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About this Context Graph:

total nodes:  extend
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.

at the conference organised by Samsung called safe on the future of electronics manufacturing. Raja kaduri, chief architect of Intel delivered a wonderful keynote that illustrates the power of thinking in terms of jolting technologies. Rajon provided a compelling picture for delivering 1000 fold increase in the power of AI systems within the next five

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This translates in an average doubling over the course of these years, every six

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But the way that he broke it down is completely different. Intel is not a totally vertically integrated company. It does depend on the effort of an entire rich ecosystem of participants, each contributing to Intel's ability to successfully deliver on its promises. And the advances that are needed are really breathtaking in various parts of the electronics manufacturing process in terms of what is the feature size, and what is the interconnectivity of the transistors, what is the topology, a meaning the way that the transistors are placed not only in two dimensions, but in three dimensions, in order to keep improving their power. What is the software that is used both to design layout, but also to improve connectivity, communication, reduce power consumption, and many other ways. The presentation should showed how each of these relatively independent components can be improved fourfold. And since the improvement, four times of each multiplies with the improvement of all the others,

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four times four times four times four times four is 1024. And that is the relatively simple elementary calculation to deliver at the end, the thousandfold improvement, this is many times faster than Moore's law. And the opportunity is to make sure that the risks are mitigated, and that the objective can be achieved. Because each of these components, of course, has its own set of challenges to overcome. And it may successfully do so or fail to achieve the full objective of four times improvement. But for example, in software, there is a potential of doing much more than four times. So Roger is confident of the thousandfold improvement. And that is the confidence that he aimed to instill in all the ecosystem participants that were invited to believe in this objective themselves. Because whether we are talking about Moore's Law, or the increasing acceleration of jolting technologies, these are not natural laws. And they only happen as self fulfilling prophecies. When thousands of

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engineers all around the world corralled by exciting perspective and the basis of objectives as it was the case of these, this keynote are springing into action. They try out the various ways that they can deliver the improvements that are required. The cumulative effect is astonishing, because we have already seen what really major performance improvements can achieve on various fields, like

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face recognition, speech recognition, but also not only recognition, but synthesis, video synthesis, text synthesis, and we are seeing how complicated the picture that emerges is in the ways that the world is transforming as a consequence of these actions. AI is going to fully take advantage of this actually, it is a classical picture, like we are seeing in other areas, for example, in the speed of internet connectivity, the more bandwidth is available, the more we want to use. For example, I'm already recording these videos in Ultra High Definition 4k a couple of years ago in Japan, well, if the stores they already had 8k television sets, am I going to record the next season of the context in 8k rather than

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Maybe or maybe 16. And then of course, some of that resolution is thrown away. So that during the editing process, there can be some panning or cropping as needed. But I am already streaming in HD High Definition 10 ATP, am I going to start streaming in ultra high definition in a years time or two years time? And then in eight K and 16 k? And you will ask yourself, Well, what is all that resolution even need it for? And I can answer to you easily. It is for example needed for immersive virtual reality, which very simply and straightaway doubles the resolution requirement, because you want to feed each eye the maximum resolution available. So if you want to achieve the ability to have an immersive experience, that is high definition, you do need Ultra High Definition bandwidth available. And that is the quantity of information that you want to send. So this is an example of expanding capabilities and expanding needs. Tracking each otter AI is the same. The question that is still open.

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When are the models of neural networks going to start delivering a diminishing return on the increasing number of parameters with which we are training them and allowing them to deliver their magic? For the moment it looks like the more we increase the number of parameters, the more capital the neural networks become. So there is a constant hunger in order to be able to do so. But we don't want and we don't need AI to be only centrally managed in the data centers, we can end will deliver AI on the edges as well. Already, the latest generations of smartphones with their advanced software are capable of user independent large rocket military, high performance speech recognition without internet connectivity on the phone itself. And the Intel keynote highlighted the fact that further advances in AI capabilities on the edge will require a massive increase in performance which they are ready to match. They are ready to ship in the course of the next four or five years. So, Intel is

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completely on board with jolting technologies. Rather than matching the traditional speed of Moore's Law doubling every two years, they are talking about doubling every six months, on average, over the course of the next five years in order to achieve the thousandfold increase in AI compute performance, that is the rhythm and the goal, and corralling cry to battle for their entire ecosystem. So, it is going to be fascinating to watch how this is going to come about. And of course, to watch how Intel's competitors, AMD Nvidia arm are going to independently lay down their own jolting strategies. Nvidia, for example, is talking about a doubling time of two months, not six. Is it going to be able to keep that self fulfilling prophecy through over the course of several years.

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It is hard to wrap our heads around the implications. But one thing is sure it is important to change the mindset. It is not enough to think about exponential anymore. The increasing rate of acceleration of technology innovation, as implemented in products and services across an entire industry has become the dominating paradigm. jolting technologies must be understood, adopted, implemented in order to be able to lead over the course of the third decade of the 21st century.

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Language Processing Settings:

language logic: stop words:
merged nodes: unmerge
show as nodes: double brackets: categories as mentions:
discourse structure:
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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.
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Main Topical Groups:

please, add your data to display the stats...
+     full stats   ?     show categories

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 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   ?

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 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
(less visible terms that link important topics):

These are the latent brokers between the topics: the nodes that have an unusually high rate of influence (betweenness centrality) to their freqency — meaning they may appear not as often as the most influential nodes but they are important narrative shifting points.

These are usually brokers between different clusters / communities of nodes, playing not easily noticed and yet important role in this network, like the "grey cardinals" of sorts.

Emerging Keywords

Evolution of Topics
(number of occurrences per text segment) ?
The chart shows how the main topics and the most influential keywords evolved over time. X-axis: time period (split into 10% blocks). Y-axis: cumulative number of occurrences.

Drag the slider to see how the narrative evolved over time. Select the checkbox to recalculate the metrics at every step (slower, but more precise).

Main Topics
(according to Latent Dirichlet Allocation):

LDA stands for Latent Dirichlet Allocation — it is a topic modelling algorithm based on calculating the maximum probability of the terms' co-occurrence in a particular text or a corpus.

We provide this data for you to be able to estimate the precision of the default InfraNodus topic modeling method based on text network analysis.
Most Influential Words
(main topics and words according to LDA):

We provide LDA stats for comparison purposes only. It works with English-language texts at the moment. More languages are coming soon, subscribe @noduslabs to be informed.

Sentiment Analysis

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

The approach is based on AFINN and Emoji Sentiment Ranking

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

Keyword Relations Analysis:

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

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

⤓ Download   ⤓ Directed Bigrams CSV   ?

The most prominent relations between the nodes that exist in this graph are shown above. We treat the graph as undirected by default 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).

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

⤓ Download as CSV  ⤓ Download an Excel File
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