They emit water vapor, not liquid water. Gasoline-fueled cars emit roughly the same amount of water as a hydrogen-powered vehicle, so we already know this isn't an issue.
It’s an issue here in Minnesota; intersections and highways where there’s standing traffic are slick from the freezing exhaust condensate, but, to clarify your statement: it potentially wouldn’t be a bigger issue than it already is.
I'm using something similar to Heisig, and I can already tell that while I can list all of the radicals and components in a character, I have no memory of their relative positions. I'm also not trying to learn them and I only using a pinyin input method, but I can't imagine really needing to be able to write by hand ever.
If you're not actually writing out the characters, you're not using Heisig. You wouldn't have that issue if you were actually writing them out. And it's not the wrote practice, it's the fact that you intrinsically must write one primitive at a time, which solidifies the order in the story. Most Heisig students end up developing slightly different primitive meanings for different placements, or an aspect of the story which controls the layout, for that reason.
I used to, but it basically showed that no one ever gave away my email address to spammers, or at least if they did, the spam filter caught it. It's not worth it.
Yes, but while animals are a black box to some extent, the outcomes of the experiments are not. And you don't have to worry about whether your results are an artifact of your simulation.
Your instincts are good - if we could simulate people in silico we could basically understand and cure every disease - but the scale of such a simulation is literally (not figuratively!) astronomical. Biological systems are way, way, way more complex than they appear and our computers are (currently) hopelessly inadequate.
Suppose your fancy new robot is wildly successful and can let me examine 2-3 orders of magnitude more sequences - that's still an infinitesimal fraction of all possible proteins with the same function, and now my DNA synthesis and sequencing costs are also 2-3 orders of magnitude higher - that's really non-trivial, and given that your company isn't selling a lot of these robots (who even needs this scale?), they aren't going to be cheap either.
How do I justify spending all this money? I need some theory of why this search is going to give me anything other than incremental improvements in activity or whatever metric I care about, but rational design can only take me so far. Generative models aren't going to give me a step change in activity. Why am I confident that the set of proteins I'm testing have megabucks of potential?
So sure, limitations in automation are an obstacle to bigger scale, but we often can't use the scale that's already achievable. There's certainly room for improvement in the automation space, but unlocking scale is not the only problem you need to solve.
I find the swift keyboard is better than the regular one, but good lord is the autocomplete terrible. It adds 's to words so often even when my finger never went close to the S key, it suggests "ang" as often as "and", and I'll often have something entered correctly, but then adding a word will cause the word three positions back to change. I could go on. I really don't get why it's so bad when my android from a decade ago basically always entered what I was thinking.
I don't know how common my situation is, but I don't have any of those constraints. I have eza aliased to ls both on my daily driver and all of my work servers and it hasn't caused me a single problem.
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