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machine: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.

Do you know that you are a transhuman? I don't know whether you agree, but in the next few minutes I will try to convince you that indeed you are and that all of us are in a condition that transcends that of what we would normally call a traditional simple baseline human being. Let me start with an anecdote in the 2013 the book Inferno was published by Dan Brown. And if you haven't read it yet, skip ahead a few minutes in this video because there will be unavoidable spoilers. The protagonist of this book is Robert Langdon. The study the, the, the researcher the professor of symbology that is unavoidably drawn into some criminal plot which then he triumphantly resolves at the end of the book. And when the book was published, I was contacted by journalists who would point to some particular passage of the book and ask me about it. Then at the beginning, I didn't realize the female protagonist, Sienna Brooks that works together with Langdon in a somewhat ambiguous role, has a

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conversation with him where she points to a napkin drawing on it, a symbol H+ and Langdon replies, Oh, I thought it was a chemistry conference. All of Harvard university was plastered with these posters promoting that meeting. And she responds, no, it wasn't. It was the largest gathering of transhumanists ever.

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That is where Bertrand Zobrist, the bad guy of the book, gets his idea. That leads to the release of a virus aiming to destroy humanity. Now, it turns out that I organized at that conference to get her with a then executive director of Humanity+, where I was Chairman, Alex Lightman at Harvard University. The H+ Summit was held in June, 2010 it was a pretty controversial conference. We had many, many speakers more than 50 in two days in parallel tracks. Ray Kurzweil, Stephen Wolfram, Ben Goertzel, Natasha Vita-More, Andy Hessell, Geordie Rose and many, many others, all friends and all firm believers of how technology can help humanity to address our challenges, to hopefully overcome them, to improve the human condition.

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But there were people who didn't want the conference to happen. The Dean of hardware received anonymous letters asking him to stop the conference from being cowled. And obviously we are relieved to the report that he didn't listen and he wanted the conference to go ahead and to be as provocative, as meaningful as it could be. And as it happened to be. So when the journalists called me, I looked at the list of participants and I could tell them, no, a Beltron job race doesn't appear in the list of speakers or participants. And of course Dan Brown didn't theatre at just a few months ago. I learned from another friend that he was sitting on Dan Brown's side in the audience without recognizing him at the time and, and ask the what he did for a living very modestly, eh, or with false modesty.

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He answered, I write this book, sold millions of copies actually, eh, there is a companion to Inferno which is also a quiet think book. And I was asked to give an interview for these a companion book so that book clubs that would read Inferno could also interpret it with the help of that campaign in book. And then of course the movie came out and so on and so forth. Really popularizing certain ideas. Of course, we, that Hollywood streak of alarmist Apple collectic tendency of drawing our, our attention. Now transhumanism is becoming more and more known. It is a movement that started several decades ago, originally in the 60s. Then in the 80s, in waves of becoming more and more popular, the world transhumanist association was formed, of which I became chairman then. It changed its name to humanity plus to concentrate more on the positive sides of what other people could understand of its aims. It is quite interesting and intriguing actually to note that if possible, one of the first

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transhumanists was Dante.

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Dante Alighieri in his his divine comedy, in the first canto of the third book, Paradise says "Trasumanar significar per verba non si poria”: you can't explain in words what it means to transcend the human condition. And he talks about the Catholic Christian paradise where you go after you are dead. But what we are talking about is a changing definition of what it means to be human while living on these world, you and I and everybody else together. Because technology is profoundly transforming the human condition to the point that talking about what it means to be human without taking into account the role of technology and the accelerating pace of change of technology as it changes our human condition. And the very definition of what it means to be human would be meaningless if we'll look at biologically to be human means to belong to the species homo sapiens and maybe homo sapiens sapiens as these things and separate from homosapiens neanderthalensis for example, or other subspecies.

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Biologically speaking of homo sapiens that existed and are now extinct or actually partially extinct as it has been established, that all of us carry a variable percentage of homo sapiens narrowed in their [inaudible] genes between 1% and two, 3%, except people who are from Africa. And they did not have shared the the, the D don't descend from cold occasion people, but they are purely of African genetic pools because they don't have Neanderthal genes. Sam Harris remarked how horrific it would have been from a interpretation point of view of the scientific fact if it were the reverse, how wide supremacists would have gone and where they would have gone if it were the reverse. So it happens that homo sapiens and our genes intermingled among various such species. But these also true and very important to note that we have been defined and we have always depended on technology for survival.

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There is no such a thing as a natural human devoid and distinct from technology, whether it these simple wooden spare, whether it is fire to cook food, whether it is the iPhone to communicate across the globe. These tools in their first emergence are separated by a hundred thousand or 500,000 years depending on how you are counting. But they are joined by the fact that we as a species and as individuals very strongly depend on them, not only very strongly depend on them, but our dependence is increasing. The type of knowledge, resilience and skills that I would need to survive. You know, wilderness are beyond my ability to acquire or to deploy or rudder. I wouldn't last long. And we did them last long, even though all of the knowledge was available to survive in natural conditions, surviving in natural conditions, universally meant to survive until we could reproduce and until we could bring up the, our children to reproductive age and then very quickly to be gone.

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Personally, I would have died many times if technology were not available. Just to quote a couple of these reasons. When I was little, I would very, very frequently gapped infections that would affect my tonsils to the point where I would be close to suffocating and the application of antibiotics. And later on the surgical removal of my tonsils that at a time in the 70s was quiet common, stabbed one of these infections killing me. But the F neither on TB Biotics nor surgical options would have been available. It is very likely that I would have died in my teens. A second example is that I wear glasses. And even though in a modern environment, these glasses are almost never a must have except for example, when I drive the car, if I had to go hunting, I guarantee that I could have had the best skills. We had a an arrow and bow to GAD the animal. But if I couldn't see the animal, there were no chance that I would be tolerated in the tribe that needed to feed me without me contributing to

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the success of a hunt.

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Can you today survive contributing to your tribe, your society, for example, with different skills or lacking those skills? Rudder skills like reading and writing, the equivalent of being able to hunt rather than hunting for animals. You hunt for ideas rather than sharing the prey, your share ideas and your ability to share depends on your ability to read, write, maybe to speak a foreign language. Certainly these days have an increasing grade, being able to use the computer or the smartphone. And I can practically guarantee that as we go into the third decade of the 21st century, your ability to use augmented reality or virtual reality components or to test and then accept and, and use eagerly deploy various kinds of ever more intimate interfaces with devices that augment your abilities is going to be necessary and unavoidable.

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And even more so if we go further in the fourth and the fifth decades of the century and talk about space colonization, radically supporting humans to survive in space or on Mars or other places of the solar system that do not inherently have the support environment that we enjoy on earth is going to be necessary. Unavoidable technology is not only going to enhance the human condition and our ability to communicate and to organize technology is going to be a necessary existential condition in order to survive. And then of course we will have the choice if we want to intervene and modify our genetic makeup in order to accelerate our ability to adapt to different environmental conditions. This has already been happening even in the scales of our own species. A simple example is our ability to digest lactose, which emerged as a genetic modification fairly recently about 30,000 years ago, and it hasn't had the chance of spreading all across the various human populations. It is much more

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prevalent in Caucasian genetic pools than not Asian or African ones.

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But the pace of biological change is not only too slow, it is too random. We cannot wait around until by complete chance. Some favorable genetic variation emerges and we stumble upon it and we say, Oh, that is exactly what we need in order to successfully survive on the lower gravitational attraction of the Martian surface, for example, which is just one third of that of earth. And it may very well require some at a patient that goes beyond our desire or ability to, to keep exercising every day in order to prevent calls from depletion in our bones that makes them fragile or other kinds of negative effects that we could experience under those conditions. Our ability to intervene and change our genetic makeup, not only of adults, but also of our progeny of our children so that they would inherit these adaptations will be irresistible. And then of course there will be further changes, further opportunities. We will address them in other episodes of the context. But let me just mention a

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couple. Would you go through a process of backing up your mind

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And if you did, would you readily sign up for a service that would instantiate your mind when you died? Or actually would you be curious enough to boot up an instance of yourself in parallel of your biological identity that still existed? What about artificial intelligence is even when we create an artificial general intelligence and AGI, will we endow it with rights and duties? Will we emancipate it or will we pretend to be able to keep it as a slave? And if we do emancipated, what will we call it? Will we call it a human? Will he call it a transhuman? Will it call it a past human? And what will be the civilization that includes uploaded the minds that originally were biological but they are not today that include biological humans that have been genetically modified in order to be adapted to radically different environments.

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Artificial intelligences that communicate, that are empathic, that make moral choices, that have their own goals but are of a different root stock undeniably. And then of course traditional humans. Those that either because of a religious conviction or because of a lack of means or opportunities or just by personal choice do not embrace the opportunities that accelerating radical technologies are going to offer. It is going to be very important to open a deep and broad conversation as soon as possible around these challenges because all of these participants in our rich and varied civilization are going to be with us sharing the solar system and the universe, and we should be ready to make the right choices so that we create a future that is desirable and where we feel as humans, trans humans, past humans, all together at home. So thank you very much. I am curious to learn whether you agree that you are a transhuman or not. Send me your feedback in the comments by email. I greatly

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enjoy receiving everybody's opinion, whether they support or whether they these agree with what I say and I'm looking forward to having you follow the context in our next episodes 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:

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:
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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