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Recently, I was able to observe a co worker exerting real violence on software tools that he was using for completely unintended purposes. Why, because he was familiar with them. And that is when I realized even more clearly through that example that excessive degrees of knowledge and familiarity can represent an important obstacle in an age where we can and must upgrade our knowledge, rapidly at an increasing at the jolting pace. My name is David Orban, and this is the context. Unlearning can be as important as the ability to learn. When the pace of technological change, provides everyone with increased abilities to collaborate, to communicate, to assign and deploy resources in order to solve our challenges. When it was the case that the space was very low, or almost imperceptible. Then we could actually achieve status by perfecting and maintaining our position around mastering a set of tools a set of practices. And then the incentive was not ever to move beyond. However, even though

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in many organizations, whether businesses or academia or government. This kind of mentality is probably still predominant. We are now in an age where that attitude is absolutely counter productive. We can, of course, achieve a certain degree of mastery of the tools and the methods and the processes that we are using, and spread that mastery in our teams, but the ROI the return on the investment of increasing that mastery above a certain level, is negative. And the threshold, within which it becomes negative, is possibly lowering. What this means is that as soon as we arrive to a given level of mastery of the tools and methodologies and processes, rather than investing our effort in increasing that level. We should start looking around, comparing the features and the benefits of the tools and methods with the features and benefits of alternatives, asking ourselves is what we are doing fit for purpose. The reason is because very likely. By the time we are asking ourselves that question,

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new tools and new methodologies, will indeed be available. And it can very well be that the answer to our question will be, yes. Moving to the new tool will represent a positive change. In order to be able to move. We need to relinquish the comfort and the stability of working with the known and embrace the unknown, rather than having to do it, infrequently, and under pressure.

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We now have to do it more frequently, and not because the external circumstances are imposing on us this kind of decision that we cannot postpone anymore, but because we realize that it is to our benefit, as well as the others that we are working together with. Now, I am talking abstractly, so let me give you a couple of examples. We have a couple of billion people at least use Google tools. And those of us who are not curious enough to go beyond may not realize that Google has fallen behind in many ways. Astonishingly, all in its approach, both to how it enables people to interact and collaborate and communicate, as well as in the technology details of how the architecture and the implementation of their tools supports that kind of approach. There are now important alternatives slack for chat notion for collaborative document creation, that they are rich with components that are dynamic and intelligent, such as task management, or what we used to call spreadsheets are now really

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hyper powered, in order to analyze and massage your data in many different ways. There are alternatives that those themes that can embrace them, is really not only enjoy, but greatly benefit from. And the mentality of course, can be twofold, either. The team is formed with those who have started using the most recent tools. And then, for them it is natural, they don't even realize that there are others who are suffering from, and the limitations of the previous generations, or even though the people within the teams are experienced in the previous generation, they are mentally nimble enough to embrace the new solutions. The other example is in science and engineering. Whether you are designing in a scientific experiment, or whether you are designing a rocket or a self driving car. Our ability to simulate is refined, to the point, before starting to implement final designs that it can greatly accelerate the cycles that iteratively brink, to what actually works. It can radically lower

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the cost of prototyping, and it can really enhance with unexpected solutions that emerge from this approach. The final result. We are seeing this in areas as diverse as the design of the starship, or the design and the deployment of Tesla's full self driving approach and feature.

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unlearning abandoning the comfort of existing tools and existing methodologies, is a fundamental ability that we must embrace develop increase, and has you age, supposedly, that is harder and harder. It is one of the reasons why, maintaining a youthful mentality, to the point of being happily accused of being childish exhibiting neoteny the maintaining of childlike features in adulthood, curiosity, risk taking. A healthy. These respect for authority. These are all positively, adaptive features, and we all can strive, not only to cultivate them in ourselves, but also in supporting others who are embarking on the path of discovering them, letting them know that it is okay, not to be too strictly embracing the existing approaches they know very well, it is okay to let them go. It is okay to embrace the new, and to go towards unavoidable stumbling blocks and mistakes along the road, because the benefits are higher, and then go and repeat. This is just one layer. And the first layer.

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Because, obviously, the question is, is this approach itself, constant. Should we examine what are the consequences. The limits the rooms for improvement. In this approach itself. And that will be for an eventual future episode of the context. Thank you very much for listening or watching today's episode of the context. If you like what you hear, you can become a fan, a supporter sponsor or benefactor, on patreon.com, slash, David Orban,

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      Word Count Unique Lemmas Characters Lemmas Density

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      network 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.
      ×  ⁝⁝ 
      Main Topical Groups:

      +     full stats   ?  

      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 and are given a distinct color.
      Most Influential Elements:
      +     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:

      Reset Graph   Export: Show Options
      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

      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
      (frequency / time) ?
      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 frequency of occurrence.

      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:
      reset filter    ?  

      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

      Sentiment analysis works for English language only. Contact us @noduslabs to propose a language and to get updated about the new features.

      Network Statistics:
      Show Overlapping Nodes Only

      ⤓ Download as CSV  ⤓ Download an Excel File

      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).
      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.
      Find a market niche for a certain product, category, idea or service: what people are looking for but cannot yet find*

      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 the search results as a graph, so you can learn more about this topic:

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      Enter a search query to analyze the Twitter discourse around this topic (last 7 days):

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