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machine:machine learning
learning:machine learning


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Your Project Notes
Interpret graph data, save ideas, AI content, and analytics reports. Auto-Generate from Analytics
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.

When new technologies emerge, it is always an important question, how to allow the market that they enable to express itself? And how if regulators need to intervene in order to make sure that the market is healthy, with a plurality of players competing in order to provide the best possible service to those that use the products in this new market? Is it possible to see and and find examples where the regulator's provided the right kind of incentives? Is it possible to find other examples, where the regulator's apparently failed, and the market finds itself in a situation that is certainly less than optimal? Yes, examples are plentiful. And sometimes, as it happens, these examples clarify and maybe provide some guidance for the future. But probably, they are not a perfect recipe for understanding what should be done. So what is the role of the new technologies, they certainly change the rules of the game, they make abundance something that was previously scars and whether we are

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talking about physical resources or access to some kind of service. Also, the new technologies make business models possible, where a the relationship between suppliers and intermediary producers, and the incomes and consumers gets completely restructured. And finally, of course, most importantly, new technologies make novel products and services possible things that literally look magical, if we were able to see them with the eyes of the past. So onto some examples. The European telecommunications market used to be extremely fragmented, and extremely expensive. And even though mobile phones it too crude in Europe, and got adopted faster in the 90s, than for example, in the United States, with both handsets and services, this innovation slowed down, I remember that I would travel to the US and look at the local choices available both in terms of the contracts that carriers would provide and in terms of the handsets available, and they would be tremendously inferior in the 90s than not

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what was available in Europe. Not only the variety of handsets provided, for example, by the likes of Nokia, but also the business novel innovation originated by Omni toe in Italy of the prepaid scratch card base the contract Well, actually no contract just your ability to pay 2040 whatever amount of well, then it wasn't even euros, right? local currency in the various countries and make the calls in minutes that was correspondent to that amount was a is able to bring a new generation of mobile phone users that wouldn't underwrite a monthly payment for one or two years. contractual obligation otherwise, then is the announcement of the iPhone completely upended the everything. And in terms of handsets, a Europe fell behind a no one until Android came along with a wide variety of hardware producers could compete with the An iPhone. But what happened in Europe is interesting because

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as the European Union tries to integrate its market ever more tightly, it the opportunity was there to step in for the regulator, and make sure that communications between the member states would not be hampered by antiquated understandings of how the interconnection fees should be charged among players, and then typically passed on to the consumers. So basically, the regulator said, Listen, guys, to the carriers, I give you a given number of years ago off to that you cannot have consumers pay roaming charges, if they are calling from one country in the EU to another country, or vice versa, or when they are traveling, they have to get the same service and the same charges. Regardless of where they are within the EU. This is very reasonable and very advantages, both to consumers and businesses, who are not exposed to the complications and asking themselves, oh, I will have this extra cost if I am calling this person, or I will be traveling, I need to buy a local SIM card and swap it

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out, and so on and so forth. And it is a fantastic step in the right direction for enhancing the collaboration and the business opportunities and tourism and everything else within the countries belonging to the European Union. Of course, in the United States, it is like this, and it has been for a long time in the types of contracts that you signed with carriers do not include a long list of US states. To call to Massachusetts, it's this much to call to taxes is this much. If you travel to California, you will spend this much in data and that much in mobile minutes, and so on. None of that crazy complication would be understandable. And it would severely hamper the healthy development of the communications market. And I don't know right now, but last time I was in India, it was still a case that you couldn't get an Indian mobile phone, you would only get a mobile phone of the state where you were traveling. And if you then this is a huge subcontinent a huge country of over a billion

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people. If you then flew from New Delhi to Mumbai, or you went to go or very well else your phone number either would stop working, or you would end up paying roaming charges, or you would have to resort to buy a new SIM card and change from Well, except if you had like I had the Google phi service, which with a single cause was even then able to provide very, very low cost communication, overcoming the these local quirks. And you can imagine if India were able to adopt the kind of regulation that across all the states of the nation would eliminate these roaming charges. It would be an extremely positive development for the country, both for consumers and for businesses. And at the end, of course, also for the carrier's themselves, who would see the numbers of people who adopt third, fourth and fifth generation, network services and handsets really blossom. Another example is pretty important to my friend Cory Doctorow and to millions of people who enjoy audiobooks.

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Today, audiobooks and podcasts have seen a blossoming because a when people do their chores at home or commute at work, or in many other ways, listening to them is enjoyable, convenient, you learn a lot, you follow a great story or a great storyteller. However, Amazon with its audible unit dominates the audiobook category with a type of exclusive licensing that hampers competition. And under costs, eliminates existing services such as the ability for libraries to land, their collections, to the patrons that visited their, their their offices, or their buildings, whether physically or digitally. And, and Cory, it launched his latest book, ethics service surface on a Kickstarter, especially to break this stronghold of Amazon, so that as many people as possible would pay for an audiobook that is not on Audible, in order to show his publisher that it is possible not to adopt the audible contract of exclusive audiobook distribution. But it is possible for his publisher to support authors

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that wants to make their books in audiobook format to be available everywhere.

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I supported corys, Kickstarter and Cory was also a guest on searching for the question and live where we spoke about this in depth. But the reason I mentioned this example is because the emerging market of audiobooks and podcasts may see the necessity of a regulator stepping in because of the markets failure to stop monopolistic practices to be enforced by these exclusive contracts that do not benefit the author, the publisher and the public. For example, if I am blind, and I want to go to a public library, and go home with an audiotape, I can do that. And listen to the audio tape of it know what author from the past. But if I go to the library, and I want to ask for the audiobook of a modern author, the library cannot lend it to me, I cannot listen to it. And my opportunity to participate in modern culture is severely restricted and hampered by this lack of competition, lack of choice, and due to the lack of intervention from the regulator that left the monopolistic practice to

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entrench itself in the market. So these are reasons for the regulator to actually act and to do it rapidly. Of course, the regulator is in a difficult situation, because new technologies are difficult to understand by the practitioners themselves, let alone policymakers whose job is is across the board horizontally for any technology, not just one, they cannot be a specialist in that they rely on the lobbyists telling them what are the best practices, what are the outcomes, and of course, those lobbyists do not represent the public interest in general. They represent specialized interests. So through these examples, I hope I illustrated that the choice is there, how and when to intervene, sometimes intervene rapidly, but always to make sure that any regulation that is implemented contains SUNSET clauses that force the policymaker to revisit, measure the effect of the regulation, updated and then implemented. New with an improved the set of incentives and conditions so that the market

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can adopt the new technology and choices can flourish and multiply

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and we get

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to enjoy improved products and services in the future as well.

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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.
Generate AI Suggestions
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:
AI Suggestions  
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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.
please, add your data to display the stats...
+     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.
please, add your data to display the stats...
+      ↻    Reveal Underlying Ideas

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.

Download: TXT Report  CSV Report  More Options
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.
Highlight in Network   ↻ Show Another Gap  
AI: Insight Question   AI: Bridge the Gap  
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.
↻ Undo Selection AI: Select & Generate Content

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:
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

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

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Discover the main topics, recurrent themes, and missing connections in any text or an article:  
Discover the main themes, sentiment, recurrent topics, and hidden connections in open survey responses:  
Discover the main themes, sentiment, recurrent topics, and hidden connections in customer product reviews:  
Enter a search query to analyze the Twitter discourse around this topic (last 7 days):

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Enter a topic or a @user to analyze its social network on Twitter:

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