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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.

Welcome to the new season of the context. In this season, we will talk about a lot of different topics around technology and how it impacts society, which is what we have been doing in the previous seasons, as well. But we will introduce new elements. A lot of you reached out to me and wanted to provide advice on how to make the context even more compelling. And we have taken that advice and incorporated it in this new season. So I welcome you. And let's get into the topic of this week, which is the future of work. A lot of us are concerned as we hear about artificial intelligence, robotics and automation. We believe that these developments in society are going to Cause a disruption that we are not going to be able to cope with. And the accumulating tension is going to cause disorders, social upheaval, maybe even violence or wars. And, of course, I don't know, if it is going to be able, it is going to be possible to universally prevent any of these from happening or all of these from

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happening. But I do believe that the dangers of these transformations are a seen superior to what reality is going to bring. Yes, these technologies are very powerful. However, human talent human creativity, human ingenuity, human passion, human ambition. are going to be a part of the future, regardless of how powerful the technologies that we surround ourselves with, and show themselves to be. The very simple reason is that we will take advantage of the tools and expand the dreams that we can realize we will be able to be more ambitious, using those technologies, rather than being limited by the inferior technologies in what we can do. And as our ambitions and opportunities expand, that will represent the new kind of work, the new jobs and the new ways of both making a living as well as building a dignified life. That will support more and more people. We don't have to fear AI and robots and automation, we have to embrace it. Because the lives of too many people are limited by the

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insufficient technologies that are at their disposal. Think of the life of a farmer in the Middle Ages. And his choices were extremely limited. It is illustrated perfectly, how limited these choices were, by the fairy tales that we inherited from those ages that talk about princesses and dragons and the Seventh Son, killing the dragon marrying the princess inheriting the kingdom. Those fairy tales embody the sheer impossibility of all of those dreams. And we today can be proud of the fact that so many things that would appear as part of a fairy tale to someone from the Middle Ages are indeed part of the daily lives of ourselves, as well as an increasing number of people all around the world. Think about it, the possibility of studying the possibility of receiving nourishing food and clean water, the possibility of planning ahead and knowing that if you do invest in your future, you will reap the benefit of that investment.

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Today, we have the ability to communicate with practically anyone all over the planet and we can undo Stand each other better thanks to platforms and tools of communication and coordination, we can form groups. And these groups can achieve their goals, projects that are ambitious, concrete, and generate value. These kinds of remote collaboration wasn't possible until 10 or 20 years ago, and today is very effective. And this remote collaboration started may be in very specific types of jobs like that of a developer who could just code away on his keyboard and looking at his monitor, and then deliver the results the code periodically every day or every week, to the team that he's part of, or in a in a independent fashion as a freelancer. to the client. Today, we understand that sales and marketing project management, but also design and many other tasks are digital and can be delivered not only within the environment of an office, but anywhere in the world and our understanding of how

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the various themes can be coordinated optimally, how the rhythm is the emotion, the passion of a team can be channeled towards high productivity is also perfected. We have tools in order to chat in real time to move away from the more cumbersome medium of email to these types of modern communications. That's still accumulate knowledge within the group that is not lost, but it's searchable. It's well indexed. It's threaded in the conversations that are divided in various groups or channels and so on. So, as the tools evolve as our understanding of how the tasks evolve, it is evident that open opportunities are available to everyone. We just have to grab the opportunities and to make sure that we leverage them. That is why I am so excited to be involved in one of the most advanced projects that live at the convergence of these various strengths. remote work, flexible work, online collaboration, digital jobs. And it is toray t o double r e Torah in Spanish means tower, founded by Alex

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negra, whom I've known for 10 years. And who is a successful entrepreneur originally from Colombia, who founded two companies, each in a specific niche in online work and online corroboration to his insight further at the layer of abstraction, and realized direct, which is a platform for your professional genome. It is a platform for finding remote digital online work, but also finding other teams for finding talent. If you are On the other side, rather than a job seeker a talent seeker and Dora uses artificial intelligence in order to analyze and match at a high confidence and individual to a job, but also individuals to teams. So that it the skills that you want to develop further develop the skills that you are not interested in developing are not part of your job description, the matching of certain characteristics of the individual with the themes that they will find themselves working in, so that the satisfaction of both is maximized. churn is reduced, and the effectiveness and

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productivity increase.

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Dora as an organization itself, is doing what it preaches. It uses story for finding talent, but also It is a remote distributed team with team members in over 20 countries and investors in over four continents, in many countries in the world, in the company is expanding very rapidly. There are as I speak today, over half a million members so on the platform growing exponentially, and opening your profile is very easy. It incorporates very lean and modern techniques in order to rapidly create the profile but also to create connections rather than the old fashioned method of networking with the cumbersome confirmations. Link a that you can find in platforms like LinkedIn. For example, Tory uses what it calls signals, the signals indicate that you are looking forward to be working with someone in the future, maybe a war with their organization. And together with hundreds of other data points, Torah enables you to create a profile that is rich, lean, effective. The platform, of course,

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is at the beginning, it will enrich itself with so many opportunities for those of us who are looking for enriching our lives in making work fulfilling in the future. Think about it. Is it acceptable, that if you do a survey 80% of the people will respond that they find their work. Boring, not engaging, unfiltered In the future, not too far, but rather close, we will be able to turn this completely around, all of us will find jobs that will give dignity to the individual and the communities that we live in. And that will enable us to thrive, to acquire new skills to apply our curiosity to keep learning in order to provide value to the society that we are part of. And this is open and everyone ever. And this is already available to everyone today. There are 5 billion people of working age on the planet, and 4 billion of them are still without a professional profile. So there A lot to do. And together, we can go ahead on this path where artificial intelligence robots and automation are

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not enemies, but allies in order to make work fulfilling for everyone.

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Has the time come for universal basic income from being a niche, initiative or thought process that only a few people have understood and embraced, basic income initiatives have enjoyed experimentation all over the world over the course of the past 10 years. And with the pandemic, they have seen a decisive acceleration and a rollout in many different nations. If you think about it, basic income is part of a long wave of civil rights and human rights that are progressively recognized, and then become an ingrained and accepted part of what it means to build a humane society.

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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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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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