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Joined 1 year ago
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Cake day: June 14th, 2023

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  • Machine learning techniques are often thought of as fancy function approximation tools (i.e. for regression and classification problems). They are tools that receive a set of values and spit out some discrete or possibly continuous prediction value.

    One use case is that there are a lot of really hard+important problems within CS that we can’t solve efficiently exactly (lookup TSP, SOP, SAT and so on) but that we can solve using heuristics or approximations in reasonable time. Often the accuracy of the heuristic even determines the efficiency of our solution.

    Additionally, sometimes we want predictions for other reasons. For example, software that relies on user preference, that predicts home values, that predicts the safety of an engineering plan, that predicts the likelihood that a person has cancer, that predicts the likelihood that an object in a video frame is a human etc.

    These tools have legitamite and important use cases it’s just that a lot of the hype now is centered around the dumbest possible uses and a bunch of idiots trying to make money regardless of any associated ethical concerns or consequences.




  • I’ve interacted with communities here for some of my interests that I hadn’t really interacted much with on reddit due to my primary interests being more niche and having a slower rate of content generation.

    It’s kind of good and kind of bad though. Some communities for things I care about are full of people who are dogmatic to the point of being actively stupid. But I do like thinking about the ideas and topics of those communities, and discussing flawed ideologies within a particular community is probably worthwhile and necessary.