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#bigdata

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Continued thread
We in the US are living in a eugenic modernity, by the way, when the putative head of "Health and Human Services" is making the kinds of statements he makes about autistic people. This is not just an anti-vaccination meme; it's an attempt to subordinate an entire class of people, suggesting they are subhuman for being who they are. This is a eugenic move. One has to wonder whether the "human services" people in HHS imagine themselves providing has to do with "improving the human stock" of the nation, the services not being provided to humans but instead having humans as an output.

Rather than get mired in the thought-terminating arguments around political parties or political factions, though, I think we'd do well to reflect on what sorts of other ways of thinking feed into this one: the measured life; standardized testing; the internet of things (sensors); tracking apps of various kinds; electronic health records; data science as a profession and Big Data generally; predictive modeling; generative AI and other optimization-oriented or productivity-promising technology. All of these function to render life as an object of knowledge in one way or another. All of them trace their origins through eugenics and the patterns of thought that led to it, and all of them threaten to enable and enhance further eugenic thinking. This is not to say these things are always all bad; this is meant to be a reflection on what exactly they're for.

Why read the number of steps your FitBit told you you took today, unless there were some sense in which you want your future self to be better than your present self? It's not an accident that this is called "physical fitness", "fitness" being the Darwinian concept describing which organisms should survive. Why subject children to standardized testing unless there were some belief it made them better students? To what end tends to be left out. Why adopt a technology meant to improve productivity, unless you're of the belief that improvement (optimization) were even possible?

Generally speaking, if one is able to bring oneself to believe that a human being is made better by a data-informed technical intervention, isn't one playing the same game as these anti-autism anti-vaxxers, just with different terminology? If your answer to this provocation is that your data is better than theirs or that you're more aligned with reality than they are--some variation of "the science is on our side"--you've ceded the territory: this is more of the same optimization logic that brought us to this point to begin with. I think we have no choice but to do better than this.

That's my reflection anyway.

#USPol #autism #vaccination #vaccines #antivax #eugenics #BigData #AI #PredictiveModeling #DataScience #science

CFP bis 30.5.2025: Die Tagung Digital Turn. Sammlungen – Provenienzen – Märkte (HU Berlin, 27.–28.11.2025) fragt nach Methoden, Potenzialen & Herausforderungen digitaler Forschung mit Big Data & KI in GLAM, Kunsthandel & Wissenschaft.
arthist.net/archive/47275
#DigitalTurn #DigitaleKunstgeschichte #GLAM #Kunstmarkt #BigData #KI #CFP #Berlin #LinkedOpenData

arthist.netDigital Turn. Sammlungen – Provenienzen – Märkte (Berlin, 27-28 Nov 25)Gabriele Zöllner. Zentrum für Kulturtechnik der Humboldt-Universität zu Berlin, Nov 27–28, 2025, Deadline: May 30, 2025

Via #LLRX #AI in #Banking & #Finance, 4/15/25 5 highlights #Tariffs, #Trump AI Are Changing Everything. Should My Portfolio #retirement plans too? How Much Should We Spend to Reduce A.I.’s Existential #Risk?; How Good is AI at Twisting Arms? Experiments in #Debt Collection; Artificial Intelligence in #Investment Banking Transforming Operations and Client Services; and The Transformative Role of Artificial Intelligence and #BigData in Banking. llrx.com/2025/04/ai-in-banking #credit #ID #breaches #crime

Replied in thread

@BethGMS I’m not going to comment on the validity of this as I usually go by what journalists have fact-checked in determining authenticity but, as a Computer Science person and someone who watched #bigData trends and recent #ArtificialIntelligence advancements unfold, I know how valuable our data is. Our data could easily be used for profit, exploitation, manipulation, and/or oppression.

Attended an event Brewing Data with Snowflake yesterday in Vilnius :blobcatnerd:

Some of they key insights:

  • Medallion Architecture (good or bad) is widespread.
  • Snowflake and Databricks are clear competitors, targeting similar landscape.
  • Open formats are trending: file format, table format, catalog, etc. - the more of them are open source, the better.
  • Time travel feature is important, many users already used it for disaster recovery.
  • Clear distinction of Storage from Compute (generic cloud approach).

Full text of one of the slides presented:

Strategic Architecture Outlook

  • Agility & Future-Proofing - Open, portable data means you can adopt new technologies or switch platforms with minimal friction. No single vendor can hold your data hostage, so you can evolve vour architecture as needed.
  • Multi-Cloud and Hybrid - An open data layer can span clouds and on-prem seamlessly. You avoid cloud vendor lock-in and leverage best-of-breed services on different clouds using the same data. This flexibility is key for resilience and optimization.
  • Accelerating Innovation - When any team can access data with the tools of their choice, experimentation flourishes. Open data fosters Al/ML and cross-domain analytics since data isn't locked in silos - more innovation and insights from the same data.
  • Vendor Leverage - Strategically, using open standards increases your leverage in vendor negotiations. You car opt in or out of services more freely, pushing vendors to provide value (since you're not irreversibly locked to them).