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InfraNodus
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Today I want to talk to you about the concepts and the abstractions of programmability. This series always aims to introduce a higher level of abstraction for the concepts that we are analyzing to see them in a broader view. Programming, of course, narrowly viewed is the ability to give instructions to a computer and to make the computer do what we want. For the past decades, as computers became more and more pervasive in society, they morphed into many different things. Not only the electronic brains of the 50s or the personal computers of the eighties and nineties, not only the mobile phones that each of us are now carrying in our pockets. But really in every area of society, computers are helping us in various ways, in different forms, managing complexity, measuring and acquiring data, supporting our decisions. So programming today is much more than not just the narrow definition of a few decades ago.

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And programmability is the opposite. What are the things that we didn't believe could be handled by computers? What are the things that we didn't realize could be made smart through the help of people who learned how to program and who learned how to dynamically exploit features of the infrastructure that was available to them, achieving results that are far superior than not. What was possible to obtain in a more static view of those same systems?

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Even though at the beginning I did say that we want to bring these concentrations at a higher level of abstraction. Maybe this sounds a little bit too abstract, so let me make it very concrete with a few examples.

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The first one that I would like to offer is the concept of programmable money. You may have heard this expression in relation, for example, to beat coin or to etherium in general, the concept of blockchains and cryptocurrencies. Think about it. When we had metal coins or bank notes as money, their futures were embedded in their physical properties and they were very static. They could evolve in time, but this evolution was over the course of decades or sanctuaries. And to introduce new features in a monetary system required very often wars and revolutions. It wasn't possible to update how money would work without that. For example when coins were made out of precious metal, people would start shaving off a little bit of that precious metal from each coin, imperceptibly stealing a little bit of gold or silver and then collect the powder that could be of course sold again.

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And somebody receiving the coin that was the, based through this shaving and the theft would not necessarily be able to tell the difference. The ridges that you see on the coins today, even though to coins today are not made of precious metals, are the remnant of an upgrading of the monetary system where the ridges stopped the thieves from shaving golf, the precious metal, because somebody receiving the debased coin would be able to tell that the regions were missing. And as a consequence, they shouldn't accept the coin because the value of the coin was directly represented by the precious metal.

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As a side note, of course, the basing was done directly by Eh, the mince because famously, even by the Roman Empire, what was happening is that less and less precious metal was included in the coins as a percentage of the total metal content. And the Roman empire were still pretending that the value of their money should stay the same. And this was accepted for a little bit of time, but then everybody came to the understanding that this was not acceptable anymore. And that put a very chaotic pressure and transformation on the Roman economy. That according to some who look at the economic roots of a social change was one of the reasons, one of the main reasons for the downfall of the Roman empire.

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Or think about the invention of bank nodes themselves. The fact that we could pass an IOU from somebody to somebody else. And this chain of debt was universally accepted and the bank notes could be used in various ways as a store of value unless you had a fire as a medium of exchange, as a unit of account in the various forms that we understand money. And of course a few decades ago another transformation of money started when it started to become digital where there wasn't any pressure. Smith Hall, there wasn't any bank note just in bank ledgers. The numbers representing the balance of an account and the changes in this balance towards another ledger was how money was started starting to be handled. And we very happily went towards that direction because rather than shipping large amounts of cash transferring value with electronic orders was much more convenient today.

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Letting aside how exactly this happens in terms of the centralized trust security, what we can do with blockchains and bitcoin and etherium and many, many other modern types of digital money is to actually explicitly design and implement new features in them. If you want to implement different types of lending and their initiatives of particular kind of kinds that have never been possible before, you can either do it yourself or you can hire a any theory them developer to do it for you and the so called smart contracts will embody and implement over the blockchain, this new function of money. And then it will be just a question of promoting that new function and letting other people see that it is actually useful, have them adopted and whether slowly or very rapidly, eh, millions or billions of people starting to realize that they can now do things that they didn't think could be done with money before.

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Very importantly, none of these steps in upgrading money require the central planning or the revolutions or the wars that were necessary before where, Eh, we needed a lot of time and they can as a consequence, be experimented with in a very agile manner where we can understand what works, we can discard what doesn't work and in general improve the features of modern money much faster. Let me give you another example of programmability solar energy.

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Our Energy Infrastructure fundamentally enables and empowers our society. If you only have a firewood and you are jealously conserving the previous fire because you have a hard time even starting it. And then when you settle after a day's worth of foraging and a and, and migrating in your nomadic tribe and it dulls who are entrusted with preserving the embers of the previous night's fire, start the new one. The things that you can do with the energy to chemical of that wood fire are very few. You can cook the meat that you hunted during the day. You can huddle around the fire to receive its warmth and you can preserve it for next days tasks and objectives. But not much more. In today's traditional energy infrastructure, we can do many more things. We help in transportation, we help in growing food with the creation of artificial fertilizers that enhance the ability of the soil to support plant growth.

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We are using it to eliminate the difference between night and day, extending greatly both the geographical range as well as the hours during the day during which we can be productive and many, many other things fundamentally to energy powers. Our communication methodologies, our tools to both analyze, formalize, store and transmit knowledge that we accumulate at an accelerating pace. But once again, to add new features to our energy infrastructure is cumbersome. It took us hundreds of years to deploy the power of previous inventions in, in energy it to cuss, eh, an entire sanctuary to fully exploit what a petroleum represented in our ability to generate energy both directly as chemical energy in our cars or to generate electricity in our power plants. But now we are starting to realize that we need solar energy and batteries. We are entering a phase of programmable energy systems. Many things that once we are viewed as weaknesses of these new systems are now becoming strengths.

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We discover how resilient the coupling of solar energy, but also wind and hydro renewables in general to get her with battery storage systems is how rapidly they can respond to varied needs coming online in a matter of a micro seconds in order to supplement it, the needs of an electric grid that he's on the verge of failing and avoiding blackouts and brownouts that can be disasters. But also, for example, providing a light it to a totally isolated villages that previously could only obtain light artificial light during the night using expensive dirty and harmful to the health. These are generators.

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So the next steps are going to be to make sure that we fully understand how to introduce new features. We are, for example, rapidly developing fully electric cars that are representing not only a novel means of transportation, but they are also the basis of a distributed storage mechanism. When an electric car is parked and it's battery is full and you energy market could bid for the electricity contained in that battery and the by that energy at a price that is much higher than the prize that it costs to originally acquire it. That is just one example of a completely new feature of this programmable energy infrastructure that we are designing.

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The last example that I want to give, and there can be many more if you are interested in learning about further examples of general infrastructure programmability, absolutely. Please reach out and let me know either in the comments to this video or via email or on social media and I will be very happy to provide further examples or actually I invite you to design based on what you heard other examples in other sectors of society. So the last example that I want to give is that regarding smart cities.

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Smart cities are obviously great idea and we are aiming to make our cities smart in order to be able to support the inhabitants in a manner that fulfills their objectives and really strongly furthers their ability to grow together with the, with the city at acting to the environment, but also being able to accommodate inventions that are going to come in the next decades very flexibly. Very openly. Smart cities are the ultimate example of programmability exactly. Because what is assumed that each of the component infrastructures of buildings, of transportation, of energy, but also water, sewage and under forms of support that the city provides to its inhabitants are accessible by various types of computers and are programmable in the sense that we can garner data, we can analyze that data and we can make decisions that are fed back in the loop to make sure that enough water is stored and enough water is distributed.

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Enough water is collected as the runoff of the various systems. And now of course we'd programmability there. Com enormous new responsibilities. We didn't have to worry about malicious attacks against our energy infrastructure. When we had just a, a bonfire in the middle of our cam ground in prehistoric times or rather defending against the, that malicious attack internal or external was relatively simple. Today we have to worry about cyber attacks and various unexpected surfaces of vulnerability in all of these infrastructures. So we have to develop the programmability of each of these and any forthcoming new programmable infrastructure with deep security in mind. That is why since it's security is constantly probed and constantly eh at tagged because of the economic gain that one can have a, if one of these attacks is successful, blockchains are so promising, they are open source.

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So it can be argued that any vulnerability that is found is very quickly discovered because thousands of people are seeking to find vulnerabilities and the code is available for anybody to look at rather than just to a few. They are distributed all over the world and anybody can tinker with them. Anybody can add a new features that can spread rapidly if they prove to be useful and based on various consensus mechanisms, they can provide a very novel trust mechanisms to introduce the features into the main lines of implementation and use. So I do believe that blockchains are going to play an important role in providing secure programmability in the new programmable infrastructures that we are going to take advantage of in the future. So what is the ultimate programmability? Well, certainly biohacking represents an incredible frontier. We are on the verge of understanding enough about our metabolism.

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The way our genetics works based on DNA and its various expressions. We are starting to understand enough about the way that our nervous system works and our brain works. So we are going to absolutely guarantee Lee tinker with these systems and make them programmable. What is your nature in the future? What are the features and characteristics that you will have in terms of sensory inputs, in terms of ability to communicate or what environment you are adapted best to is going to open incredible new degrees of freedom. And we will leverage those degrees of freedom, hopefully responsibly, but certainly unstoppably. So I believe that programmability is certainly a novel feature that is blossoming today around all that comprises human society. And we are going to recognize and debate how best to use these new features.

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Developers have a specific expression, API application programmer interfaces and it is an acronym that is worth keeping in mind. Anytime that you find a new gadget or you move into a new home or you sign up for a new service, ask the creator of the gadget, the previous owner of the home or the provider of the platform that supports the service. Do you have an API? Can I use that API? What are the active security measures that you are putting in place so that the API cannot be maliciously exploited against my interests? And if the answers are lacking, you know that we still have a long way to go, but more and more often the answer would be positive and it will give surprising new opportunities to fully enjoy the advantages that each of these systems can provide you. So thank you for watching this latest episode of the context. I greatly enjoy recording them on a weekly basis, and Eh, my ability as well as my team's to continue to do so entirely depends on your attention, on your

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activism and on your support. You can become a supporter of the context on Patreon for as little as $5 a month. You will help me and my team to continue analyzing and creating the future together.

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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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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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Structural Gap
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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:

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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):

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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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