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About this Context Graph:

total nodes:  extend
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.

Intelligence augmentation is the ability to complement our biological natural faculties of reasoning with new ones based on cultural and technological advances, it is something that we have been doing for ever. If you think about it, even something as fundamental as speaking, and understanding speech is not entirely biological only, as demonstrated by the fact that young children who are found having grown up alone, for example, in the forest were they survived without being surrounded by other people who would teach them how to speak. Even if they are able to start in that process, they will never fully develop it in adulthood. So, it is something that we learn we learned through our society, as social animals, many other animals are social, whether insects or dolphins, or of course, dogs or sheep. And they do communicate to a certain extent. However, as far as we know, none of them invented better ways of learning, better ways of sharing knowledge, like, for example, reading, and

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writing, moving from societies that were able to learn and share their knowledge, based only on the spoken word, to those that multiplied this ability, through reading and writing was a gigantic leap. Even today, thousands of years later, we are still benefiting from the wisdom of people who lived in ancient Greece, for example, and that is, thanks to the invention of writing. It enabled us to improve enormously, both the precision precision, and the volume of information that we were able to

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the printing press was a similar leap, before books, and writing and courses in the Middle Ages had to be painfully and individually copied over and over and over by patient and dedicated monks. But after, it was just a question of improving ever more the automation that machines enabled us to do. If you compare the cost of, for example, having 1000 copies of the Codex, in the Middle Ages, with printing the thousand copies of a book, for example, at the end of the 19th century, the differences probably a million fold. And of course, with the introduction of electronic communications, this achieved a further huge leap to the point where Today we are transmitting the equivalent of thousands of books every second in our digital lives. And when we do video conferencing or when we watch a movie, or even when we are immediately able to access and start reading any book out of millions that are available in the online source or even in free repositories. The next quantum leap in this long

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process of improving the tools that allow us to gather data about the world to codify that data to systematize it and to transfer it to others who can take advantage of it is about to happen as well through brain machine interfaces of various kinds. These are today experimented and then implemented mostly almost exclusively on people who are designing who either have lost the limb and through a robotic arm or a lag, are now able to use that function the that that they lost through their biological arm or biological lag. A friend of mine, Nigel recently died. And he was a wonderful symbol of this kind of hybrid evolution of humans and machines of working class origin in England, he had a industrial accident, he lost his arm. And he was given a fairly advanced bionic arm instead, I remember meeting him the first time at the Singularity University summit in Budapest many years ago. And how wonderfully human, the tale that he told was, and but how powerful his narrative to the point of

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bringing all of us to tears, and CAD, how amazing was the punch line, when he would show his robotic arm and turn his wrist 360 degrees around, which of course, no human would be able to do. outputs like this are complemented with inputs, where bionic ears and eyes aim to restore senses that we may have lost. And just like Nigel wrist, that can do things that biological risks can't, our hearing and our vision, are going to do more than standard human senses as well. The same is going to happen with cognition. It is fair to say that people who read and write can reason better than those who don't, not because those who can't read and write are different people, but they don't have the tools

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to collect, analyze, and act on data. Similarly, the people who will have brain implants directly enabling them with the ability of handling data at superior speeds, at the larger bandwidth, at a faster ability of massaging the data flow are going to be able to adapt better to the world of tomorrow. This world where artificial intelligence and human societies are evolving together a world where AI's have a fundamental impact, including decisions that they will be taking autonomously, because of the speed requires, because of the volume of data required. And the only way of being in the loop is going to be augmented ourselves. When a jolting world exposes you to dig degrees of adaptation required superior to your natural abilities, you have two possible choices to give up, because you are unable to overcome those limitations or to embrace that through technological means. Those limits indeed, are non existent. Because through those technological means, you can extend the range of your

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adaptability to the world whose acceleration in technological change is increasing. So you don't have to throw in the towel. You don't have to give up. You don't have to feel disenfranchised without any power. But you can feel Just as before a protagonist of the jolting world of tomorrow. That is why I am so excited to also offer an entire series of courses about jolting technologies that you can enjoy, you can use to your advantage talking about artificial intelligence, quantum computing, blockchain and cryptocurrency, currencies, decentralization, and many, many other topics that we have covered in past episodes of the context. The jolting technologies courses are an exciting way of learning and learning to act interactive, deeply documented, participatory, community oriented, updated constantly modern and I am awaiting you to join so that you can also take advantage of the sharing of this empowering knowledge. Now, there is also another set of tools on top of intelligence,

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augmentation or alongside it that are available in order to be able to keep the pace with AI's in our jolting world. And in the next episode of the context, I will tell you about that as well. Thank you

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Language Processing Settings:

language logic: stop words:
merged nodes: unmerge
show as nodes: double brackets: categories as mentions:
discourse structure:
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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.
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.

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

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.
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Main Topical Groups:

please, add your data to display the stats...
+     full stats   ?     show categories

The topics are 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.
Most Influential Elements:
please, add your data to display the stats...
+     Reveal Non-obvious   ?

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

Click the Reveal Non-obvious button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.

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.
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.

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Action Advice:
Structural Gap
(ask a research question that would link these two topics):
Reveal the Gap   Generate a Question   ?
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.

Latent Topical Brokers
(less visible terms that link important topics):

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.

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.

Keyword Relations Analysis:

please, select the node(s) on the graph 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 / Bigrams
(both directions):

⤓ 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 as it allows us to better detect general patterns.

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
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