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InfraNodus
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.

Welcome to the context. Today I want to talk to you about open source and why it matters so much in our fight against covet. 19. Open source is a concept of information knowledge abilities skills designs. Freely not necessarily in the sense that they shouldn't be paid but in the sense that it is legally permissible to transmit this knowledge in a community that is becoming ever larger.

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04:14

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This has been the case in many ways in communities that would need to defend themselves and that could understand that they would be strengthened by the sharing of the information. Certainly we have not always understood that this would be possible or desirable. The alchemists. Would die in experiments that could be avoided if they would have had the ability to learn from the examples of other archemists trying to transform lead into gold but not sharing their mistakes.

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05:03

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In the 70s with personal computers being born there were clubs with passionate people trying to put them together and thinkering with scarce documentation, but very creatively in these machines that had a very modest functionality. They couldn't do much but it didn't matter because learning about them was what? Mattered instead.

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05:39

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And so learning and sharing this knowledge is what would naturally be going on in this clubs. The documentation would be improved and the programs that the computers could run exchange both on paper because often you had to put them in each time you wanted to run the program and I am mimicking the keyboard with my hand, but sometimes this computers wouldn't even have keyboards you had to manipulate sequences of switches in order to program them.

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06:19

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There was in particular a young programmer who was very passionate about computers and one computer model popular at the time called Altair. And he convinced the friend of his to design a programming language for the Altair a visual basic. Not to visual A basic interpreter and there was nothing visual about it because it was actually stored on perforated the tapes and and that is how the computer would learn how to understand the other programs that were written in the basic language.

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07:05

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Altair was headquartered in New Mexico so from Boston the two friends drove in the 70s to new Mexico and were hired to. Do actually run this program and the successfully delivered it to Altair. The the two guys were Bill Gates and Paul Allen and they formed Microsoft at the time with an uppercase M, and an uppercase S and they were in sense when they realized that people all over the US had formed these clubs and when I was there in turn would run.

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07:53

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Either those clubs the various owners of the Altair Kits after assembling them would exchange the perforated tapes duplicating them in order to be able to run the basic language and the programs on top of the basic language. Bill Gates wrote an open letter to the community to please stop this because the software industry couldn't be born and couldn't blossom if the intellectual property of the softer developers were not respected.

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08:37

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There are many types of intellectual property as it is called in it's an umbrella term copyrights patterns trademarks, and also there is a, An important and interesting way to protect one's ideas the industrial secret because the, Society inexchange for the protection that a patent gives you for 20 years anybody incorporating your invention protected by a patent in something else is either in violation and can be suit for damages or must come to an agreement with you and pay royalties.

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09:27

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In exchange for this kind of protection society wants to see that the inventions and the creations are flowing and and the delivering benefit and value to society at large. Now. Copyrights have been. Notoriously extended in the past decades. If originally, it would be conceivable that an artist would say I am so glad that I have the copyright protection for my art because that is what feeds me.

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10:11

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If nobody were compelled with the force of law to pay for my song or my movie or or a poem or a novel that I wrote I would. Not be able to feed myself. However, today copyright lapses a 75 years after the death of the artist. So, Are we sure that these are incentives that are benefiting society.

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10:48

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Do you believe that any artist will say no? I'm sorry if copyright protection doesn't last so that my great-grandchildren can also benefit from the poem that I am writing right now. I'd rather put down the pan. It doesn't matter how it burns inside me this passion of creativity. I will resist giving the opportunity for it to burst out.

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11:23

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And to be embodied in the point because I find it intolerable that after I am dead and my children go up and their children grow up their children don't receive the royalties of the poem that may or may not be a masterpiece because obviously as I'm writing it. I still don't know.

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11:50

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So, I think we can say that that copyrights are at least a little bit broken. Now. What simultaneously with the birth of the closed source softer industry was happening is that also an open source software movement was being born. Originally at universities and research centers it was absolutely the norm to freely exchange information including software programs.

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12:29

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The programs that are at the basis of running computers the operating systems. That is how it was with the Unix operating system that people could copy compile and run on their own computers. And in the eighties the Linux. Alternative or variant was born for personal computers inspired by Unix in an open manner and Linux became extremely popular.

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13:04

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So popular that today's internet couldn't exist without Linux. It is at the basis of the Macintosh operating system of iOS of Google and Facebook and Twitter and the servers of cloud systems that we use every day. It is basically everywhere. It was so successful that there are now platforms with hundreds of thousands and millions of programmers storing open source projects and those platforms that do nothing but facilitate this storage and the coordination of programmers between them have become a valuable enough for.

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13:54

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In a bit of irony Microsoft acquiring GitHub one of the leaders of these platforms for several billion dollars. As a further demonstration of the victory of open source software last year IBM acquired Red Hat for 34 billion dollars and Red. Hat was one of the first commercial Linux providers Companies that would compile Linux in a usable form create in-depth documentation provide the training and installation services technical support.

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14:40

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And so on and so forth. And today one of the biggest names in the history of information technology felt that they should base their future with the largest acquisition they made ever on acquiring this open source software company. Red Hat. In hardware similar. Dynamics are are playing out. On one end of the spectrum there are car makers or makers of farm equipment tractors and and so on that are obsessed in tight vertical control of not only their machine the repair ability of the machine too.

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15:30

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You cannot bring your car to any dealer or let alone blasphemy attempt to repair your car yourself. It must be brought to an old. Authorized dealership that paid a lot of money to buy the equipment that can communicate with your car and they agree yes. I am the car of that brand you are the repair machine of that same brand.

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15:59

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I allow you to to repair me. And if that dialogue is not completed or if the the maker of the car thinks that the car is attempted to be repaired with something that is not officially sanctioned there can be legal consequences. Imagine if your tractor broke broke you paid it a large amount of money hundreds of thousands of dollars and the official.

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16:37

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The dealership repairing your tractor is too far they cannot come for a week or you letting your crop rotten the field because you are not legally allowed to repair your tractor that is the situation we are in with closed source hardware, but there is also open source hardware that doesn't mean that just like with software.

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17:04

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I can copy your hardware and in a similar way to software we will both have it. Maybe in the near future with 3d printers becoming ever more sophisticated really advanced features, maybe even electronics can be printed and then duplicating hardware on the fly will mean that if I want to have it and leave it to you as well, you don't need to sell it to me or to rent it to me, you don't need to be without if I want to steal it, you can have it and I can have it at the same time.

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17:45

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But in the meantime what is happening until we get there is that open source hardware companies are freely sharing the designs the list of components the list of instructions on how to put the hardware together. A very successful open source hardware company was founded in Italy by Massimo Banzi and the name of the company's Arduino and we know brought back 40 years later the joy of tinkering that disappeared with the extreme integration of electronics components and it was hard before Arduino for somebody without specialized tools and very advanced knowledge to create for example little.

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18:37

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Sensors for internet of things applications or experiment with parallel computing where a task could be distributed across multiple processors and many many other things that today are possible thanks to Arduino, which has become very very successful. So what does have this to do with covet nineteen? Well a lot of the equipment that hospitals and patients people secret covet 19 need today is available in quantities that are absolutely insufficient and of course some things are fairly elementary we are told how to how to sue our own masks and that is fine, but there are other things like the ventilators and the respirators and many other medical devices.

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19:39

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That are tricky and delicate and important live saving but still not available in sufficient numbers my son cosimo was a member of a Facebook group open source medical devices, there were less than 2,000 members in the group only two three months ago now the group has a hundred thousand members and it is translating the documentation of the open source medical devices.

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20:14

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That frantically this community is collecting analyzing implementing testing and producing often with the 3D printing technologies the translation of the documentation alone coordinated by another friend of mine. David devert is happening in almost 70 languages as we speak this is a beautiful example of a global collaboration that is creating tremendous value saving lives and, Guaranteed it is going to be the basis of a very large number of new startups creating value, they will create value the way that rat had creates value that was that became worth tens of billions of dollars, they create value the way Arduino creates value and the community benefits with the acceleration of knowledge that derives with a more enlightened view of how we can.

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21:24

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Optimally benefit both the inventors, but also society in a positive collaboration, so thank you very much for watching this week's the context. I invite you to become a supporter on patreon at patreon.com slash. David orban and I will welcome you to the next episode of the context next week.

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22:37

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See poliber canopy.

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Network Structure Insights
 
mind-viral immunity:
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stucture:
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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.

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Influence Distribution
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Topics Nodes in Top Topic Components Nodes in Top Comp
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Nodes Av Degree Density Weighed Betweenness
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Narrative Influence Propagation:
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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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Main Topical Groups:

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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:
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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:
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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:
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Structural Gap
(ask a research question that would link these two topics):
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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.

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

Emerging Keywords
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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):
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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):
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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


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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:
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Text Network Statistics:
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