no other happiness than feeling capable of magic, then Kafka’s enigmatic definition of magic becomes clear. He writes that if we call life by its right name, it comes forth, because "that is the essence of magic, which does not create but summons.” This definition agrees with the ancient tradition scrupulously followed by kabbalists and necromancers, according to which magic is essentially a science of secret names. Each thing, each being, has in addition to its manifest name, another, hidden name to which it cannot fail to respond. To be a magus means to know and evoke these archi-names. Hence the interminable discussions of names (diabolical or angelic) through which the necromancer ensures his mastery over spiritual powers. For him, the secret name is the only seal of his power of life and death over the creature that bears it.

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But according to another, more luminous tradition, the secret name is not so much a cipher of the thing’s subservience to the magnus’s speech as, rather, the monogram that sanctions its liberation from language. The secret name was a name by which the creature was called in Eden. When it is pronounced, every manifest name—the entire Babel of names—is shattered. That is why, according to the doctrine, magic is a call to happiness. The secret name is a gesture that restores the creature to the unexpressed. In the final instance, magic is not a knowledge of names but a gesture, a breaking free from the name. That is why a child is never more content than when he invents a secret language. His sadness comes less from ignorance of magic names than from his own ability to free himself from the name that has been imposed on him. No sooner does he succeed, no sooner does he invent a new name, than he holds in his hands thelaissez-passer that grants him happiness. To have a name is to be

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guilty. And justice, like magic, is nameless. Happy, and without a name, the creature knocks at the gates of the land of the magi, who speaks in gestures alone.

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semantic variability:
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Semantic Variability Score
— modulates diversity of the discourse network  how it works?
The score is calculated based on how modular the structure of the graph is (> 0.4 means the clusters are distinct and separate from one another = multiple perspectives). It also takes into account how the most influential nodes are dispersed among those clusters (higher % = lower concentration of power in a particular cluster).
Actionable Insight:


We distinguish 4 states of variability in your discourse. We recommend that a well-formed discourse should go through every stage during its evolution (in several iterations).

  1 - (bottom left quadrant) — biased — low variability, low diversity, one central idea (genesis and introduction stage).
  2 - (top right) - focused - medium variability and diversity, several concepts form a cluster (coherent communication stage).
  3 - (bottom right) - diversified — there are several distinct clusters of main ideas present in text, which interact on the global level but maintain specificity (optimization and reflection stage).
  4 - (left top) — dispersed — very high variability — there are disjointed bits and pieces of unrelated ideas, which can be used to construct new ideas (creative reformulation stage).

Read more in the cognitive variability help article.
Generate AI Suggestions
Your Workflow Variability:
Shows to what extent you explored all the different states of the graph, from uniform and regular to fractal and complex. Read more in the cognitive variability help article.

You can increase the score by adding content into the graph (your own and AI-generated), as well as removing the nodes from the graph to reveal latent topics and hidden patterns.
Phases to Explore:
AI Suggestions  
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Main Topical Clusters & High-Level Ideas
The topical clusters are comprised of the nodes (words) that tend to co-occur together in the same context (next to each other).

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 using the Force Atlas algorithm (Jacomy et al) and are given a distinct color.
please, add your data to display the stats...
+     full table     AI: Reveal High-Level Ideas

AI: Summarize Topics   AI: Explore Selected

Most Influential Keywords & Concepts
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.

We use the Jenks elbow cutoff algorithm to select the top prominent nodes that have significantly higher influence than the rest.

Click the Reveal Underlying Ideas button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.
please, add your data to display the stats...
+      ↻    Reveal Underlying Ideas

AI: Summarize Key Statements   AI: Topical Outline
Network Structure:
The network structure indicates the level of its diversity. It is based on the modularity measure (>0.4 for medium, >0.65 for high modularity, measured with Louvain (Blondel et al 2008) community detection algorithm) in combination with the measure of influence distribution (the entropy of the top nodes' distribution among the top clusters), as well as the the percentage of nodes in the top community.

Download: TXT Report  CSV Report  More Options
Structural Gap Insight
(topics to connect)   ?
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.
Highlight in Network   ↻ Show Another Gap  
AI: Insight Question   AI: Bridge the Gap  
Discourse Entrance Points
(concepts with the highest influence / frequency ratio)   ?
These nodes have unusually high rate of influence (betweenness centrality) to their frequency — meaning they may appear not as often as the most influential nodes but they are important narrative shifting points.

These are usually effective entrance points into the discourse, as they link different topics together and have high inlfuence, but not too many connections, which makes them more accessible.
↻ Undo Selection AI: Select & Generate Content

Emerging Keywords

Evolution of Topics
(number of occurrences per text segment) ?
The chart shows how the main topics and the most influential keywords evolved over time. X-axis: time period (split into 10% blocks). Y-axis: cumulative number of occurrences.

Drag the slider to see how the narrative evolved over time. Select the checkbox to recalculate the metrics at every step (slower, but more precise).

Main Topics
(according to Latent Dirichlet Allocation):

LDA stands for Latent Dirichlet Allocation — it is a topic modelling algorithm based on calculating the maximum probability of the terms' co-occurrence in a particular text or a corpus.

We provide this data for you to be able to estimate the precision of the default InfraNodus topic modeling method based on text network analysis.
Most Influential Words
(main topics and words according to LDA):

We provide LDA stats for comparison purposes only. It works with English-language texts at the moment. More languages are coming soon, subscribe @noduslabs to be informed.

Sentiment Analysis

positive: | negative: | neutral:
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We analyze the sentiment of each statement to see whether it's positive, negative, or neutral. You can filter the statements by sentiment (clicking above) and see what kind of topics correlate with every mood.

The approach is based on AFINN and Emoji Sentiment Ranking

Use the Bert AI model for English, Dutch, German, French, Spanish and Italian to get more precise results (slower). Standard model is faster, works for English only, is less precise, and is based on a fixed AFINN dictionary.

Concept Relation Analysis:

please, select the node(s) on the graph or in the table below to see their connections...
+   ⤓ download CSV   ?

Use this feature to compare contextual word co-occurrences for a group of selected nodes in your discourse. Expand the list by clicking the + button to see all the nodes your selected nodes are connected to. The total influence score is based on betweenness centrality measure. The higher is the number, the more important are the connections in the context of the discourse.
Top Relations in 4-grams
(bidirectional, for directional bigrams see the CSV table below):

⤓ Download   ⤓ Directed Bigrams CSV   ?

The most prominent relations between the nodes that exist in this graph are shown above. We treat the graph as undirected by default. Occurrences shows the number of the times a relationship appears in a 4-gram window. Weight shows the weight of that relation.

As an option, you can also downloaded directed bigrams above, in case the direction of the relations is important (for any application other than language).

Text Statistics:
Word Count Unique Lemmas Characters Lemmas Density
Text Network Statistics:
Show Overlapping Nodes Only

⤓ Download as CSV  ⤓ Download an Excel File
Discourse Network Structure Insights
mind-viral immunity:
The higher is the network's structure diversity and the higher is the alpha in the influence propagation score, the higher is its mind-viral immunity — that is, such network will be more resilient and adaptive than a less diverse one.

In case of a discourse network, high mind-viral immunity means that the text proposes multiple points of view and propagates its influence using both highly influential concepts and smaller, secondary topics.

We recommend to try to increase mind-viral immunity for texts that have a low score and to decrease it for texts that have a high score. This ensures that your discourse will be open, but not dispersed.
The higher is the diversity, the more distinct communities (topics) there are in this network, the more likely it will be pluralist.
The network structure indicates the level of its diversity. It is based on the modularity measure (>0.4 for medium, >0.65 for high modularity, measured with Louvain (Blondel et al 2008) community detection algorithm) in combination with the measure of influence distribution (the entropy of the top nodes' distribution among the top clusters), as well as the the percentage of nodes in the top community.

We recommend to aim for Diversified structure if you're in the Biased or Focused score range and to aim for the Focused structure if you're in the Dispersed score range.

Influence Distribution
Topics Nodes in Top Topic Components Nodes in Top Comp
Nodes Av Degree Density Weighed Betweenness

Discourse Advice:
AI: Develop the Discourse
Narrative Influence Propagation:
The chart above shows how influence propagates through the network. X-axis: lemma to lemma step (narrative chronology). Y-axis: change of influence.

The more even and rhythmical this propagation is, the stronger is the central idea or agenda (see alpha exponent below ~ 0.5 or less).

The more variability can be seen in the propagation profile, the less is the reliance on the main concepts (agenda), the stronger is the role of secondary topical clusters in the narrative.
propagation dynamics: | alpha exponent: (based on Detrended Fluctuation Analysis of influence) ?   show the chart
We plot the narrative as a time series of influence (using the words' betweenness score). We then apply detrended fluctuation analysis to identify fractality of this time series, plotting the log2 scales (x) to the log2 of accumulated fluctuations (y). If the resulting loglog relation can be approximated on a linear polyfit, there may be a power-law relation in how the influence propagates in this narrative over time (e.g. most of the time non-influential words, occasionally words with a high influence).

Using the alpha exponent of the fit (which is closely related to Hurst exponent)), we can better understand the nature of this relation: uniform (pulsating | alpha <= 0.65), variable (stationary, has long-term correlations | 0.65 < alpha <= 0.85), fractal (adaptive | 0.85 < alpha < 1.15), and complex (non-stationary | alpha >= 1.15).

For maximal diversity, adaptivity, and plurality, the narrative should be close to "fractal" (near-critical state). For fiction, essays, and some forms of poetry — "uniform". Informative texts will often have "variable + stationary" score. The "complex" state is an indicator that the text is always shifting its state.

Degree Distribution:
  calculate & show   ?
(based on kolmogorov-smirnov test) ?   switch to linear
Using this information, you can identify whether the network has scale-free / small-world (long-tail power law distribution) or random (normal, bell-shaped distribution) network properties.

This may be important for understanding the level of resilience and the dynamics of propagation in this network. E.g. scale-free networks with long degree tails are more resilient against random attacks and will propagate information across the whole structure better.
If a power-law is identified, the nodes have preferential attachment (e.g. 20% of nodes tend to get 80% of connections), and the network may be scale-free, which may indicate that it's more resilient and adaptive. Absence of power law may indicate a more equalized distribution of influence.

Kolmogorov-Smirnov test compares the distribution above to the "ideal" power-law ones (^1, ^1.5, ^2) and looks for the best fit. If the value d is below the critical value cr it is a sign that the both distributions are similar.
Please, enter a search query to visualize the difference between what people search for (related queries) and what they actually find (search results):

We will build two graphs:
1) Google search results for your query;
2) Related searches for your query (Google's SERP);
Click the Missing Content tab to see the graph that shows the difference between what people search for and what they actually find, indicating the content you could create to fulfil this gap.
Please, enter a search query to discover what else people are searching for (from Google search or AdWords suggestions):

We will build a graph of the search phrases related to your query (Google's SERP suggestions).
Compare informational supply (search results for your query) to informational demand (what people also search for) and find what's missing:

We will build two graphs:
1) the content that already exists when you make this search query (informational supply);
2) what else people are searching for when they make this query (informational demand);
You can then click the Niche tab to see the difference between the supply and the demand — what people need but do not yet find — the opportunity gap to fulfil.
Please, enter your query to visualize Google search results as a graph, so you can learn more about this topic:

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Discover the main topics, recurrent themes, and missing connections in any text or an article:  
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Discover the main themes, sentiment, recurrent topics, and hidden connections in customer product reviews:  
Enter a search query to analyze the Twitter discourse around this topic (last 7 days):

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