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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. Add 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.

n the world of blockchain. Periodically we have waves of enthusiasm and innovation, where new ways of implementing and leveraging this technology is explored by an increasing number of people who include of course, developers, investors, users experimenters of all

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We are now at that point of enthusiasm, and one of the new innovations that I would like to call your attention to, and invite you to experiment with his big cloud, big cloud. As of this recording is barely two weeks old, but already about 100,000 people signed up to play with it. Even though, up to a couple of days ago the website was password protected, big cloud is based on a blockchain, with a native token, which is big cloud, and the user interface. As of today, looks very much like Twitter, you can

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You can reply, you can like, and there are users that you can follow and be followed by them. The big difference is that each account, in turn, has its own native currency. Very simply, that symbol of that made to the currency is the dollar sign followed by the name of the account. I have an account on big cloud. The name of the account is David Orban, and the native currency of that account is dollar sign, David Orban, big cloud calls these creator, coins. Now, we have been speaking about the Creator economy for a long time and of course, there are truly a lot of genuinely artistic and valuable creators, some of them achieving the level of financial support through their agreements with publishers or distributors that allows them to concentrate on what they love to do and what they are fans love them to do, write songs, published novels, come up with new video games. And there are orders of magnitude more 3456 orders of magnitude more.

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Who would like

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to be able to dedicate themselves to those activities, but they can't, because for a million, writers, there is one that achieves bestseller status. Well, unless you buy yourself, the few 1000 books that are needed in order to become a best seller right. And the reality is that a lot of people would be able to be much more creative, if they were able to dedicate the time to eat if they were able to cultivate their budding passion budding talent. Admittedly, there are people whose talent and passion is so boundless. It cannot be stopped. And, and it doesn't matter how poor they are, how isolated, they are how hopeless they are to be known and recognized, they will sail, create incredible value, potentially to be recognized only long after they are dead and tragic stories to be told about their lives. But yeah, that is not to be emulated, so we should find new ways to encourage creativity, even more so in a world where AI guys are going to automate many activities in the cognitive

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occupations in the white collar jobs, so that we don't have to waste our time in doing repetitive mechanical steps that our computers, a few years ago we're not smart enough to do in our place now that they are. We should feel thrilled to be freed to pursue truly human things that make us, passionate and connect us with so many others. Now, we already have a few platforms, for example,

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And a shoutout to those wonderful people who support me and my team on slash David Orban, you are welcome to do so and keep doing so, the nation's on Patreon that fans have creators of many different kinds. Provide can make a huge difference. There are a large number of people who have been able to dedicate themselves to what they love. Because the people who love what they do allow them to do so on that platform and there are other students. Now, on bid cloud, what you can do is to put money in the Creator coin of the people you like and you follow. And that of course will spare them to keep doing what they do and what you love, but rather than just having that as a return, and of course, it is the most important and substantial return on top of that, you will have a potential financial return as well. And the reason is because, as more people putting money in the Creator coin of a given account, the value of that account will go up. Now, when anyone decides to sell the

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Creator coin, the value of that coin will go down. So, as you see, bid cloud, created a market in the consensus of the value around an individual. And if you are a sports star, an entertainment

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a music music music assist a songwriter a singer. You will find millions of people who follow you for example today on Twitter. And these people may follow you on beat cloud as well, and they may invest in your Creator coin. A lot of things still have to be explored and understood around big cloud. Unsurprisingly, given that their platform is only two weeks old. They raised $180 million. Last time I checked, including top Silicon Valley venture capital firms, and they are run by a pseudonymous team of developers, several of whom have been identified, others have self identified, and the system is the centralized so big is merely an interface, representing the reading of the transactions on the platform, who posted what who followed whom, who liked what who bought the Creator coin of whom who sold the Creator coin of whom, what is the content of a wallet with all the Creator coins that someone thinks are valuable, or will appreciate. In time, and alternative user interfaces

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and visualizations are sprouting with unprecedented speed, dozens of teams are working on big cloud based applications. Some of them oriented towards the financial implications, others differently. Why does big cloud matter in social media. It is one of the first business models that may be able to break the monopoly of the advertising base models which we are suffering under, and which Google and Facebook and Twitter, and every other social media platform that reached hundreds of millions or billions of people were not able to shed. Because on big cloud, which is again a decentralized blockchain based network. You are free, and you use it for free. But nobody needs to sell your data. Well first of all because your data is public. With Cloud is transparent. There are private messages. And these are, indeed, not visible to the outside world, but at least as of today, apart from that everything else is pretty much, accessible, and tools will be developed to make it even more immediately

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accessible and usable. And on the other hand the platform doesn't need advertisers to give it money. The people who transact on it who investigate who put money in the Creator coins of the people and the creators, they love are supporting the platform. Do you remember how many times you were wondering. Oh, if only Facebook had a paid version, so that there would be no advertising, and I would know that my data would not be sold to advertisers. If anything you can think of big cloud as that, where you are not compelled to pay. You are driven to pay because you want to support the creators you love other aspects of big cloud are also important. If indeed human relationships and human creativity come to the forefront in an age of AI and automation, then yes, supporting creators and creatives directly, rather than

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supporting the distributors the labels, the publishers is the right thing. It gives them freedom it gives them the opportunity to keep creating, and the markets that bid cloud creates are extremely interesting. Think about what can be built on top. For example, rather than a creator having to sell the coins, it could do like David bawi did the 20 plus years ago securitize and collateralize its creator coin in a defy layer, decentralized finance layer, guaranteeing the loan with those coins whose value, the creator believes will be stable or increasing, and use the liquidity to embark in a new project that will further create positive feedbacks.

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let's think about what kind of incentives, this system will create. If we crave the likes on Facebook or, or Twitter, or Instagram, imagine the kind of craving that could derive from these actions, or more precisely the action of your fans buying your Creator coin could engender and the potentially negative consequences of seeing your Creator coin crash because someone saw the or because a new version of the canceled culture or recognizing an in tolerable mistake you made the late night post. Bad bands together and sells your coin to make it crash. What will our reactions be. There are a lot of things that we will have to discover. I think that bit cloud will explode. Well, there are two directions, that it can explore explode towards either become very very big and important, and potentially Why not Eclipse, Facebook, or self destroyed in some unexpected way because a bug is found in the blockchain or or some other reason, it is very very early. But when I would say that, Yeah, every

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corporation will have its own cryptocurrency, who was to tame it. As my friend Bob Bailey done it pointed out to me yesterday. We now know that there will be billions of cryptocurrencies attached to each individual potentially multiple cryptocurrencies attached to each individual. And then, one of the next episodes of the context, I will tell you why. Actually, we are going to be talking about not a few billion, but hundreds of billions of crypto currencies, based on the kind of thinking and paradigm that big cloud now enabled us to start imagining. So, I don't want you to invest in my Creator coin. If you do so I will be honored, and I have no idea whether you will be making a good choice or not. I don't want you to invest in the Creator coin of anyone else. But I do want you to get your hands dirty, to check it out to invest 10 minutes 15 minutes. a little bit of time

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experience and to start understanding the implications of big cloud. Let me know what you think. Is it dystopia, like in one of the episodes of black mirror that you may have seen that tries to vaccinate us against those future scenarios where very bad things could happen. Based on this technology, or similar ones. Is it going to be the flourishing of a new renaissance of creativity, supported by millions of fans who are engaged and excited about what big cloud can mean for their creators, and the financial returns for them. We don't know yet. But if you don't start to understand. Please, don't complain in a year's time or in five years time, that you feel left out and how privileged. The others were how unfair, the exclusivity of this inequality. Listen. Check it out. I'm looking forward to meet you on base cloud, send me a message post something. Make yourself known. I'm looking forward to it. Thank you.

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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:

please, add your data to display the stats...
+     full table   ?     Show AI Categories

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.
Most Influential Elements:
please, add your data to display the stats...
+     Reveal Non-obvious   ?

AI Summarize Graph   AI Article Outline

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:
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
Discourse Structure Advice:
Structural Gap Insight
(topics that could be better linked):
Highlight in Network   ↻ Show Another Gap   ?  
AI: Bridge the Gap   AI: Article Outline
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 Connectors
(less visible terms that link important topics):
?   ↻ Undo Selection
AI: Select & Generate Content
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
(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.

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 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
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.
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).
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 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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