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index.md (2706B)


      1 ---
      2 title: "Weeknote for 2025-W06"
      3 description: "Constructive pessimism, technology vs. hi-tech, and open source
      4 machine learning"
      5 date: 2025-02-03T04:59:00-08:00
      6 draft: false
      7 categories:
      8 - Weeknotes
      9 ---
     10 
     11 ## Constructive pessimism beats false hope
     12 
     13 Do you feel like the state of the world is bad, is nearly certain to get worse,
     14 and that there's virtually nothing you can do to change any of it? If you
     15 answered yes to all three questions, I think you have a more realistic
     16 understanding of our circumstances than many people.
     17 
     18 But that doesn’t mean you’re off the hook! Research described in this article
     19 shows that those of us with a pessimistic outlook are more likely to act to
     20 improve our circumstances than naive optimists---as long as that pessimism
     21 inspires action.
     22 
     23 This article focuses on attitudes toward climate change, and back in December I
     24 shared it with many of my friends who share my concerns about our warming
     25 planet. However, the argument is also relevant in other contexts---such as the
     26 political environment---so it's worth reading again.
     27 
     28 [The Upside of Climate
     29 Pessimism](https://undark.org/2024/11/28/opinion-upside-of-climate-pessimism/)
     30 
     31 ## “Technology is the active human interface with the material world”
     32 
     33 This quote comes from a essay from my favorite sci-fi author, Ursula K. Le
     34 Guin. It challenges nerds (like me) to remember that “technology” doesn't mean
     35 "high tech stuff." It's a quick read and worth your time.
     36 
     37 [A Rant About
     38 “Technology”](https://www.ursulakleguin.com/a-rant-about-technology)
     39 
     40 ## What is an “open source” machine learning model?
     41 
     42 I loved this Mastodon post from Timnit Gebru so I'll just reproduce it right
     43 here:
     44 
     45 > Friends, for something to be open source, we need to see
     46 > 
     47 > 1. The data it was trained and evaluated on
     48 > 
     49 > 2. The code
     50 > 
     51 > 3. The model architecture
     52 > 
     53 > 4. The model weights.
     54 > 
     55 > DeepSeek only gives 3, 4. And I'll see the day that anyone gives us #1
     56 > without being forced to do so, because all of them are stealing data.
     57 
     58 I made a similar point in [the "terms" I wrote]({{< ref
     59 "/about/index.md#copyright" >}}) for anyone who wants to scrape my site to
     60 train a model[^cc]. If you can't reproduce a digital artifact using the
     61 "sources" provided, then it's not "open source", and machine learning models
     62 (including large language models like DeepSeek) can't be reproduced without the
     63 data that trained them.
     64 
     65 [Link to Gebru's original
     66 post](https://dair-community.social/@timnitGebru/113909880610412733)
     67 
     68 [^cc]: I'm not naive enough to think that the people scraping the web to train
     69     these models would respect my terms, but doing so would be one step toward
     70     anything we could consider "ethical AI".