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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
Bad Norms and Policies Produce "Legislatice Mediocrity" in Organizations
Summary:
Encouraging a culture of being teachable and open to listening to others is crucial for innovation and improvement in organizations.
While standard operating procedures (SOPs) and efficient systems are appreciated, they should not create taboos or hinder learning, leading to what the speaker refers to as 'legislative mediocrity.' The speaker advocates for a focus on innovation and continuous improvement, rather than being stifled by rigid norms and policies.
Transcript:
Speaker 1
You want to be teachable and you want to have a culture of being teachable and listening to others. Yeah. That's that's really important. And so I love SOPs. I love I love it when you get a system working well and efficient. But I don't like it when it creates taboos and when it stops people learning. Legislative mediocrity. It drives me nuts. I'm very much let's do innovation. Let's improve.
Organizational Structures That Enable Knowledge Flow With Stuart French
Because You Need to Know Podcast ™
The Danger of Incorrectly Mapping Between Scientific Measures and Truth
Transcript:
Speaker 1
And it's a problem when scientific culture tolerates too much ambiguity. There's always a caveat there, which is that at the early stage of theory development, sometimes you need ambiguity because you don't actually know really what you're talking about Yet. And so you need to allow for multiple interpretations to be possible until you can figure out what you mean. But a mature theory should be minimally ambiguous. This is at odds with things like metrics in terms of let's say how to evaluate something because people think, oh, well, it's scientific. Therefore, I want to use this to then therefore impose a value judge on something. It's better because it has a higher score on it. But that's not what science is actually able to do. Science can say, it has this score and it measures this thing because what it measures is this. If you say what it measures is this, and therefore it means this other thing, that's a problem because that's a false mapping. And it's not really about ambiguity versus precision. It's about, I think, the imprecision of the mapping between the measure and the term. So if you want to measure something like happiness or economic prosperity, you can say, well, we'll measure the genie coefficient, we'll measure GDP. But those are rigorous, clearly unambiguous measures. They have a meaning. This is what they are. This is how we measure them. We can compare things on this measure. And that's not problematic until you then say, and it is better to have a higher GDP full stop.
Paul Smaldino & C. Thi Nguyen on Problems With Value Metrics & Governance at Scale
COMPLEXITY: Physics of Life
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