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Welcome to this new episode of the context, I want to talk to you about sustainability in a thermo dynamic sense. thermodynamics is the physics branch studying systems from a statistical point of view, as they evolve through time, through their inputs of energy and how their behavior, it can reflect emerging phenomena that we will understand in our day to day lives, but that can be derived from understanding

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1:00

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The microscopic behavior of these systems. So for example, we talked about the pressure of a gas. But this pressure can be derived through the motions of the individual molecules composing the gas. And we can anticipate how the pressure is going to change if, for example, we increase the temperature in the chamber where we are measuring the pressure. Now,

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1:32

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when we talk about sustainability, what we represent mentally what we imagine is a system typically pretty complex, where we want to achieve some kind of equilibrium, some kind of balance between the inputs and the outputs. And we want to preserve the structures that have emerged

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2:00

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In this system, because we attribute value in them, and as a consequence, we want to see them going forward in time.

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2:13

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The paradox is that there are basically no closed systems that exist in the real world and that open systems are subject to an evolution. That

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2:35

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by definition negates the perfect balance that would be required if we were to talk about sustainability.

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2:47

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As a consequence, we can only talk about sustainability in a very limited sense, in a given area or volume in a given interval of time.

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3:01

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But as soon as we get out of that set of parameters in space and time, we will or serve, that the system actually that we believed was sustainable is not anymore.

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3:19

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Now, the conclusion of this reasoning that I will illustrate through examples soon shouldn't be that sustainability is a fallacy by itself or that it should not be pursued.

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3:39

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It means only that the ideal simplistic representation of sustainable systems that can be easily replicated and adopted the better in our approach to

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3:56

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understanding ecological systems or

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4:00

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in how we design and implement and modify and incentivize economic systems should be

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4:11

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improved, should be informed by a deeper understanding of what is and is not possible in the real world.

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4:22

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So, if we look at Planet Earth as the preeminent example of sustainability, we admired the complex and well balanced in ecology, the biosphere that Earth supports

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4:44

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and ride there, we can instead

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see

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4:52

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a very large number an uncountable number of examples of how that system itself

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5:00

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is not sustainable. And it is enough to start from the largest time intervals. And to understand that, yes, Earth started 4 billion years ago, more or less, and in a few billion years, it is going to be destroyed by the sun becoming a giant red star, if that will engulf Mercury, Venus and Earth itself. And as we understand its evolution, it will stop around or before the orbit of Mars. So, whatever Earth is doing, it is doing it in a specific time interval of let's say, 6 billion years, plenty of time admittedly, but still, it's still sustainability has limits

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5:59

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within

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6:00

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This time interval

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6:02

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the life forms that constitute the earth biosphere

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6:10

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have started relatively modestly in the oceans, but even then, they have catalyzed processes that had dramatic disruptive changes on practically every every component of the earth system.

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6:34

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oxidizing metals precipitating.

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6:40

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The dissolved metals in the in the oceans exactly because it the oxygen byproduct of life enabled this oxidization process in the

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7:00

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made the water in the oceans transparent, they were not transparent before and through the water becoming transparent, it enabled photosynthesis at a deeper depth that not just a very shallow,

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7:18

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millimeter level than before. In the very minerals of rocks in the Earth's crust are a consequence of microbiological life working on these rocks for billions of years, not all of them, but many of them would not be possible without life.

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The atmosphere itself is in a constant state of thermodynamic, this equilibrium, it is far from

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8:00

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From being chemically and thermodynamically in a stable state,

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8:07

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it is constantly reforming oxygen that is formed by our plants and plant ons and, and this oxygen

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8:21

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goes everywhere in the atmosphere. 20% of the atmosphere is oxygen forming

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8:29

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ozone in the highest layers

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8:34

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where sunlight is absorbed in its frequencies, where if they could penetrate the rays of ultraviolet light from the sun, that ozone stops would actually destroy the DNA chains that constitute life on Earth. So ever

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9:00

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everywhere we look, the solid, the liquid and the gaseous components of Earth are impacted by life, which is itself is in a constant state of change, far from being in balance with itself in its surroundings. This lack of being in balance is what separates

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9:29

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the individual organisms as well as the entire species to keep trying to survive, and the vast majority of the time failing to survive. This, being far from equilibrium is the very basis of of evolution, a perfectly stable environment with perfectly adapted, organisms would cease to evolve.

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There would be no reason to evolve, it will never push itself outside of the boundaries of its biological comfort zone.

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And

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10:12

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so, per definition, even though we are looking at nature, we are looking at nature, as if it embodied our ideals of sustainability and and balance and equilibrium.

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10:28

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It indeed represents the very opposite.

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10:34

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There are complex interconnected processes that

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10:43

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mutually support the

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10:46

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ecosystem, and these processes can be made more robust or can be impoverished or even interrupted. And when we observe

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11:00

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That, for example, in 10s of thousands of years ago, the Sahara Desert was green. Well, data is the observation of

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11:16

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a vast environment, dramatically changing. And or we could go even farther

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11:25

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back in time and look at Antarctica, that rather than being covered by a kilometer, the ice sheet, it was covered with forests. Or we could go back in time still further and stop at the ice bowl age of planet Earth, when the entire planet was covered with ice in life could survive only because of the freak out

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12:00

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accident that our universe exhibits that water is one of the very, very few substances we know where the solid state is lighter than the liquid state. So this ice that covered Earth actually protected a liquid layer where life could could survive. Imagine if that were not the case, if ice would form from the bottom of the oceans and keep growing, growing, growing until all of the water was frozen, and life could not survive under those conditions.

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12:45

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So, the processes of entropy and processes of complex structures far from equilibrium are intrinsic to

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13:01

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cosmology and planet formation, geology, biology, and pretending that we can and we should form

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human Greven

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activities that, contrary to all that precedent, can implement and preserve perfect

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13:33

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systems that are sustainable in the sense that their inputs and outputs are in balance with each other while expressing complex structures inside

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13:47

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is profoundly misguided. We have to recognize that starting from the principles of physics, starting from what it means to exist in our universe,

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14:01

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The complexity that we observe, we require in order to exist in that we express in our activities as we form society's economies. And as we look out in the universe to try to understand more of it and to hopefully form complex systems, outside of the gravitational boundaries of planet Earth itself, these complex systems will be necessarily and in perpetuity, far from equilibrium, they will not be

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14:43

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fundamentally sustainable. They will always operate in a state of striving to preserve themselves and oftentimes failing to do so.

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14:58

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Now, the

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15:00

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ability to understand how a system can react when under the stimulation of its environment, it is moved outside of the boundaries that previously it would comfortably and lazily pretend to control. That is of fundamental importance. And of course, in the past, the systems themselves had the feedback mechanisms in order to preserve their characteristics, their identity, they recognize ability. But these feedback mechanisms were relatively limited. Because if there wouldn't be any kind of look ahead with which the systems could recognize future states that could put them up

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16:00

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Outside of those boundaries where they could find the trajectory to bring themselves back into

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16:10

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a state where they would be recognizable, there could be simulations that pushed the systems so much outside of those zones of existence in their fundamental parameters, that there was no way of going back.

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And the lack of these

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16:30

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look ahead abilities is what causes, for example, biological evolution to be so incredibly wasteful, because it is completely unable to create systems that recognize if they are heading in a dead end. The the classic myth of the lemming

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migration

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17:00

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That just pushes thousands or millions of individuals off a cliff is a metaphorical representation of the blindness of purely biological behavior. And it is appropriately contrasted with the human ability to plan to forecast to observe future states and to classify them and to label them and to understand that certain behaviors, certain

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17:36

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characteristics of the trajectory of the complex systems, that that we can observe and model

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17:45

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are to be minimized because they would lead to the destruction of the complex system itself. So, we plan we plan for the future

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Have ourselves as individuals of our cities societies, and we should

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18:06

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do that planning much more effectively taking full advantage of all the tools and all the systems that we have available,

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when you hear some

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18:21

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accrued wisdom, to express that history always repeats itself, that we cannot learn and cannot improve.

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18:33

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That is a very defeatist position of people and and and wizened old sages who have actually given up who have

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18:47

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renounced the hope of perfectibility and and whether individually or as societies that leads to a self

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fulfilling prophecy where the lack of trust in the ability of the individuals to improve themselves and of societies to evolve in positive directions

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becomes reality. Because it reinforces those behaviors that instead should be kept in check and and minimize if, if at all possible. On the on the other hand, we are also in a situation where there are a wide variety of beliefs, behaviors and expectations towards the future. So, even though there could be some parts of

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19:56

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the planet that fall into this defeatist

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trap and ends up destroying itself. As of today, there is enough variety across the planet to hopefully that not happening

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everywhere. Now.

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20:21

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It has been the case that a lot of the biological processes were local in their expansion on a global scale would take a long time. When oxygen free oxygen wasn't available on the planet, we already had life forms that didn't need it for their own metabolism, an aerobic bacteria constituted for more than a billion years, the only life forms and paradoxically

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Because the

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product of their metabolism was oxygen, they were

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21:08

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creating it rather than absorbing it. It We are the result of the end game between

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21:18

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anaerobic life forms and aerobic ly forms were the second ones one, and the first ones lost their dominion over the planet. These still exist in niche environments where oxygen is not available, for example, in deep ocean volcanic vents, where they received the heat and the energy from these volcanic sources and is lacking oxygen they are the only ones that can survive and thrive in those environments. But

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21:57

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on the surface at the

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The

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22:01

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current generation for the past 3 billion years is the aerobic organisms using oxygen. So the past is transformations aware this passage has certainly required

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hundreds of millions of years for the Arabic to Iraq

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war to conclude in today instead we are in a situation where

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22:40

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very, very rapidly we can have effects on a global scale. The Industrial Revolution with large amounts of co2 emissions

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has changed the chemistry of the atmosphere, producing

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23:00

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Acid rains in in many parts of the globe. And as we reduce the sulfur content of the carbon, we burn those acid rains, these appear then forests could

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23:16

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once again flourish. In bonds, co2 itself still represents a problem, because we are producing too much of it. And yes, co2 has always been part of the atmosphere. And natural processes both produce it as well as absorb it, but it is enough to have a little bit extra every year. And just like when you eat too much, and you get fat and you didn't realize

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23:51

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day after day, week after week, month after month, but in the course of a year, you gain maybe two or three

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24:00

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three kilos. And in the course of 10 years, you become a BS, and then your metabolism your organism starts to break down. Similarly, even though over the course of a single year, the extra co2 we emit in the atmosphere may not be excessive. The fact that it keeps accumulating leads to a potentially fatal breakdown of processes that we need in order to survive.

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Another dangerously

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fatal or potentially fatally dangerous, global effect that human civilization must keep.

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Minimizing is of course, the eruption of nuclear war on a global

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scale.

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There are many movies that have been

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in,

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25:10

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written and then and then shot around the fall of the Soviet Union when the previously United Nation of the Soviet Republics divided into independent states, and there was a period when many of these states ended up having on their territory, nuclear bombs a day at

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the world didn't trust the local governments to

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save, keep or even not to fire in an act of crazy grander. And in these movies

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would represent those crises that could develop

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given the chaotic years after the breakup of the of the Soviet Union. Currently, all of the bombs are under Russian control. But

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26:19

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Have there been scripts? And have there been scenarios of what would happen if the United States broke up and the various states in more or less chaotic situations would end up having on their territories, nuclear bombs? The situation is completely different, of course, for many reasons, political, geopolitical, ethnic,

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26:52

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of governance and so on and so forth.

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But wetter, the United States

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is on a trajectory of decline and it is going to be able to get out of it or whether it is going to end up sunsetting as the last Empire of the 20th century on able to cope with the challenges of the 21st. It is certainly true that a global thermonuclear war is something that would put human civilization outside of the bounds of what is able, what it is able to withstand and recuperate healthily from

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27:34

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how much this is concrete. Let me illustrate it with maybe the last example of our open systems and permanent changes in our environment that these open systems represent in their unsustainable transformation. When technological human civilizations started

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28:00

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Let's say just to make it simple 10,000 years ago, as it went from stone tools to bronze tools to

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ever more

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technologically evolved the applications of metallurgy. It could count on the accumulation of these metals in what we then labeled our minds

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through hundreds of millions or billions of years of

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geological and biological processes that that

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made it so that these metals would be

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in a

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28:50

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relatively small place in a very high percent of

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28:59

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of the

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29:00

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In the Earth's crust, and so we would mined the ores that we would then refine using fire and other processes into

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29:11

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plows and swords and and everything else we needed nails and hammers and so on.

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29:19

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But if we would have to start over,

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29:23

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not so much luck anymore, all those mines are exhausted.

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29:29

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Today, in order to extract minerals from the Earth's crust, we have to resort to ever more sophisticated processes because the richest of the veins, that 10,000 years ago with the technological tools we had available then

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29:51

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we could take advantage of, those riches veins are gone. So, if we were to start over, we

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30:00

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would not be able to build the kind of civilization that uses metals in order to bootstrap itself through agriculture, in producing food that enables a percentage of the population to invent riding, to invent the scientific method, to invent philosophy and all the things that we leverage today for our current civilization.

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30:29

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Would we be able to find an alternative way of achieving it? Maybe, maybe not. Let's not find out. So,

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30:38

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to conclude,

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30:41

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sustainability is a goal that we have to recognize is unachievable. It has never been achieved anywhere, it is always just a limited outcome of a spacetime bubble that

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31:00

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we observe and we admire, but we cannot count on it. We have to strive constantly in a never ending quest, as our open systems, through the inputs of energy, produce the outcomes we desire. If you want to learn more about this topic, an incredibly powerful starting point is represented by the works of Ilya Prigogine. Ilya Prigogine was a Russian born chemist, who won the Nobel Prize for dissipative systems and dissipative structures, studying the thermodynamics and entropy, and how open systems can generate the structures countering the entropy increase that would suggest that these structures would break down and begone. So, I hope you enjoyed this and I will see you in the next episode of The Context.

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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:

N/A

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.
Generate AI Suggestions
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:
AI Suggestions  
×  ⁝⁝ 
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.
please, add your data to display the stats...
+     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.
please, add your data to display the stats...
+      ↻    Reveal Underlying Ideas

AI: Summarize Key Statements   AI: Topical Outline
Network Structure:
N/A
?
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.


 
Download: TXT Report  CSV Report  More Options
Structural Gap Insight
(topics to connect)   ?
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.
N/A
Highlight in Network   ↻ Show Another Gap  
AI: Insight Question   AI: Bridge the Gap  
 
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.
N/A
↻ Undo Selection AI: Select & Generate Content

Emerging Keywords
N/A

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):
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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

 
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...
+   ⤓ 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 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
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Text Network Statistics:
Show Overlapping Nodes Only

⤓ Download as CSV  ⤓ Download an Excel File
Discourse Network Structure Insights
 
mind-viral immunity:
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stucture:
N/A
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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.

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.

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Topics Nodes in Top Topic Components Nodes in Top Comp
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Nodes Av Degree Density Weighed Betweenness
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Discourse Advice:
N/A
AI: Develop the Discourse
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.
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.
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:
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 Google search results as a graph, so you can learn more about this topic:

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Discover the main topics, recurrent themes, and missing connections in any text or an article:  
Discover the main themes, sentiment, recurrent topics, and hidden connections in open survey responses:  
Discover the main themes, sentiment, recurrent topics, and hidden connections in customer product reviews:  
Enter a search query to analyze the Twitter discourse around this topic (last 7 days):

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Enter a topic or a @user to analyze its social network on Twitter:

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