Keeping food frozen costs money. It also risks spoilage if the freezing temporarily fails, which is hard to test for later. If jam is obsolete, it's only for sufficiently rich first world families.
Also, many people like sweet spreads and use jams regardless of their preservation properties.
I think the disparity in number of words is proportionally so large that this method won't work. The (small) hypothetical set of dolphin words wouldn't match to a small subset of English words, because what's being matched is really the (embedded) structure of the relationship between the words, and any sufficiently small subset of English words loses most of its interesting structure because its 'real' structure relates it to many words outside that subset.
Support that dolphins (hypothetically! counterfactually! not realistically!) use only 10 words to talk about fish, but humans use 100 words to do the same. I expect you can't match the relationship structure of the 10 dolphin words to the much more complex structure of the 100 human words. But no subset of ~10 English words out of the 100 is a meaningful subset that humans could use to talk about fish.
The approach of the linked article tries to match words meaning the same thing across languages by separately building a vector embedding of each language corpus and then looking for structural (neighborhood) similarity between the embeddings, with an extra global 'rotation' step mapping the two vector spaces on one another.
So if both languages have a word for "cat", and many other words related to cats, and the relationship between these words is the same in both languages (e.g. 'cat' is close to 'dog' in a different way than it is close to 'food'), then these words can be successfully translated.
But if one language has a tiny vocabulary compared to the other one, and the vocabulary isn't even a subset of the other language's (dolphins don't talk about cats), then you can't get far. Unless you have an English training dataset that only uses words that do have translations in Dolphin. But we don't know what dolphins talk about, so we can't build this dataset.
Also, this is machine learning on text with distinct words; do we even have a 'separate words' parser for dolphin signals?
I think you are absolutely right.
I'm not quite that sure I'm right. (I was genuinely asking about the mechanism, not claiming there isn't one!) I am not an expert and there are other epidemics that die out without having infected most of the population, like indeed seasonal flu and cold, and I don't know all the causes of that that might apply here.
End result will be less casualties but longer pandemic.
It could be worse; 'less overall casualties' relies on the reasonable but unproven assumptions of:
Moreover, the worst of the pandemic, or at least of this first wave, will be over in 2-3 months, as long as the containment measures are in place,
What's the mechanism behind the slowing growth? When we let up on our measures (quarantine etc) why will the growth not speed up again, until most of the population have been exposed?
If you don't mind the expense, you might want to consider an electric bike; I found them to be just as fun to ride as regular ones. You can set the e-assist level low enough to exert serious effort when you want to, or just turn off the engine entirely, and conversely set it high if you're tired or your knees hurt.
I found a dropper post to be a great help with that. It's much easier to figure out the right height while riding and not having to dismount to adjust it. And anecdotally, it sometimes feels better to adjust it up or down by 5-10 millimeters, maybe due to different clothes or shoes or posture or surface grade.
Note: even the cheapest dropper posts costs around 100 euro (from a cursory Google search). People who aim for cheap bikes often don't consider them. If you can afford it, consider if it would be a small investment into your comfort and longer-term health.
I don't see how that is applicable.
In the first case, to avoid the penalty of being fined, you pay taxes.
In the second case, to avoid the penalty of being taxed, you don't donate.
If I allow you to donate without being taxed, it doesn't follow that you will donate. Maybe you don't want to donate to begin with, or not unless everyone else does as well. That's the model the OP assumes.
Tax rates on non-donation gifts (= marginal income taxes of the non-rich) are "only" a few tens of percents. For the OP's model to work, he had to assume a ratio of 1:1,000,000 between the value to a noble of keeping or donating money. That's as if there was a 99.999,999,9% tax rate on donations! If there was such a tax rate, then making donations tax-free would certainly stimulate a lot of donations. But as it is, under the OP's general assumptions, tax rates of ~~ 30% should not much matter.
I don't understand your point. Paying taxes or not is not related to whether and how much other people also make charitable deductions. Bezos donating less or more doesn't influence Gates donating less or more. What is the coordination mechanism?
Thanks, that's informative.
One thing I would like to figure out is whether this can be explained by businesses restructuring so that some of the rich people who used to be owners getting dividends are now company executives getting salaries - but the salaries are still set mostly by themselves to benefit themselves, out of proportion to the value of their work to the company. Directors or board members often also get salaries, again for very little work in most cases.
These are things that might be colloquially called 'capital'. Jeff Bezos has a total compensation of 1.6 million; that is indeed a tiny part of his net worth, but I still think of it as "Jeff Bezos is a capitalist who is making money from the successful business he owns", not as "Jeff Bezos is being paid for his talents as a CEO". I don't care about the distinction from the income he gets from Amazon dividends, shares, or his salary as a CEO. But then I'm not an economist; perhaps these are really significant differences that I should care about.