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Joined 2 months ago
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Cake day: July 17th, 2024

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  • The learning facilitators they mention are the key to understanding all of this. They need them to actually maintain discipline and ensure the kids engage with the AI, so they need humans in the room still. But now roles that were once teachers have been redefined as “Learning facilitators”. Apparently former teachers have rejoined the school in these new roles.

    Like a lot of automation, the main selling point is deskilling roles, reducing pay, making people more easily replaceable (don’t need a teaching qualification to be a "learning facilitator to the AI) and producing a worse service which is just good enough if it is wrapped in difficult to verify claims and assumptions about what education actually is. Of course it also means that you get a new middleman parasite siphoning off funds that used to flow to staff.










  • Forgot to say: yes AI generated slop is one key example, but often I’m also thinking of other tasks that are often presumed to be basic because humans can be trained to perform them with barely any conscious effort. Things like self-driving vehicles, production line work, call center work etc. Like the fact that full self drive requires supervision, often what happens with tech automation is that they create things that de-skill the role or perhaps speed it up, but still require humans in the middle to do things that are simple for us, but difficult to replicate computationally. Humans become the glue, slotted into all the points of friction and technical inadequacy, to keep the whole process running smoothly.

    Unfortunately this usually leads to downward pressure on the wages of the humans and the expectation that they match the theoretical speed of the automation rather than recognise that the human is the the actual pace setter because without them the pace would be 0.





  • There’s definitely something to this narrowing of opportunities idea. To frame it in a real bare bones way, it’s people that frame the world in simplistic terms and then assume that their framing is the complete picture (because they’re super clever of course). Then if they try to address the problem with a “solution”, they simply address their abstraction of it and if successful in the market, actually make the abstraction the dominant form of it. However all the things they disregarded are either lost, or still there and undermining their solution.

    It’s like taking a 3D problem, only seeing in 2D, implementing a 2D solution and then being surprised that it doesn’t seem to do what it should, or being confused by all these unexpected effects that are coming from the 3rd dimension.

    Your comment about giving more grace also reminds me of work out there from legal scholars who argued that algorithmically implemented law doesn’t work because the law itself is designed to have a degree of interpretation and slack to it that rarely translates well to an “if x then y” model.




  • I feel like generative AI is an indicator of a broader pattern of innovation in stagnation (shower thoughts here, I’m not bringing sources to this game).

    I was just a little while ago wondering if there is an argument to be made that the innovations of the post-war period were far more radically and beneficially transformative to most people. Stuff like accessible dishwashers, home tools, better home refrigeration etc. I feel like now tech is just here to make things worse. I can’t think of any upcoming or recent home tech product that I’m remotely excited about.