This is, by the way, the dirty secret of the machine learning movement: almost everything produced by ML could have been produced, more cheaply, using a very dumb heuristic you coded up by hand, because mostly the ML is trained by feeding it examples of what humans did while following a very dumb heuristic. There's no magic here. If you use ML to teach a computer how to sort through resumes, it will recommend you interview people with male, white-sounding names, because it turns out that's what your HR department already does. If you ask it what video a person like you wants to see next, it will recommend some political propaganda crap, because 50% of the time 90% of the people do watch that next, because they can't help themselves, and that's a pretty good success rate.

Forget Privacy: You're Terrible at Targeting Anyway

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if your job is legible enough that people can make a dataset clearly pointing out what is right and what is wrong, you are at the highest risk for an AI model being “superhuman” at your job. It is even more risky if it is possible to articulate your thought process in a way that is verifiable.

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How to have a career even when OpenAI's o3 drops

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Metacognitive laziness: Effects of generative AI on learning motivation | Hacker News

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Thoughtworks • Technology Radar | An opinionated guide to technology frontiers | Thoughtworks

There is a broad assumption underlying many machine-learning models that the model itself will not change the reality it’s modeling. In almost all cases, this is false.

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both beautiful and tragic. It is beautiful because on a good day it requires very little work; you often don’t need to spend much time on the pesky job of feature engineering, and in the best case the machine takes care of a large fraction of what needs to be done. It is tragic because nothing ever guarantees that any system in the real world will

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AI is exceptional at average . If something can be taught, it can learn it better. If it can be copied, it can copy it faster.

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Give a machine billions of data points. Add the power of pattern recognition. Suddenly, the mystique of creativity seems glaringly mechanical. Art, writing, and even strategic games like Go are revealed to be less divine and more like precision-based machinery.

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