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Top keywords (global influence):
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Explore the main topics and terms outlined above or see them in the excerpts from this text below.
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I am often asked as I talk about the disruption of technology and how it changes our society, what is the right age to introduce people to these concepts and shouldn't we, rather than talking to executives or people who are founding startups actually target high schoolers or even sooner children in elementary school so that they understand the power of exponential technologies and what is happening in society sooner rather than later. This is an important challenge.

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Of course, the younger the mindset, the more dynamic it is and the more readily it can accept the new rules that are driving our societies rather than having to demolish the dogmatic assumptions that have been accrued through decades of experience being exposed to more traditional places at the workplace or at the college a little earlier, and having demolished to these a dogmatic assumptions, reconstruct a worldview. We had a great effort, but also with the, the danger that comes from having glossed the roots supporting us. So on the surface it would look that the advantage is to actually start sooner. Starting in high school, starting elementary school. And is this true? Is this the case?

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There are certainly skills that are not innate that we can learn very early. How we learn to walk is biologically programmed in what we are, eh, we have legs and all our muscle movements the ways that we learned to grab chairs and then in a magical moment for the parents that look, let go of the chair and take the first steps unaided on, on our two legs. Well, these are things that are unstoppable and natural for, for all of us. There are other, the skills that we can learn very early. For example, riding a bicycle that are much less natural and actually much less intuitive. The fact that we are moving our legs in a certain way and the world around us moves in a way that is completely different from what would it be natural to expect with respect to movement of our legs is something that we have to understand.

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Reprogramming our brain, reprogramming our intuition, reprogramming our muscle memories and demonstration of the plasticity of the brain that can be experienced, that at any age is linked to this. If you go on a treadmill and maybe you watch a screen or you work, there are now treadmill desks for working while walking gonna on a treadmill, presumably not running. And you do this for an extended period of time and let's say an hour when you get off the natural and traditional movement of walking will be misaligned with the expectations of your other sensor input. Most importantly, your vision. Because during that hour your brain got accustomed to the fact that even though you are walking, the world is unchanging. So for a few moments when you get off the treadmill and you walk, the fact that the world is coming closer is interpreted by your brain as if you were much faster than not what your legs are telling you.

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It is quite to disconcerting. And other and example of the same kind of brain plasticity that all of you can experience is Klein being on an escalator that he's not moving. We are accustomed to the little jolt of a dynamic acceleration when we step on the first few stairs of the escalator. And then either we extend steel or we keep walking upwards, but we are in motion. And then when the escalator ends, as we get off, there's the same kind of a jolt of negative desolation. And then we resume walking normally. However, when the escalator is not moving because maybe it's broken but you still walk up it, the first few steps again, are incredibly disconcerting because the expectation of change that is not happening in our state of motion at Misa lines, our muscle memory with our brain as it has been previously programmed.

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The ability of learning skills is there whether we are young or or older. The question really is should we expose very young people to the uncertainty and to the stress of a worldview that is based on these ruptures that is based on accelerating change? Is it the right thing to do?

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In the past we decided that it definitely wasn't, not only we structured the educational system in a manner that is resisting change as every bureaucracy does, but even more so because the protagonists of the educational system, the teachers themselves in a position of authority and respect that they need to maintain towards their students cannot afford to say that they are confused or they don't know something or that what is going on in the world is hard to decode. And the tools that we have available may not be sufficient to do so. But we went even beyond. We created all kinds of fairytales and metaphors and magical explanations that are old air to pretend knowledge when the knowledge is missing and pretend explanations when the explanations are potentially too complex or would require a chain of knowledge that is, that is lacking.

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So in the past, this wasn't the case. Can this be the case today? How can we go from here to there? How can we achieve an educational system that is able to admit its own ignorance, that is able to experiment in front of the unknown that is putting teachers and students potentially on the same level in front of the task of a decoding the path of our interactions with new technologies and, and new ways of acquiring and managing and deploying knowledge. I am skeptical because just as the proactive attitude and the courageous experimentation on the side of adults in the population is in general is available only to a very small percentage that must be more or less the same among teachers as well. And we would want the school system to adapt in a uniform manner to this challenge. If we tell teachers, hey, don't pretend you know, everything, tell the kids that you are as confused as a anybody else and start experimenting. That kind of organization would be extremely different from school

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to school and we wouldn't want that to happen. And parents very importantly, wouldn't want that to happen.

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And in our current educational system, the three components of students and teachers and parents must all agree how to implement any kind of change.

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And the parents are of course a key here. There could be some who are very open and say, well, I don't want to lie to my kids. I don't want to pretend that a, a lot of what we are teaching them is going to be very useful. And a lot of what we do in school is to teach them how to learn but in a very indirect manner. So if we can do without all those, all that indirectness and just teach very directly, these are tools for learning. These are tools for throat in the previous episode of the context that we referred to that very explicitly of how our tools for thinking evolve and become better. So if we follow that in our school system, potentially a lot of time will be freed up. Think about it, how much time is spent in the school to teach the children facts to the point where if they realize and one to start using Wikipedia or other systems that accelerate their ability to leverage facts in order to do something with them often they are punished. And this, this punishment is a clear

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indication of the resistance of the educational system to embrace a new agreement around what should be told and how it should be told.

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And if some parent is not as open and not as ready to start some new educational system where experimentation is constant and the, we are all looking at the results and analyzing them and admit if we fail and we start over, well they could be pretty justified in claiming that the experiments are not only the things that are done at school, but the experiments or the experimental subjects are the student themselves. And they would be able to come and say, sorry, I don't want my children to be experimented upon. I want the school to expose my children to known facts, to known methods in order to achieve the expected outcome of a well educated, well adapted person who can then enroll in college for example, a and go on in his or her life. And the school would have no way of stopping that parent or, or provide a counter argument because if the school is honest, it would have to confirm that the new method has no guarantees of achieving an intended outcome. And indeed the experiments are

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actually done on the students as well.

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Now, what this could mean is that the danger of opening the minds of younger and younger people to the uncertainties of disruption that exponential technologies are bringing in our world is preparing them for a struggle that may lead to them leaving the traditional school system. Because the necessity of conformity could be a prize that they are not ready to pay. They may achieve a level of a self confidence or a self consciousness where the compromise required to adopt the methods that they know are in effective in today's and especially tomorrow's world is, is not something that they want to abide by. And then he's even more important that there is an alignment within a family for the parents to realize that this kind of attitude is the, at least partially the intended outcome of what is happening. That the parent, if he or she is honest must tell the child you are actually right. So in my own family, that is kind of what happened. All three of my children have gone through periods

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of growth and maturity and, and increasing degree of self awareness that led them to leave the traditional school system either later or earlier. And this is, of course, I'm pretty seriously non-conformist. I wouldn't expect a large percentage of families to be able or to want to do the same.

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We don't know what the final outcome is going to be, whether in another 50 years time. And my children will evaluate that this positively or negatively, even if we are crossing our fingers for the, the, the positive. But it is important that they are actually very active in acquiring knowledge, in acquiring tools, in acquiring skills, in different areas that prepare them to be adaptable, that prepared them to cope with the change. And this is kind of the polar opposite of what these achieved through a traditional educational path from elementary school to high school to college to postgraduate programs through a phd and then achieve an extreme degree of specialization and then either follow with various research brands and programs to apply that specialization. And then maybe through some patents and other ways of applying that specialization go forth in an entrepreneurial path or in an academic path to teach and keep researching, adding publications, adding a new findings following

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on that journey.

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I'm sure there are a lot of people who are happy with the path. We know it is not scalable. We know that only a sliver of the children that starts school will end up in that. So what we must keep searching for is scalable solutions, is the ability to teach how to think, to teach, how to learn, to teach, how to adapt to the changes, to teach, how to recognize that the lack of certainty is not lack of respect in a relationship between either peers or even in a more hierarchical relationship. That the value and the gain from honesty, openness and truth is much higher than the false narrative of stable falsehoods.

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So I hope you enjoyed this latest episode of the context and the, Eh, I recorded it in, in the, in New York, and you may see a different background. I didn't know if I will be able to eliminate in the post editing all the noises of sirens and everything else that is going on here. If you enjoyed it, you can support the context on Patreon with as little as $5 a month, which is the suggested amount. As a matter of fact, you can become a supporter for even less. The link is available to name your own level of support. Whatever you believe is the right amount and whatever. You can economically sustain. For me, the pleasure is to continue these and not only record episodes, but to establish a dialogue or conversation. I greatly enjoy the feedback that I receive. Emails, tweets Facebook comments and everything else in the various platforms where we interact. So I invite you to do so. Ask me questions and let's continue the conversation.

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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.
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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:
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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.
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+     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.
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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.

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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.
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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.
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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:
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(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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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:
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