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Welcome to episode two of The Context. Today I want to talk to you about Tesla and I want to tell you what is the context for what Tesla is trying to accomplish. Of course, all of us know about the cars and all of us know that they are trying to change the way transportation works, going towards electric transportation, away from internal combustion engines, ICEs. But, is there more? And especially in the way that they are combining the different components of innovation in hardware, the car itself, in software, the self-driving components, for example, autopilot and the future, full self-driving option, but also in the production process? Does this have implications that go beyond what we can hear on the Twitter streams or what the numerous deniers of Tesla trying to let us understand? Of course, the naysayers have it easy. Elon Musk himself repeatedly says what they are trying to accomplish is very hard, probably impossible.

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It is very likely that they will fail. Now, I don't know whether that is what he says to his investors when he's asking for money to invest in the company, in new models and new production lines. But obviously, he's right. The risk associated with creating a new car and new car factory, entire new methodologies and new software and everything else is extremely high.

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odayI am actually talking to you from Kilometro Rosso in Italy, a cool science and technology park near Bergamo, itself near Milan in the north of Italy. And one of the companies at Kilometro Rosso is Brembo, a world leader in braking systems and also a Tesla supplier. We just finished visiting the Brembo factory. Actually here is mostly an R&D center rather than a place where they are alos producing because the production has to be closer to where the large volumes of the cars are being produced: in Germany, in Spain, in China, in the US.

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So back to Tesla. The car itself is designed so that it can last much longer. The car itself is designed in a manner that together with the software, what they are about to launch, the Tesla network rather than depreciating constantly right after you bought it. It is supposed to be able to keep generating value generating revenue and as a consequence maintain Its value much longer in time. So the electric car is itself cheaper to maintain than a traditional car. There is no oil change. The number of mechanical components that can break and need to be replaced is much lower, the, the brakes. Importantly, using regenerative braking when possible last much longer and so on and so forth. Tesla actually says that they are aiming to build cars that can last for a million miles with a relatively low maintenance. Already today, the software in the car is such that there are all kinds of functions that are not available in normal cars.

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Whether it is the autopilot system that today very importantly, requires the driver to pay attention to what is going on. But in the future, it is expected to be constantly upgraded with features that will allow increased degrees of autonomy to the point where according to Elon Musk in a couple of years, maybe it will take four, doesn't matter. It will become capital, a complete autonomy, full self-drive so that the, even the steering wheel can be removed from the car to features that are more strange like the dog mode. When you can leave your pet in the car and the display will show that there is a pet in the car and then the car is keeping the interior temperature cool enough for the pet to be comfortable so that if the car is parked and somebody, while you are shopping or somebody comes by, the will not freak out.

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Oh my God, the dog will die. But they know that the dog is comfortable in the car. So some of these software components are going to enable soon the launch of the Tesla network that will directly compete with Uber, with Lyft, and with the other car-sharing networks. But the important difference that the Tesla network will arrive sooner rather than later with them to the point and all of the Tesla owners will be strongly incentivized to make their cars available for the Tesla network. Also, this will be an integrated network, just like Tesla is a vertically integrated company, the Tesla Network will be for Tesla cars only and Tesla will be able to control the features at the very fine-grained ability that is not necessarily available to other providers of car sharing platforms. But now what happens when a car becomes available for the Tesla Network?

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Well, isn't it the case that we like driving, but as soon as we started driving more than an hour, maybe two per day we started complaining about it and a car can be shared, maybe among different members of the family. But if you are using your car to commute to work, then definitely your car is not available at home. Why Youth is away waiting for you to come home at the end of your workday. So many families by multiple cars, but in the end, our cars today are sitting 90% and more of the time. Imagine what other important investment is so low in its rate of utilization. 90% of the time it's doing nothing. You are using it only 10% of the time.

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If you leave your home most of the day and you only go back to sleep, even that has a rate of utilization of over 30% if you sleep eight hours out of the 24 every day. So today we are seeing a lot of cars standing on the roadside doing nothing. We see them in parking places and in cities where a lot of the city is designed around cars and car owners, people like KA, Los Angeles, in California, in the United States of America what we are is that a large surface area of the city is dedicated to the cars. Estimates vary, but something like 30% of the city's area is dedicated to the cars rather than to people. Now, with car sharing, the rate of utilization on the car can go up. If you went to work and you know that you are not going to use the car until you finish working, you can make the car available for others to use.

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If you are at home and you know that you are going to stay home for the weekend and you are not going to go shopping or something else, you can make the car available for the Tesla Network and this will increase the rate of utilization of the car and the benefit to you as an owner is to be able and earn additional income and to lower the cost of ownership of the car through this. Now the estimates, of course, are just that we don't know yet. We haven't been able to see the details. We don't know what the market will bear in terms of the per mile cost for driving a Tesla or having a test drive. You once full set of drivers available. But what Elon Musk boldly declared is that he believes that there will be about $20,000 additional income generated by each Tesla car per year due to the Tesla network operations.

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There will be also some additional costs for sure, but if the car last 10 years which is perfectly achievable under the current assumptions then there is $200,000 that is going to be generated by the car participating in the Tesla Network that the cost of a Tesla is going down the first model, the roadster, the second model, the Model S and then the additional models constantly cost less and less. That is how Tesla was able to bootstrap its operations. First they were targeting only those fanatic enough to buy an electric car that was very exotic and only after a while more or less 10 years they aimed to produce a version that was affordable and they knew they would be able to produce in large numbers, but that these likely the cost, at least the ability to produce low-cost cars is going to continue in the future.

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Models that haven't been even announced yet may cost not 30, 35 or $40,000 like Model 3. The current lowest cost models for Tesla, a class 30, 25 $20,000, maybe even less. Now, the vast majority of the Tesla cars are sold under a leasing contract, which means that you put some money down and then you pay a monthly fee and then after three, four years there is another large chunk of money in order to own the car outright. And very interestingly, Tesla already declared that in the United States these final amount is not going to be available in the option of the leasing agreement. It will not be a lease with an option as it is technically said, but when the leasing contract expires and you will have to return the car to Tesla itself because they will put in the Tesla Network after cleaning it, refurbishing it, checking in.

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So if you pay, let's say $10,000 initially and, $300-$400 over the course of the next three years, that is an additional, let's say $10,000 to make it simple. That's $20,000. That Tesla having you pay in order to get a used car to be put in the Tesla network. But if the car is able to earn $20,000 per year in the Tesla network then there is no reason to sell you the car at all as long as Tesla is able to afford in terms of the capital needed to produce the cars and without having you subsidize the production of the cars with your initial $20,000, and they can put new cars in the Tesla network, as soon as that point is reached, basically you won’t be able to buy a Tesla anymore because all the new Tesla cars are going to go and replenish and extend the network for car share.

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Now, does that mean that you should go out and buy a Tesla right now because they won't be available in the future anymore? You may want to think about. Even though if the Telsa Network is available, it means that you won't need to own a Tesla because you will be able to just have one anytime you needed it. But it could be an interesting proposition for somebody. Not only one, but if they can afford it, maybe owning more than one and then I have a mini network of their own under Tesla's control of course, but benefiting from the additional income generated by those cars. Now, this may be surprising, may be an unexpected consequence of the context about Tesla. I want these videos to be not much longer than 15 minutes, but there is definitely another part of this that I want to add.

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And then you tell me if you want to hear more. If you are interested about Tesla, I am fascinated by it and there is more to say, but let me just give another component. What if the product of Tesla rather than the cause themselves is actually the Giga Factory. Elon Musk already said that in order to achieve their goals, he believes that there are going to need to be approximately a dozen Giga factories in the world. When the first was built, Tesla single-handedly gobbled the production of leaping iron batteries off the planet. These are all the batteries that they need and current and near term Tesla cars. And they knew that unless the two things in their own hands, the current supply wouldn’t be enough for what they needed. But what they also did, very ambitiously, maybe a little over ambitiously initially and they had to pull a little bit back, is an automated system for turning role materials into the product that is the car.

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And within the Giga factory, they tried to do as much as possible from the working of the sheet metal to the production of batteries, the assembly of course, with as high as speed as possible in order to meet the volumes that the market can bear. The ability to design and deliver at the fastest rate a Giga factory is itself part of an industrial process that is improving. Giga Factory 3in China has started its construction in January. I was at a conference in January in Puerto Rico about artificial general intelligence to see if it, the insecurity organized by the future of Institute, which is financed by Elon Musk. And he was supposed to be there, but he wasn't because he was at the groundbreaking of the Giga factory in China. And a lot of naysayers said, oh my God, that started but it will take more than a year to finish.

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Or maybe it will be never finished. But actually, within six months, the construction of the Giga factory 3 has been completed and now it is being filled with all the machinery that is required to produce cars in China. Actually, Tesla is the first car manufacturer that has been allowed to own 100% of their operations in China. Rather than having, being forced to set up a joint venture, and the local production, of course, we will eliminate any, tarrifs, any import duties and these will allow Tesla to supply the largest market of electric cars in the world with the Tesla models that will be available. So back to the Giga factory, accelerating the rate of innovation in the production process itself and turning the factory into a product that can be replicated at an increasing pace, at the decreasing costs is, I believe, a very important part of the Tesla master plan.

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And may become something that we hear more about as potentially, the same concept will be applied to other products. As a matter of fact, and if you are interested, I will record new episodes of the context talking about this, already, Tesla, more than just cards. It has batteries for storing solar energy, solar panels to generate solar energy, solar groups that can be incorporated in newly built homes so that solar generation is part of the home from the outright and producing at scale these other products. And maybe future ones will definitely be part of the flexibility and the power of the Giga factory as a product. So, thank you for watching this second episode of The Context and please let me know if you liked it. Let me know what your questions are both about this as the previous one about Libra, or what are the things that you believe are intriguing, but you would like to hear the context. You would like to hear them put them in context. The next episode of the context will be

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out in a week. And we will talk about 21st-century life design skills and why they matter. And what are the political implications of the emancipation of the individual that is promoted by an increasing ability, of feeling empowered by technologies that are available to anybody. And we have derived the kind of knowledge rather than disenfranchised and disempowered. So come back for the next episode 3 of The Context. Thank you.

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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:

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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:
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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):
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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.

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

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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...
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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 / 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:
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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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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:
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