David Orban 1:25
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self driving cars have been part of our imagination and technological aspirations for a long time. Now, that they are here. The question is not if their features are ready for us. But if we are ready for them. My name is David Orban, and this is the context. Season 04 Episode 05 over 10 years ago, the Department of Defense of the United States organized a contest for cars to drive over 100 miles without a driver across desert to rain. And a lot of challenges. The first edition of the contest was a complete failure. The cause couldn't even progress. For the first few miles before succumbing to the adversities of the terrain. Second was better. And then the third, the session. It was really a trial of a specific theme from Carnegie Mellon University, led by Sebastian Thrun won the context. And it has been. Then a broad abroad by a
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specific team, led by Sebastian Tron won the contest, and was welcomed by Google to lead the self driving car initiative within the group that became X, the laboratory for the moonshot projects at alphabet. Since then, over the course of the next 10 years, many teams have invested billions of dollars from Uber to the Google self driving car project which then became vaimo to Tesla, and many many others in order to deliver on the ambitious project of creating and then spreading all over the world. Cause that didn't require a driver. Regulators have issued to somewhat arbitrary and relatively useful set of guidance reference instructions. For example, that there used to be there has to be a series of autonomy levels. But the car is level 1234 Or five autonomy, where the first levels are merrily Lane Keeping assisting the driver. Level four means that the driver is actually not needed but still has to be in the car and level five, is when the car doesn't actually require a driver
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anymore. Currently, and many of you may not even realize there are already level five, autonomous cars on public roads in the world, not everywhere, but there are. And this is an important point of principle, because a lot of people have maintained that dogmatic position, that this would never be achieved. Well, never say never. And indeed, in Phoenix, Arizona. You can now summon a self driving car. Climb on board, and there will be no one else but you. You will be sitting in a back in a car will bring you on its own to your destination, as if it were a taxicab, of the past decades, but there is no taxi driver. So, of course, Phoenix Arizona is a very specific location. It very seldom rains. It never snows. The urban grid is very regular, and Wei mo invested a lot of money in mapping the city, at the very high resolution, and it is constantly updating the map so that the car is aided by this high resolution knowledge, digital knowledge about the world, and is able to compare that
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knowledge with the reality that its sensors are acquiring the reason why it matters that this car or this service exists, because it will never be worse every day, every month, every year, it will only get better, both from Wei Mo, as well as other providers. Tesla is expected to release their full self driving software upgrade shortly. First, to a set of beta customers, and then to an ever growing number of people, to the point where, in a year or two, it will release, what it calls the Tesla network, its own fleet of self driving autonomous cars, which will be available for a single trip to be rented by anyone. Hailed as if it were a taxicab,
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and many others will follow. The self driving car is not a dream anymore, it is a reality of our society today. So, it is time. It is urgent that we not only think about the technological hurdles that can make this a reality. But the sociological and regulatory hurdles that eater, can make the spreading of self driving costs much slower than otherwise it could be, or actually generate a lot of unexpected side effects that if we are not able to foresee can actually slow down even further, or reverse this important technological development. Why are self driving cars, so important. The most important factor is the ability to eliminate human suffering, all over the world. More than a million people die every year in car accidents, and the vast majority of these accidents is due to human error. If we are able to have cars drive themselves and progressively, eliminate human drivers, the source of these errors will be eliminated, and it is expected that the vast majority of these accidents
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will not happen. Hundreds of 1000s, and soon millions of people without even realizing will not have to die in car accidents. And even when someone doesn't die. There are 10s of millions of people who suffer very disruptive, very painful. In these abilities. Due to the accidents, and the possibility of their full recovery is not at all guaranteed. Those will be avoided as well. And as a consequence, the moral calculation of accelerating the rollout of self driving cars is. In this respect, absolutely clear. Now, what can make individuals and society. The hurdle for the rollout of self driving cars. When the first cars were introduced in urban streets, or the omnibuses the trams, that preceded them. Well, we realized pretty soon, that the cities were not ready, people were not ready. It used to be the case, somewhat ridiculously looking at it from our current observation point that a person would walk in front of the tram the omnibus with a flag, alerting the others, that the strange
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thing was coming, so that they wouldn't panic, they wouldn't behave strangely, they wouldn't put themselves in danger. Is someone like that going to be necessary for self driving cars as well. We have developed over the course of many decades of instincts towards how we have to behave in front of cars driven by humans. We for example realize that there is a very natural reaction time below which the driver cannot go. Even if we have the right of way on pedestrian crossings. If the incoming car is close enough. We are not going to commit suicide in voluntarily by stepping off the curb on the the false assumption that the human driver is going to be able to break in time, without taping hitting us. Now, similar but different reflexes and behaviors will be absolutely needed with self driving cars going to be
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more and more frequently among us, in the cities. There is a constant communication between drivers and pedestrians and among drivers themselves. When you cross the eyes with with someone and you signal them either, either with a nod or with your hand. That indeed you let them pass. Or you flash your headlight, or you move the car just a little bit. All these rich components of non verbal non textual communication are enhancing our ability to negotiate and navigate modern urban traffic. Almost all of this is unavailable and accessible inexpressible by self driving cars. So, we will have to understand how to communicate with this strange alien artificial intelligence that is going to be soon. Living together with us on planet Earth. We will have to be able to understand their intended behavior, and it is going to be quite important because it doesn't matter if we are right misinterpreting what they are about to do is going to be quite dangerous, equally dangerous, as if we made these
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interpreted the intention of a human driver. Now, self driving cars of course can be cautious. They can even be extra cautious. They can be excessively cautious, for example, in respecting speed limits. But if everyone on the highway is going 10 miles faster than the speed limit, suddenly finding in front of your car that is respecting the speed limit to the digit can be dangerous to you have to brake and the car behind you have has to break, and the self driving car without intending to. Without even realizing could cause an accident. By following the law to the letter. So will we have to train self driving cars to break the law, if necessary. If everyone else 1000s of cars on the highway, are breaking the law because they are going 510 Miles More than the speed limit. Will the self driving car have the freedom to do the same.
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Unknown Speaker 16:23
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And if we do,
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David Orban 16:26
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will we start examining other laws and progressively teach the self driving car, walk laws to obey, and walk laws to these obey. Are we going to train a specialized AI to be rebellious. Society has to get ready, self driving cars are here, individuals, communities, regulators will find themselves with an alien, artificial intelligence in desperate need to communicate and to understand its needs, or intentions, its objectives, our goals and lives will depend, and the future of humanity will depend on our ability to achieve this. Thank you very much for following this episode of the context. If you like what you have heard, you can support the context, as well as all my other activities by becoming a supporter or a sponsor or a benefactor. On patreon.com slash David Orban. See you at the next episode.
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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.
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:
? 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.
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.
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.
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:
? (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.
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.
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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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.
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Sentiment analysis works for English language only. Contact us @noduslabs to propose a language and to get updated about the new features.
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Main Topical Groups:
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+ ⤓ ? ⤓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.
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.
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.
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Structural Gap
(ask a research question that would link these two topics):N/A
?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.
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.
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) ?
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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).
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):loading...
⤓ ? ⤓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.
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):loading...
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
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:
Top Relations / Bigrams
(both directions):
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).
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).