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The Dataome: The Energy Intensity of the Digital World
Key takeaways:
• The generation and usage of digital data requires a significant amount of energy and resources.
• Silicon chip production is an energy-intensive process due to the creation of ordered structures from disordered material.
• Efforts to generate electric power for the current informational world are hindered by the fight against entropy.
• The energy requirements for computation, data storage, and data transmission are increasing exponentially.
• Without significant improvements in efficiency, the energy needed to run our digital data homes may soon match the global civilization's total energy usage.
Transcript:
Speaker 1
Its everything, right? It's this conversation in recording to yr bits. It's the information that went to and from your phone when you picked it up in the morning. It's the video you made. It's all the financial transactions, it's all the scientific computation. And that, of course, all takes energy. It takes the construction of te technology. In the first instance, making silican chips is an extraordinarily energy intensive thing, because you're making these exquisitely ordered structures out of very disordered material. And so there too, we go back to simo dynamics. And you're fighting, in this sense, against entropines. In a local fashion, we're having to generate electric to power current informational world, that piece of the data. And the rather sobering thing is that already, the amount of energy and resources that we're putting into this, it's about the same as the total metabolic utilization of around 700 Million human and if you look at the trend in energy requirements for computation, for data storage and data transmission, the trends all upwards. Its an expedential curve. And they suggest that perhaps, even if we have some improvements in efficiency, unless those improvements are then in a few decades time, we may be at a point where the amount of energy, Just electrical energy, required to run our digital data home, is roughly the same as the total amount of electrical energy we utilize as a global civilization at this time.
Speaker 3
The
Caleb Scharf on the Ascent of Information — Life in the Human Dataome
COMPLEXITY: Physics of Life
People have more accurate models of people in close proximity than they do of people far away (socially)
Summary:
People have a good understanding of their friends and are accurate in predicting their behavior.
This is shown by their ability to accurately predict election results based on their friends' voting preferences. However, biases arise when people are asked to judge unfamiliar populations.
These biases can be attributed to the structure of their personal social networks.
The more biased their social networks are, the more biased their estimates of the general population will be.
Transcript:
Speaker 1
Oh yeah, after seven years of research on this paper, that people actually have a quite a good idea about their friends, family, acquaintances, people that they meet on every day basis And then we'd whom they need to cooperate with, learn from or avoid. And that they're actually not that not as biased as a traditional social psychology would like us to think. And we see that because when we ask people about their friends, we see that this predicts societal trends quite well. So in one line of research, we asked a national probabilistic sample of people to tell us who their friends are going to vote for. We average those things across the national sample and got better prediction of election results than when we asked people about their own behavior. And this would not have happened if people were biased in reporting their friends. They must have told us something that must have given us information that's accurate and that's goes beyond their own behavior in order for that to happen to predict the elections better. And by now we saw that in four further, so we five elections all together in the US 2016 in France, the Netherlands, the Sweden and US 2018, and we hope to predict again 2020. So things like that tell us that people are actually pretty good in understanding their social circles and then the apparent biases show up when people are asked to judge people that They don't know so well. So when I'm asked to tell you something about people in another state or another country or people from another socioeconomic cluster, which I don't know well, then I am likely to have Some biases. But these biases we show can be explained by what I know about my friends. So if you ask me something like that, I will really try to answer your question honestly. And to do that, I will try to recall from my memory everything that I know about our social my social world. But you know, if I'm surrounded by rich people like here on the East side of Santa Fe, it could be very difficult to imagine in what poverty people can live in other parts. And so even if I'm trying my best to recall, you know, the most poor person I know, I might never recall such poverty that actually exists in the world. And when asked about the overall level of income in the US, I'm likely to overestimate the overall level. And similarly, if you are poor, you're people who are poor might have problems imagining the wealth of really rich people and they will typically underestimate the wealth of the country. So okay, so let me let me summarize this. So this piece actually suggests that people are not that biased when it comes to judging their immediate friends. They have a lot of useful information about their friends and pretty accurate. The bias is show up when people are asked about other populations that they don't know so well. And they can be mostly explained by the structure of their own personal social networks. The more biased your social networks are, the more biased your estimates will be about the general population.
Mirta Galesic on Social Learning & Decision-Making
COMPLEXITY: Physics of Life
Balancing Intellectual Exploration and Action
• There is an anti-pattern in certain podcasts that overemphasizes intellect and underemphasizes action.
• Consuming knowledge from brilliant people can be stimulating, but it may lead to overthinking and under-practicing.
• It is important to balance intellectual comprehension with taking action, initiating projects, and practicing.
• Encouraging agency, initiative, entrepreneurship, and proactive energy is crucial.
Transcript:
Speaker 1
One piece of the puzzle, I think, is that there's an anti-pattern of podcasts, especially in the game, B space and related sort of sense making intellectual philosophical spaces, Which is I'm concerned about an overdoing the intellect and an underdoing the action. You know, there's all of the people that you interview on your show. They're brilliant people. You know, and it's like, every time I can get a new episode of my favorite podcast and listen to this person and be like, wow, they're so smart. And it's really stimulating to listen to these smart people that can communicate really clearly. And the concern that I have is that people get into a habit of just consuming knowledge, just listening to more and more different people and assembling this sort of like pristine map Of how they think reality works. And maybe they start a little bit to think about how they might initiate some kind of community or some project or something that they're interested in, but still they do this thing of Like way over engineering and overthinking it and under practicing, under experimenting. And so my energy is to try and interfere with that tendency and push people more towards their agency, more towards their initiative, their entrepreneurship, their get up and do it Kind of energy.
EP51 Richard Bartlett on Self-Organizing Collaboration
The Jim Rutt Show
...catch up on these, and many more highlights
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