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Project Notes:
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.

David Orban 2:04

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The context season four episode three. Understanding aliens with AI. Our desire to understand is boundless, driven by our curiosity. And as we reach out through our robots astronomical instruments in the hopes of being able to record and communicate the good.

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Our desire to understand, is boundless. We reach out with our robots in the solar system with astronomical instruments all across the universe, in the hopes of recording communications from alien civilizations. And as we do that we operate under the assumption that we will be able to understand them, is this the case, can we prepare to understand, aliens, better could artificial intelligence and our desire to communicate with it, become a training ground for the broader objective of talking to extraterrestrial technological civilizations.

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When all of humanity was composed of tribes roaming the savannahs of Africa, in each group. Just a few dozen individuals. Every encounter with another group was fraught with peril. Very often, as we judge from the fossil remnants could end up in a violent confrontation. The grounds for mutual understanding, versus can't. On one hand, the territory. Even though, Apparently, unlimited was scarce. The tribes that met would be actually competing for the resources of these large territories, they each controlled or wanted to control. And on the other hand, there were a myriad of languages that were used and people just couldn't understand each other. This is very well illustrated in Papua New Guinea. Were just from one valley to another, they are mutually unintelligible human languages spoken with 1000s of languages on the island. Currently the richest pool of language diversity on the planet. So translating, meaning, and mutual understanding, or the lack of it. We're really defining

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factors of whether there could be a bridge for a common ground in trade and commerce. And then, exchanging of cultures, and peaceful coexistence. It is the same today. In order to understand each other. In order to be able to coexist. We have to make a real constant effort, even though we have languages that are now supposedly spanning the globe, and communication. A infrastructure that is going to support this desire to understand the ambition of broadening and deepening our understanding of the universe, encompasses the desire to first intercept record decode communications from extraterrestrial technological civilizations, if they exist, and then sooner or later. To go back to them, depending on where they are probably outside of the solar system may be quite far. This communication could take many years. But regardless, we have to be able to understand the language, we have to be able to understand both the basic structures as well as the more abstract structures that are encoded

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in the language is assumed, even though not universally that certain elements of language could be the same everywhere numbers for example, it would appear almost impossible that the, suppose the alien civilization wouldn't have the concept of numbers the same we do both. Countable numbers as well as they are operations. seelen world from the creator of mathematic and now the physics project actually believes that this may not necessarily be the case that an alien technological civilization could develop across a path that is divergent from ours, and their mathematical and as a consequence, scientific concepts, could not be immediately intelligible to us, and vice versa. Now, before we get to the

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point where we are tasked with understanding an extraterrestrial technological civilizations communications. Of course we could try to prepare. What could we do. If we are not in a position of modeling, imagining, with a sufficient degree of alienness this civilization, then we want have the basis to believe that the training that we are trying to do the preparation we are trying to do is itself sufficient. If you watch the episodes of Star Trek or many other science fiction movies with a few exceptions. It will be something that either makes you mad or brings a smile on your face, of how the vast majority of the aliens are humanoid, maybe they have a crest on their head or they have pointy ears, but they have a head they have two arms and two legs, and they speak English well, not very likely. The degrees of difference among the various intelligent species could be vast, and as a consequence that the, the way they communicate and the way they understand each other. But it turns out

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that we do indeed have an opportunity. A very important immediate need, and the training ground. To achieve this objective of communicating with aliens, and it is our current urgent objective of communicating with artificial intelligences. As a matter of fact, the Explainable AI project from the European Union, as well as other places that are pursuing a similar goal does exactly this. The goal of the Explainable AI project is the ability to understand how an AI reasons and makes decisions in a manner that is intelligible understandable to a human being expressed in human language, expressed in a manner that makes the reasoning transparent. The structure of AI is is supposedly inspired by our own brain, and the way that the brain is structured. Even though this is the case, the way AI is represent knowledge, and the way that knowledge leads to decision making on the side of API's is already sufficiently alien, so that it is not immediately transparent to us already. At this stage, we

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need an ability to translate an ability to render their reasoning so that we can understand it. And we can expect that as AI advances, this distance could grow. The degree with which it's evolving structure resembles our kind of reasoning winner in the way that knowledge is represented, or the way decisions are taken from the data rather than becoming ever closer to the structure of the human brain, and the type of human reasoning, it could further diverge. It could even fork in many different ways of reasoning.

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Unknown Speaker 13:29

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David Orban 13:30

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different, but also each far from our own. It is as if we were trying to talk to alien species to alien civilizations. And some of them may be easier to communicate with others harder, some of them would be eager to talk back, have a conversation. Others may be built in a manner that they don't, that they aren't predisposed to this kind of interaction. So, imagine the opposite. Imagine that we do not succeed making an AI, explain itself. We do not succeed in effectively establishing a positive and constant communication with artificial intelligences. How can we pretend. At that point, that we can talk to alien civilizations and a eyes, actually, are not even the first instance of an opportunity to decode complex communications. We have species biological species on Earth, that have complex communications of their own. Think of the dolphins, think of whales. They have millions of years of oral culture on this planet. And we have barely touched the surface in understanding what they say

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to each other. What is the degree and the depth of the richness of their communication. What level of abstraction, do they reach. We know for example that they give names to individuals and they use these names to talk about those individuals, amongst each other. Are there other layers of abstraction in their culture and in their communication. As of right now we don't know. But just like with AI. This will be a necessary training ground. And also, a test. If we can't understand AI, if we can't understand dolphins and whales. What hope, can we have of ever understanding an extraterrestrial technological civilization. Thank you for following this episode of the context. If you like what you hear. You are invited to become a supporter on Slash, David Orban, you can also subscribe to the online course where I talk about technologies, whose acceleration is increasing on jolting dot SEO. Thanks again and see you at the next episode of the context.

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This unit Liliana

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cuando Luminita era composta Benningfield God. Three bu K vive Eva Remi grabado Sula, Savannah Fergana on incontro era Pino de reality but equally existentially error the feature communica kpsc trade on a fourth and competency on a parallel territory and he said, call your cardiology Culloden Sita the popolazione Alora quasi Poitevin of cembra st sensor, limited command bed exemplification alto de la popolazione della patwon guava gi Neha, dove LG che la de Cates Majora, the lingue Bilotta Suki Anita, Dona Valera loutra si Barcelona lingua Devesa gwazi fossa. Bear gamma Brusha, and Cal Laura lingue era Barrera reality. Play three Boone OC c cap even on Friday, Laura. A cuando real Shiva no copy of a sing, Boniva no les Bazi bedroom come from Tokyo magari nom de when taba pew VR Endo, ma permit Teva scam be co mandatory scan Bianca, the typical Corolla. A record existence suspect of a bacilli figure orgy. A generator lingue K quazi to TT Lizarraga CL chinae Zero engleza Glazer led him

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first to do the comunicazione global kplc veramente a in model, Africa Billa. Ultra tanto important a common premium, a loss for this theory they can be recovered all thread e confini del pianeta Abrar chan sistema Solaria Dover lesson there are both exotic, explore and Oh Martin paralever or campioni da co may take care of presenting a story that sistema salada van or copied a lot composite see on a platform that the Illuminati joven is not on Korra con song Dima, Captain de Sena Ali loan Danny de Luna sofa Charmel Ultra tanto new Roger same purpure ampio. Are you Schendel are registered registrar is Sonali sampling today Billy nella speranza, says it's not the primo boy interchange Tara comunicazione, the trivial technological extra diversity Elina totalmente Oliver Lagonda domanda saremo in grado de that you Friday Kabira Christa comunicazione and then moment to inquire Rivera Soprema responder sensoren their children equally in order Maria assistance Yala de toda dependent

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demented Allah, the stanza, catchy Sarada LePhone to the electrons we Sunni questa comunicazione naturalmente porque Red Bay, Ricky the grand numero the anime, the chain action deniability, Amelia PetroVietnam podi Amanda indietro, ma Karanka Sia, a questo Cousteau numero dos JustAnswer Imani center in Shama, they could if he got a copy de la comunicazione la barrera sobre como impenetrable. Quindi, possiamo criticality indicators to cool my book right best sobre la comunicazione de una vez de la COVID potremmo sabay say hello SEE THAT IS HE Flagler. A OpenNebula solutia amo in quad commodified to enter gingerale scope

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in effective abbiamo Jang una sfida core rent the requested depot. Cuando Costa reamaze sistema de intelligence RVV Charlie of ansata, a gamble on nature subtly competed Kumiko sustained me and I drawn in a coma already attracted the Gianni alle project to the Explainable AI the intelligence of Charlie spiega Bella delone on a rope bear. In effect the Siboney propria questo get EVO different see caning one GA to rally intelligibly de la persona co Mooney lead it resuming daily intelligence LTV Charlie vengono Raisa constantemente a affordable mentor to Solanki project the analog into turn on the appropriate back a normal standard. He sustained me the intelligence of TV Charles see speed, you know, Alice Futura they'll travel oh man or al modo in cui noi stood to rearm or la conoscenza grand Yamaguchi Gianni lavoro complicit are fussy Cassie seguida Preterism enter command. A vn a lower a tournament or no solo known see Habana lemma molto spesso known see a property or proceed belay

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diventare suported su slash David Orban, or put it for MK R Bonati Corso. K, tengo su lead technology Ella Kui, action and a C on occasion. Su jaunting punto CIPO.

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A presto, Allah for simple data, data context.

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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 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 Paraphrase Graph

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.

Reset Graph   Export: Show Options
Action Advice:
Structural Gap
(ask a research question that would link these two topics):
Reveal the Gap   ?   Generate an AI Question
A structural gap shows the two distinct communities (clusters of words) in this graph that are important, but not yet connected. That's where the new potential and innovative ideas may reside.

This measure is based on a combination of the graph's connectivity and community structure, selecting the groups of nodes that would either make the graph more connected if it's too dispersed or that would help maintain diversity if it's too connected.

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

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 / 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. 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:  
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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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