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My previous/alt account is yetAnotherUser@feddit.de which will be abandoned soon.

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Joined 1 year ago
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Cake day: June 1st, 2024

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  • Nope, the treaty with Ukraine (purposefully) never specified consequences for anyone violating it. It only said (I’m paraphrasing here because I don’t want to look it up) that the signatories will respect Ukraine’s borders.

    The US respects this treaty still and doesn’t recognize Russia’s claims to Ukrainian land. The lack of specified consequences for anyone violating it makes the treaty nearly worthless.

    Signing “I will respect your border” is very much different from “I will defend your borders”.



  • Base load doesn’t exist. At least not in the way you consider it to.

    Right now energy production is based on demand. With renewables, this should be reversed: Demand should adjust to the supply.

    One very quick way to achieve this is by mandating dynamic electricity pricing for everyone - company or individual alike.

    It will not take long for energy intensive companies to construct their own battery storage (since “purchasing” at -1 cent/kWh is much more economical than at 60 ct/kWh). Consumer demand will also adjust. If your washing machine costs 3€ to run at midnight and -10 ct at 2 pm, when do you think people will wash their clothes? The same goes for charging their EV, vacuuming etc.

    The sole remaining factor is heating in winter. Which can not be solved by better battery storage but rather by building thousands of wind turbines everywhere.





  • The idea of anomaly detection is to project some input onto a (high dimensional), numeric output. From the training data alone, you can then see where the projections are clustered and develop a high dimensional “boundary” where everything within is known and good and everything outside is unknown and possibly bad. Since orders come in relatively slow, a human would be able to check for false positives and overwrite the computer decision.

    By the way, an ideal training set is preprocessed and has duplicates removed and new orders added by recombining parts of individual orders.

    For example, if we have 3 orders:

    • (Hamburger, Fries)
    • (Hamburger, Fries)
    • (Cheeseburger, Sandwich)

    We could then create the following set:

    • (Hamburger)
    • (Cheeseburger)
    • (Fries)
    • (Sandwich)
    • (Hamburger, Fries)
    • (Hamburger, Cheeseburger)
    • (Hamburger, Sandwich)

    And so on, and so forth. A naive variant is just taking the power set of all valid orders.


  • There are machine learning algorithms for anomaly detection though. They actually work decently well because exploits like this do in fact differ significantly from regular orders. Because they assume all anomalies are attempted exploits, their false negative rate is rather low while their false positive rate can be a bit higher.

    Taco Bell has the capability to create a decently large training set from all recorded orders (which must all be valid and non-malicious) so they shouldn’t have too many issues developing this model.

    If an anomaly is detected, make a human verify it is indeed an irregular order.