nshepperd
Joined 14 years ago
Joined 14 years ago
I would really like to believe that, but given how Medtronic and every CGM I have used have seemingly intentionally sabotaged open source loop compatibility with every new product, it doesn't seem like everyone's on board...
Moderna? The research startup that had only recently IPO'd and didn't have a single commercial product before covid? Seems weird to call it a "big pharmaceutical company".
I'd rather a few founders become billionaires from a successful startup than have Moderna gone bankrupt and lose the vaccines they were developing forever.
Certainly, if you think you can identify people who are particularly qualified to pick good ideas and make investments in them, it's not a bad idea to give them some money (but where do you get that money? hopefully not somewhere with negative externalities such as stealing it from different random business owners). Although structurally it's better if the billion itself is given as an investment instead of a gift, because then the person giving the billion has "skin in the game" themselves. Which is basically what I think VCs do, although I don't know how effective they usually are.
(Yes, this whole thing assumes a regulatory framework where businesses that have negative externalities or are otherwise bad for the world do not make profit. Indeed, companies that use dark patterns or rent seeking tactics to make profit should be legally punished, and if such punishments make them bad investments it's a good thing, because it means they were on net bad for the world.)
The return on investment is the payment it is in our self-interest to give for this activity, because we want and need such work to be done (so that we can make use of the fruits of such ventures, instead of getting nothing at all when wealth is squandered on bad ideas or just plain old consumption).
Your examples do relate to problems the algorithm actually has in this application, but they manifest as things like "extremely large warmup time" and "adjusting to a regime change in the distribution taking time proportional to the change". For instance if your data is [1000, 1001, 1000, 1001...] then it takes 1000 steps to converge, which may be longer than the user has patience for.
However, the algorithm does always converge eventually, as long as the stream is not something pathological like an infinite sequence of consecutive numbers (for which the median is undefined anyhow).
Furthermore, if you want to come to valid statistical conclusions about whether a discrepancy is due to a particular cause (and can't just do a RCT), it's not enough to just ignore plausible confounders until they are "provable". You need to systematically control for all possible confounders.
That sounds great tbh. Not everything needs to be a 4MB bootstrap.js webshit that doesn't even load with adblockers enabled.
I actually wrote a js bookmarklet to solve this problem by continuously capturing the caption updates and writing them into a separate window. It's a little janky, but works (at least until Meet releases the next update that changes the obfuscated class names in the DOM). https://zlkj.in/bookmarklets#record-meet-captions
(Needless to say, running an invalid vaccine trial also has very bad consequences: it would delay the actual deployment of a working vaccine to the general public, resulting in millions of unnecessary person-hours of potential virus exposure.)