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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
How Measurability/Mathematical Bias Limits the Scope of Scientific Inquiry and Human Discovery
Transcript:
Speaker 1
So there's this old paper from the, I think, 1960s by Eugene Vigner, the Nobel Prize physicist. It's called something like, on the unreasonable effectiveness of mathematics. The fun paper, and he's like, there's no good reason why mathematics should work as well as it does. And there's no good reason why there should be a tool that allows humans to predict things as well as math does. There's no good reason. It's kind of nuts. And we should all just be grateful. And he says some other things, but he's basically just kind of being all about how great mathematics is and how there's no good reason why it should be. And it's pretty cool that it does work so well. I think that there's a counter to that, which is that not everything is that easily described that mathematics. And there's lots of things for which mathematics is not that effective at describing. And it's actually just the things that were well described or easily described by mathematics are the things that were discovered using mathematical tools. They're the things that lend themselves that were amenable to mathematical inquiry. And a lot of the things that we're interested in terms of social science and cognitive science and the related philosophical inquiry are things that are much less tangible in terms Of this kind of specification. And you can see it like in a physics equation, right, a physical theory, whether it's about mass or electricity or something else, right, you have a theory about how things work. And then you can write out equations. And all the terms in the equations have units. And they are all directly related to the things that are measurable. The theories are directly about relationships between things that are measured. And in social theories and cognitive theories, so often our theories are about relating constructs. And then we have proxy measurements, but the theory isn't about the relationship between the proxy measures. The theory is about the constructs and the relationships between the constructs that are social in nature, that are cognitive in nature, but aren't the things that are being measured. And so there's this gap. And I don't know the extent to which that gap can be overcome.
Paul Smaldino & C. Thi Nguyen on Problems With Value Metrics & Governance at Scale
COMPLEXITY: Physics of Life
DEEP Framework: Documenting Decisions, Events, Explanations, and Proposals in Your Org
Summary:
The DEEP framework emphasizes the documentation of decisions, urging the recording of the rationale behind business and general decisions.
It also stresses the importance of documenting events such as meetings and town halls, highlighting the need for summarization. Furthermore, the framework encourages documenting explanations, especially in the context of onboarding, as they often involve repeated material.
Lastly, it emphasizes documenting proposals or ideas, allowing individuals to present their rationale to others and providing time for considered reactions.
The acronym 'DEEP' serves as a reminder for teams to consider the documentation created within their workflow.
Transcript:
Speaker 1
So I came up with an acronym as well, and I call that acronym deep. I think you'll identify with some of these. So deep for decisions, if there's ever a decision, then you should record the rationale for it. And we've talked about it endlessly on our tech radar's decision record systems. But I extend that to business decisions as well and general decisions as well. So similar format. Then there's events. So you have a town hall, you have a meeting, all of those are events, right? And you better document them for the benefit of other people. And when I say document, I mean, summarize, sure, you can have a recording or snippets of recordings if they are useful for people, but the summary is the more important thing. Then there's explanations, and I found these very useful in the context of onboarding, because there's a lot of explainer material that gets repeated in onboarding. And those are definitely great candidates for documentation. And the last one is proposals. And I called that proposals, but really I'm trying to talk about things like ideas. So let's take an example. I want to use this new library on my project. I have a certain rationale for it. Let me write down the thought process. What value is it going to bring? Let me present it to everyone. Everyone has the time to consume it. Oftentimes we go into decision making with a lot of cognitive load, where, you know, Ken explains in rapid fire things that he's been thinking about for the last 15 days. And now I have to consume it in the next 30 minutes and give Ken a year or nay. It's really difficult because Ken's done all the deep thinking, I need the time to process it and writing gives me the time to process it, right? And I can also not give knee jerk reactions, but considered reactions. So proposals, and that starts to include design documentation, idea papers, any kinds of proposals that you make on the team. So that acronym deep is a good trigger for teams to kind of hold on to and think about what is the documentation we're creating in the flow of work.
Asynchronous Collaboration — Getting It Right
Thoughtworks Technology Podcast
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