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The context series, season 04 Episode Two living with AI's.

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Your life is influenced by technology and the new components that you have to take into account is artificial intelligence that is going to permeate what you do, and learning to live with AI's is a skill that you must acquire in order to understand the world of today and tomorrow.

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Artificial Intelligence is a technology that has been simmering for decades and promising incredible breakthroughs described by science fiction books and movies, we have all become accustomed, kind of discounting it, where we thought that it would come. But we wouldn't take into account that actually, we would have to really understand it, and take it into account in our own lifetimes. Well, we cannot discount it anymore. AI is here. And today is the day with the least amount of artificial intelligence around you, every day, From today onwards, is going to have only more of it. Now, what happens when your world begins to be permeated, and then little by little potentially saturated by artificial intelligence is counter intuitive. Because we have been accustomed to seeing the depictions of AI in software or in robots as very obedient. And well, if we wanted to show the conflict, of course, rebellious, but still a with not many surprises. There are just a few exceptions of wonderful

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books and movies that are able to really represent the radical kinds of surprises that it is likely AI is going to bring into the world. Just to give a couple of examples. If you haven't seen the movie her, I greatly recommend it as well as x Mark inner ear, these are two movies that have a clear message and an important message of how artificial general intelligence is not something that we will be able to control. Now, we are admittedly not there yet. And we don't even know if it is going to take 10 years or 100 years before we develop an AGI. But there are fewer and fewer people who believe that it is not going to be at all possible at any time in the future. And among the experts, there is a growing consensus that not only AGI is are possible, but that we are assembling the tool set that it is that is going to make it practically possible as well in terms of both software and hardware. In the meantime, what can you do How can You prepare for that world? Well, even the narrow AI's

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that are around us contain already seeds of surprising types of behavior, either on their side or the behaviors that elicit that they elicit on our side that we can try to understand it and that we can either keep or substitute with better types of reactions that are fruitful, that are leading to desirable outcomes. And what is a fairly common, almost recurring conclusion in the use of these AI's is how adaptable human beings really are, how quickly we realize the parameters under which a certain system functions. And then we are very fast in

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using those parameters to our advantage. A good example of this is speech recognition, where we have been using now speech recognition systems for 2030 years, and they have become ever more performing. But still, if you correctly announced the eight, you will have better results. If you mumble. Or if you speak in truncated sentences that don't very much make sense. Well, you will find out that either the system is unable to recognize you, or when you read what you have been saying, well, the difference between what you wanted to express and what is actually on the screen or on paper will be very, very evident. So many of us have learned to speak clearly, and to formulate sentences that are concise and meaningful through the help of this particular AI. Another example is the concern, quiet, reasonable and substantiated concern of the degree of bias that AI systems x hibbott. Because of the particular training, model, and data sets that they grew up, learning from, and these biases are

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actually an excellent opportunity to understand that it is not the AI's fault. It is the collection, analysis and representation of our reality. That is the source of bias. Our society is biased, our behaviors are biased. So if the AI inherits this bias, actually, it gives us an opportunity to correct that bias. And it shouldn't be corrected only at the level of the AI. It should be corrected at the source, we should. And we can take advantage of that opportunity in order to go back and ask ourselves, how can we make our society in its entirety in the physical reality of our world less biased. And under challenge that AI today has is that the artificial neural networks that are a kind of automatic programming, that is able to take into account all the parameters that need to be optimized for and it exhibits the desired behavior, but at the cost of becoming a black box, including for most of the times the programmers that designed these learning systems is the explain ability and we

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have to be able to under Why the system behaves in a certain manner, both in order to improve its behavior, as well as the necessity to be able to justify the decisions to the people impacted by them. In Europe, for example, this is imposed by law, you cannot tell someone that you decided in a certain way because the computer told you so. And this is also an excellent opportunity. Because yes, it is the case that the behavior of so many of our systems, independently of being based on AI or not, is unexplained, maybe non unexplainable at the source, but we don't bother to make explicit and understandable and accountable the set of rules that are already governing complex systems and complex decision making in our societies.

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The ability of AI to act, promoting ethical systems and ethical decision making, ultimately, is summarized in the expression AI for good, we have the ability to choose, every time we adopt and deploy advanced technologies, what we want to do with them, and AI for good is the ultimate desire to make sure that the powerful technology of artificial intelligence is going to be deployed in a manner that benefits humanity that inclusively promotes the opportunities of everyone. And the way that you should be able to take advantage of artificial intelligence is corresponding to the root of your adaptability of your access to tools of your interpreting the world taking advantage of AI today is a stepping stone to be able to coexist with AI in the future.

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an AI that is explainable an AI that is Hello.

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Okay. an AI that is explainable an AI that is trained on unbiased Data is going to be applied towards for Cody yo

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current editor: ?
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      network structure:
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      Network Structure Insights
       
      mind-viral immunity:
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      stucture:
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      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.

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      Narrative Influence Propagation:
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      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.
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      Main Topical Groups:

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      +     full stats   ?  

      The topics are 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 and are given a distinct color.
      Most Influential Elements:
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      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:
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      Reset Graph   Export: Show Options
      Action Advice:
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      Structural Gap
      (ask a research question that would link these two topics):
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      Reveal the Gap   Generate a 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
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      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
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      Evolution of Topics
      (frequency / time) ?
      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 frequency of occurrence.

      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):
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      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):
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      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

      Sentiment analysis works for English language only. Contact us @noduslabs to propose a language and to get updated about the new features.

      Network Statistics:
      Show Overlapping Nodes Only

      ⤓ Download as CSV  ⤓ Download an Excel File

      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 as it allows us to better detect general patterns.

      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).
      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.
      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 the search results as a graph, so you can learn more about this topic:

       
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      Enter a search query to analyze the Twitter discourse around this topic (last 7 days):

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