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Welcome to this new episode of the context. Today we will be talking about technology adoption and what are the principles that can drive a proper behavior both from individuals as well as corporations and society at large when facing jolting technologies. I have participated just for the past two days at the singularity U Italy summit and the, as it is customary because this was the third edition and the same thing happened in the previous two years. There were protests welcoming the participants and the speakers and the organizers, protests against technology protests against science protests against the type of progress that singularity university represents. So I was able to, to ask myself, where does this anguish come from? Where does this anger come from? Luckily the protests were loud but totally peaceful and there was no apparent danger or threat to our physical security. Certainly as it is appropriate, our mental models were challenged and maybe this episode is at a counter

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proposal to the challenges of a mental model that sees only the risks that sees only the downsides of innovation and of technology.

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The first year when the people protesting appeared actually they were already there when I arrived. Eh, and, and they recognize me and they said, Oh or band is here. And I realized what was going on. I parked the car and walked up to them to greet them and to have a dialogue and I was a little bit let down that they said that they were not interested in having a conversation the year after. And this year too, I went back and shook their hands and, and, and, and welcomed them as well as I also went to the police, which were there and you know, they, they knew about the protest. Maybe they needed some pyramids so they were already alerted and they came out and I and I agreed to the police as well and I asked how they were doing if they wanted a coffee or something.

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But anyway, neither last year nor this year, I attempted having a conversation anymore after what they replied the the first year. And I'm sure that there can be situations where conversations are not particularly useful. The will not change anybody's mind. The positions are too far apart. But what is pretty amazing to me is that the flyers, the leaflets that the protestors were distributing sound very much what I am saying in these videos or what I write on the blog posts and it is really strange to read those words knowing that they are cited in a completely negative manner and they are the same sentences. That to my mind are very positive. One of the things that I didn't do and I couldn't do is to go to them and ask, Oh, I'm so sorry. Would you like some, some not only coffee, but maybe a hot soup or, or a coat or shoes.

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Because evidently if you negate the value of technology you go about your life without being able to leverage the tools that civilization provides us with without being able to leverage medicines and atrophy city and cars and trains and airplanes. Well probably that is not the case, which actually means that even people who are pretty fanatical and fundamentalists in their views to the point where they don't believe there is any value in a conversation, in a dialogue with those that don't think like them. Even those cannot do without technology, which is quite mind blowing if you think about it. Technology has so much value that even those who hate it must use it.

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Evidently the benefits of technology and the benefits of innovation are much greater than the downsides and the costs and the risks. Analyzing these benefits of course is easy when the facets of any given technology are well known and analyzing when and how that technology should be adopted is almost trivial from the point of view of a corporation that is too late. That is too far in the technology adoption cycle to represent any sustainable competitive advantage. The very reason why the implications are well understood is because a lot of other companies has have adopted the technology that is being analyzed and since a lot of other companies have adopted it, you will be at least middling, but maybe even a late adopter and you will not be able to gain any competitive benefit. The only thing that you will do is not fall further behind. This means that if you want to adopt technology in order to improve your competitive positioning, you must do so by definition at a point in time when

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not everything is understood around it. When the costs of the disruption that the particular technology will create haven't been fully measured,

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Where the risks are not completely modeled, where you cannot be sure that the exact path that you are following is going to be the winning one, so you will to take reasonable precautions, you will have to, for example, operate in an experimentation at small scale and isolation so that the potential fallout of an experiment that goes wrong cannot contaminate the entire organization. But also because by definition an organization that can afford to do these experiments will be as successful for one at confident. One an organization that is, since it is successful and confident in its own ways, we'll see somewhat myopically but unavoidably these experiments as wasteful and the organization, the immune system will try to stop the experiments if they are not protected against this immune reaction. Now, taking reasonable precautions is the keyword here because precautions

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And being risk averse can be definitely going too far. As a matter of fact, the precautionary principle that assumes that complex systems must not be perturbed because the consequences of our change are inherently unpredictable and we will cause chaos and we will end up with a negative balance between the benefits and the costs of what we are doing. This precautionary principle, in my opinion, goes to this access that we should avoid. It is somewhat tragic that the precautionary principle is part of those principles that the European union adapts as one of its founding principles. And as a consequence, the European union is very much risk averse, very much averse towards innovation. Developing, experimenting, deploying and adopting new technologies in Europe is difficult due to this original sin of the precautionary principle.

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The proactionary principle formulated originally by Max Moore is an alternative that aims to take into consideration the opportunity cost of not developing the innovative solutions that we may need in order to address the challenges we are facing that is advocating proportional measures, not absolute bands that advocates these experiments to preserve the freedom of individuals and organizations to trace their own path towards the solution that they are seeking. And to me it looks like that the proactionary principle is much more well adapted to the needs of our times than its predecessor, the precautionary principle. So as you go about looking at new technologies, you must keep these principles in mind and of course strike your own balance to understand how your organization is able to face the unavoidably complex consequences of the experiments that you will be executing.

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Traditionally the areas of knowledge are divided between things that we know, things that we know we don't know and the most frightening. Unknown unknowns.

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That is why these days when we are seeing an acceleration of technological change, open systems are much better to prepare organizations to understand the dynamic that they are exposing themselves to. Unknown unknowns will have a variable landscape and being able to compare notes and to share information about experiments and to leverage the knowledge and the practices of other organizations which open innovation and open collaboration promotes is going to be better than not failing in isolation. Cory Doctorow the science fiction author uses two side to the example of all chemists who would die in secret with the outcome of their mortally dangerous experiments as they were trying to transform lad in gold using various chemicals, often poisonous, but since they would be doing these in a very closed closeted, mystical and mysterious secret environment, nobody could learn from their mistakes. Unknown unknowns of course can be dangerous and being able to share the burden and share the

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risk and have peers in the adventure back to back trying to make sure that unknown unknowns don't kill us is the right way to go about all of this.

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Also because as we know with Jolting technologies, we cannot afford to wait. It used to be that we would say, Oh, exponentially accelerating technologies are progressing, match much faster than merrily leaner phenomena. We are accustomed to linearly changing systems, but now we must change our mindset to be able to recognize and adopt exponential technologies. But now, whether it is in a digital biology or it is in quantum computing or many other fields, we are seeing an increasing rate of acceleration. We are seeing jolting technologies, which means that we have even less time to wait on the sidelines until the features, the characteristics and the consequences of technologies are worked out. We have to be even more prepared to make mistakes and to make sure that we don't die from those mistakes. That we can learn from those mistakes as we are adopting these jolting technologies unavoidably, when we are successful in these experiments done in small scale and isolation per definition,

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if jolting success technologies are included in these experiments, they will explode in the view with a transformational power that will encompass the organization that created the experiment regardless of how isolated and how protected it is.

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And if it is successful per definition, this experiment will become the core of something new that the organization will morph into these days. Nokia is not an example of corporate success anymore, but what many remember, eh, even less is that Nokia was originally a finish maker of a completely different goods from the phones that we have learned to love them for the time that they were leaders of these phones.

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Or originally if I'm not mistaken Nokia was a local Finnish producer of boots and, and woodworks of, of various kinds, certainly not a high technology provider and, and many other companies have a different roots, eh, that make them unrecognizable to those that learned about them in their original embodiment. So as you adopt new technologies and as you carry out experiments, since you want those experiments, sooner or later to be successful, you have to be ready as an organization but also as an individual psychologically to fully embrace the power of that new thing that you become, that you become first as an organization. And then depending on the technologies that we are talking about, that you become as an individual as well. This is maybe one of the most interesting conundrums in my opinion, that the protestors highlighted and where their manifesto is still aligned with the things that I'm saying, except that they label it very negatively and I label it very positively. I not

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only believe in the positive value of change, I believe in the necessity of change. The alternative to the change basically for me is death to the individual and death to the organization because by being constant to itself faithful to itself, it makes itself unfit to exist and let alone thrive in a future that is coming rapidly.

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I hope that you liked this episode of the context. I welcome your questions and feedback. I am receiving quite a few pieces of email responding to the newsletter that I send or comments on the videos and I am very easily reachable on Twitter, Facebook my website via email. You can just use your favorite search engine and you can find me and you can get in touch with me. I am open to conversations, including with those that have opposing views to mind. And I welcome your questions and I hope that you enjoy this and the future episodes of the context. If you do, you can support it on Patreon for $5 or less so that I can produce the future episodes together with my team. I am receiving enhancement requests and we are working towards including illustrations, charts, sources, a higher production value in future episodes of the context as you requested. Thank you very much.

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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.
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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:
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Main Topical Clusters & High-Level Ideas
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.
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+     full table     AI: Reveal High-Level Ideas

AI: Summarize Topics   AI: Explore Selected

Most Influential Keywords & Concepts
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.

We use the Jenks elbow cutoff algorithm to select the top prominent nodes that have significantly higher influence than the rest.

Click the Reveal Underlying Ideas button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.
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AI: Summarize Key Statements   AI: Topical Outline
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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Structural Gap Insight
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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.
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Discourse Entrance Points
(concepts with the highest influence / frequency ratio)   ?
These nodes have unusually high rate of influence (betweenness centrality) to their frequency — meaning they may appear not as often as the most influential nodes but they are important narrative shifting points.

These are usually effective entrance points into the discourse, as they link different topics together and have high inlfuence, but not too many connections, which makes them more accessible.
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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.

Concept Relation Analysis:

please, select the node(s) on the graph or in the table below to see their connections...
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Use this feature to compare contextual word co-occurrences for a group of selected nodes in your discourse. Expand the list by clicking the + button to see all the nodes your selected nodes are connected to. The total influence score is based on betweenness centrality measure. The higher is the number, the more important are the connections in the context of the discourse.
Top Relations in 4-grams
(bidirectional, for directional bigrams see the CSV table below):

⤓ Download   ⤓ Directed Bigrams CSV   ?

The most prominent relations between the nodes that exist in this graph are shown above. We treat the graph as undirected by default. Occurrences shows the number of the times a relationship appears in a 4-gram window. Weight shows the weight of that relation.

As an option, you can also downloaded directed bigrams above, in case the direction of the relations is important (for any application other than language).

Text Statistics:
Word Count Unique Lemmas Characters Lemmas Density
Text Network Statistics:
Show Overlapping Nodes Only

⤓ Download as CSV  ⤓ Download an Excel File
Discourse 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.

We recommend to try to increase mind-viral immunity for texts that have a low score and to decrease it for texts that have a high score. This ensures that your discourse will be open, but not dispersed.
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.

We recommend to aim for Diversified structure if you're in the Biased or Focused score range and to aim for the Focused structure if you're in the Dispersed score range.

Influence Distribution
Topics Nodes in Top Topic Components Nodes in Top Comp
Nodes Av Degree Density Weighed Betweenness

Discourse Advice:
AI: Develop the Discourse
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:
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(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.
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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).
Compare informational supply (search results for your query) to informational demand (what people also search for) and find what's missing:

We will build two graphs:
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