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machine:machine learning
learning:machine learning


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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.

the names and labels that we give to things, allow us to concentrate our attention to their nature, guided by the intuition and the associations that the name elicits every name represents an analogy, or a metaphor to another object or a concept. In reality, it is an interesting chain of cultural heritage. And of course, it accelerates our understanding as we leapfrog through those intuitions into better gathering the meaning behind the words, but it also represents a little bit of a trap. Because we are pushed to take the analogy, represented by the label to an extreme, rather than using it, just as a reference point or. This week we are going to talk about a specific set of terminologies to different groups of concepts. Their analogy, and their difference. And we will try to conclude, hopefully something useful about it. The terminology is that of a singularity. And the two concepts are the physical singularity at the center of a black hole. On one hand, and the technological

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singularity, on the other. The Nobel Prize committee assigned this year's Nobel Prize for Physics to three scientists. One who explored the theoretical concepts that lead to an understanding of the nature of black holes and the probability of their formation. And to whom independently were able to follow the experimental data in order to prove that the end at the center of our home galaxy, the Milky Way. There is massive black hole. This is a wonderful example of the power of science, and the two sides of scientific exploration, the theoretical progress, and the experimental proofs that go hand in hand. When Einstein formulated the general theory of relativity, that analyzes the role of gravity, and the interaction between energy and mass and space time, as they are evolving in our universe, he himself was surprised and somewhat alarmed, that the theory predicted the existence of specific conditions that may allow the formation of so called singularities a singularity is a particular

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point in a mathematical function, where a certain value goes to infinity, a physical Singularity is an impossibility. As far as we know, rather than in mathematics where we can entertain ideas that are very abstract detached from reality and go to even further layers of abstraction, without being hindered by what is possible with our only guidance being the coherent set of statements that we must take into account as we develop new theorems. And we prove them in physics.

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We don't have this freedom.

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If we meet some conditions that lead to infinite magnitudes of certain variables that has absolutely no role in our universe. So the discovery of the possibility that the general theory of relativity would lead to these singularities was

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Shortly after it was established that these singularities, if they existed, would be shielded from the rest of the universe. So, they may exist, but we never actually encounter and interact with a so called naked singularity, the Singularity is behind what we call an event horizon. And the event horizon is such that it represents one way membrane. You can traverse it going to watch the singularity, but you cannot come back. Not only you but nothing can come back from the event horizon. The current nomenclature around these objects, is that of black holes. A black hole is black. Indeed, because the light that goes towards it cannot bounce back reflected by the singularity inside the black hole in the event horizon of the black hole is a point beyond which actually a sphere, where if you are closer than that. frontier towards the center of the sphere. You cannot turn back. And not only you but not even light can turn back. Because the mass. That creates the gravitational field requires

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a certain acceleration in order to describe an orbit. That is not bound around, but can for example, reach your eyes if we are talking about the photon. And if the mass is large enough and the radius is of a certain kind. Then, the acceleration needed in order to free the orbit to leave the proximity of the black hole is a speed larger than the speed of light, which is universally understood to be a maximum speed in our universe. Originally, as these concepts were understood it appeared that only under extremely rare conditions black holes could form and Roger Penrose. The name of the recipient of the Nobel Prize. The theoretical part actually discovered that. Instead, black holes can form under much more generous conditions, much more widely possible conditions.

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the theory of relativity the general theory the theory of left relativity was formulated in 1916. The concept of black holes was started to emerge around the 30s and Penrose his work, happened in the 60s even closer to today. In the 80s and the 90s. Lee Smolin established, and even more surprising the result.

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It looks like that not only

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black holes exist. Not only black holes can form. Not only the conditions under which black holes form are more permissive. But actually, our universe, appears to produce the maximum number of black holes possible without itself collapsing on a single black hole. It would appear as if the universe is optimized to be a black hole generating machine. And no, We have absolutely no idea what this actually means, what are the implications on the experimental side. The Quest has been equally intriguing and and magnificent. Imagine setting out, and being persistent and precise and really believing in your mission. Strong enough that for 20 years you collect data. And it is not data that it is easy to come by. You collect the data of the stars that you can image with your telescopes that are the closest to the center of our galaxy.

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10s of thousands of light years away.

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And you keep watching their orbits. And of course, all the mathematical calculations needed all the error correction, all the even availability of newer and newer instruments that allow you to make these observations at a higher degree of precision and confidence, show you that yes, these stars are on an elliptical orbit, depending from their mass, depending from their momentum. But each of them seems to be orbiting something that is itself not visible. And then, based on the equations of motion that originally were formulated by Kepler. And then, enhanced by Newton and enhanced by Einstein, you can calculate the mass of that invisible object that forces the stars in their orbits. And you can conclude that yes, that object which cannot be seen as the mass of approximately 4, million times the mass of our Sun. And the only thing we know of today that can be as compact, and as massive and still completely invisible not emitting any radiation is a black hole. So, the theoretical side and

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the experimental side concluded, really something wonderful of an object that was extremely exotic posited to be maybe even impossible. Looked at with gait great concern and, and with awareness on the side, or even one of the fathers. That made possible the concept to emerge. And this allowed understanding that indeed the object exists in understanding its parameters, its nature. As a matter of fact, we have today. All kinds of understandings and expectations of the nature in the behavior of black holes, whether they are rotating and how fast and what does that mean with respect of the nature of space time around them, whether they are interaction with quantum systems, makes some even more complicated and strange mechanisms to emerge by which. Even though. Nothing can leave a black hole black holes, very, very slowly evaporate as quantum pairs of particles, being born half in half out of the event horizon. Take away, mass energy and information from the black hole. And of course, also

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very recently, the merger of black holes to black holes orbiting each other. Ever faster ever closer until the event horizon such, and they become one single object. Now, I always like to make connections between different layers and different fields, and we have been talking about an analogous concept that we call the technological singularity, where rather than talking about the membrane of space time representing the event horizon, the event horizon for the technological singularity is in in time through the history of the development of our technologies and in particular the emergence of artificial intelligence that is able to modify itself in order to improve its own workings, and the various people active in the field of understanding the consequences of these concepts, and the implications of those technologies and their applications

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have formed originally

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a point of view that saw the technological singularity as something impenetrable similarly to how originally we saw black holes as

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something that we couldn't go beyond.

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We were supposed to stop even trying to understand what could happen after the technological singularity. But today, that is not the case anymore. We are starting to formulate all kinds of theories, and we are starting to experiment, at least on paper with what are the parameters that could lead to different kinds of technological singularity. And since we are the ones, giving rise to the tools that become autonomous and generate that technical, the technological singularity itself. When we look at the available alternatives. we are then able to pick the ones that are most beneficial to us. And rather than the technological singularity representing an impenetrable final moment. We are now starting to look at what is a past singularity tarion world going to look like.

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What is going to happen.

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Beyond the times of the technological singularity, how will the artificial intelligences that will shape the world and the universe. Act, and how will humanity, find its role, interacting coexisting with these artificial intelligences.

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we are closer to the formulation of the original theory hasn't, there hasn't been enough time to establish yet. All the details that the physicists have had over the course of more than 100 years, they had at their disposal.

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There are a lot of things that we don't know.

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To be honest, there are still a lot of things we don't know about black holes, as well. But in the field of artificial intelligence and the technological singularity. It is really important that we get things right. We don't have the luxury of observing 10s or hundreds or thousands of independent events, and then look and pick what is the one that is proving a particular theory that that we want to confirm. We don't have alternative experiments to carry out. We only have planet Earth. We only have the human civilization. We only have very likely one chance to get it right. That is why a lot of people are extremely concerned about one. The lack of deep understanding of the power of artificial intelligence among both the general public, the policymakers, but also many of the technologists, and that is why a lot of people want to have more resources dedicated to understanding what are the right conditions for the safety and security of artificial general intelligence for beneficial AGI

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to emerge. So that humanity can thrive. Alongside. These new protagonists of the trajectory of our civilization. Now, the analogies of these two sets of labels and terminologies and theories and experiments cannot go too far. And it is not the case that the parameters and the variables characterizing black holes can be taken as an inspiration for understanding the nature of AI. Also, too many people take the root of the word singularity. Literally, and they pretend to give a further meaning by talking about singular experiences and uniqueness and or the solitude. None of that none of that has any relevance to what actually the topic at hand is sure you're welcome to explore other branches of philosophy or a understanding linguistics, or ethics. But the interpretation of the root of the words as they apply to these phenomena is likely to lead people who are not specialists are stray rather than helping them in a better understanding of

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what we are actually talking about.

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There may be

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one exception to this.

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And that is our insufficient understanding of the frequency of technological civilizations in the universe. their ability to survive, or inability to survive. And the formation

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of technological singularities.

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If we indeed start to understand the trajectory the dynamics and the nature of a past singular etherion world. We may be better equipped, in looking out in the universe and try to observe and record the traces of extra terrestrial technological civilizations that have progressed through the technological singularity to build past singularity Aryan civilization. And even when we do that, resolving the Fermi paradox that asks

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Until now we haven't been able to observe alien civilizations. We may conclude that the universe is not only maximizing the number of black holes. But the universe is also a machine to optimally build the maximum number possible of technological singularities, leading to the maximum number possible in the universe of post singularity Aryan civilizations. Now, wouldn't that be a surprising analogy, and connection in the two fields.

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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.

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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:
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We analyze the sentiment of each statement to see whether it's positive, negative, or neutral. You can filter the statements by sentiment (clicking above) and see what kind of topics correlate with every mood.

The approach is based on AFINN and Emoji Sentiment Ranking

Use the Bert AI model for English, Dutch, German, French, Spanish and Italian to get more precise results (slower). Standard model is faster, works for English only, is less precise, and is based on a fixed AFINN dictionary.

Keyword Relations Analysis:

please, select the node(s) on the graph see their connections...
+   ⤓ download CSV   ?

Use this feature to compare contextual word co-occurrences for a group of selected nodes in your discourse. Expand the list by clicking the + button to see all the nodes your selected nodes are connected to. The total influence score is based on betweenness centrality measure. The higher is the number, the more important are the connections in the context of the discourse.
Top Relations / Bigrams
(both directions):

⤓ Download   ⤓ Directed Bigrams CSV   ?

The most prominent relations between the nodes that exist in this graph are shown above. We treat the graph as undirected by default. 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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Enter a search query to analyze the Twitter discourse around this topic (last 7 days):

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