Data Science Weekly Digest #1

How Green Is Tech, Really?

Estimated reading time: 5 minutes

Energy has dominated the news the last couple of weeks. On the one hand, the Colonial Pipeline hack exposed the vulnerability of the networks that control our energy distribution system, resulting in a surprise counterattack by the US on the hackers that both locked them out of their own system and apparently also made it impossible to get to their own digital currency.

How Green Is Tech, Really?
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On the other hand digital currencies have seen a major run-up, especially in Ethereum and Dogecoin, as speculators have bid prices of these blockchain-based coins into the stratosphere, likely in response to the possibility that increased commodity prices are an early warning of hyperinflation (which is grist for another editorial).

Similarly, Non-Fungible Tokens (NFTs) have taken the art and speculative realms by storm, employing blockchains to make a digital copy of a piece of artwork unique, something that is almost impossible to otherwise manage in the digital realm. DSC Contributing Writer Stephanie Glen has written a must-read article this week that explains what NFTs are, why they are attractive, and why they also may be yet another tulip mania.

NFTs Explained in Two Pictures: The Good, The Bad … and The Ugly

This site is devoted in great part to machine learning and other computationally intensive technologies, but it is always worth remembering that computation always comes at a cost, and that cost is almost invariably energy-related. There is a perception that technology is “green”, but it’s more accurate to say that technology is simply distributed, which means that most people do not see the direct impact of technology, because the place where the power is generated is typically quite far away from where it is consumed.

This means that we as data professionals need to recognize that we need to keep our algorithms as efficient as possible, perhaps trading some (usually spurious) accuracy for less power-hungry applications. Moreover, we should advocate the notion that the energy involved in creating better models (or calculating primes to ensure rarity) be better accounted for in our businesses, rather than simply dumped out the back door onto society’s commons. If the speculators who have dreams of becoming rich on Ethereum or Dogecoin or some dubious NFT scheme had to factor in the costs of data mining for primes, they may find that the benefits aren’t worth it.

This is why we run Data Science Central, and why we are expanding its focus to consider the width and breadth of digital transformation in our society. Data Science Central is your community. It is a chance to learn from other practitioners, and a chance to communicate what you know to the data science community overall. I encourage you to submit original articles and to make your name known to the people that are going to be hiring in the coming year. As always let us know what you think.

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