Not that it's relevant, but the claim that malaria has killed half of humans who have lived is completely absurd. Falciparum malaria is nasty because it has only recently jumped to humans. With time, it would adapt to be less deadly, to better spread, and humans would adapt to be less vulnerable. This is exactly what happened to vivax ("benign") malaria. Vivax jumped to humans 35k years ago, while falciparum only 5k years ago. The genus Homo was free of malaria for millions of years. If you define humans as starting 50k years ago, then some form of malaria was present for most of that time, although how deadly it was varied a lot over the millennia.

What makes you think so? The main reason I can see why the death of less than 100% of the population would stop us from getting back is if it's followed by a natural event that finishes off the rest. However 25% of current humanity seems much more than enough to survive all natural disasters that are likely to happen in the following 10,000 years. The black death killed about half the population of Europe and it wasn't enough even to destroy the pre-existing social institutions.

More crucially it would require permission.

no. print your own after buying the ebook. If you go to an office-supply store they should be able to print and bind it for you.

Yes, there are differences.

I talked to a person who was hiring for tech jobs in Silicon Valley, and he said that the Ivy League schools in the United States get a much better quality of training than other United States schools in the fields of engineering and computer science. For example, the Ivy League schools would have 3 hour exams where you have to show how you arrived at an answer as well as the answer. Most of the other schools had 1 hour multiple choice exams.

The situation is different in other countries. In Canada, unlike in the US, engineering is a regulated profession. That means certain types of designs have to be approved by a Professional Engineer. There are rules about how to become a professional engineer. One path to becoming one involves graduating from an accredited program at a university. So every engineering program at a university is monitored by the Canadian Engineering Accreditation Board. No matter which university you go to in Canada, you'll get the 3 hour non-multiple choice exams, and very good training.

For this reason, the person at the tech company liked to hire Canadians, since they have the good training, but not the entitled attitude of the Ivy League graduates. But he considered Ivy League graduates. He wouldn't even consider the non-Ivy League ones, unless they showed some other way that they actually have the skills and training, since they didn't get it at school.

Another difference between Harvard, MIT, etc. and the typical Canadian university is that Harvard and MIT have huge endowment funds and many wealthy alumni and donors, so they have access to a lot more resources than most universities. Like about 1000x more money. The MIT endowment fund reached $13.5 billion in 2015.

I also know someone who went to Harvard for a Masters degree in political science, and she said that after getting a degree from Harvard, she was taken a lot more seriously. People listened to what she said, and deferred to her, in a way that they didn't before she could say that she went to Harvard. So it seems to make a difference in public policy and government work.

Another difference between universities can be their intellectual property policy. The University of Waterloo, in Canada, has the most respectful intellectual property policy that I know of, among universities. Simply, if you do research or work there, it's yours, and you can use it in a startup company if you wish. Many students and professors do this. The University of Waterloo is well known for many technology spinoff companies. I know of one prof who doesn't even publish journal articles any more, he just documents his work with patents, and then uses them in his startup. This is seen as adding prestige to the institution, unlike in some other universities, where there's a bit more disdain for commercialization. The co-op program at the University of Waterloo also means that students get real-world experience and bring that back to the classroom, and is another reason for the more positive attitude to collaboration with industry.

The University of Waterloo's intellectual property policy is even more respectful and individualistic than the one at Stanford, which is also a university known for its spinoffs.

Oxford has more name recognition in North America than Cambridge. Within the UK, they are seen as mostly equivalent, to the point that "Oxford and/or Cambridge" is often shortened to "Oxbridge". It is easier to get work in government in the UK with an Oxbridge education.

Also, the people you meet at university are more likely to end up in powerful positions if you go to one of the big name universities. So the alumni network becomes more valuable.

It depends on what you want to study and what you want to do afterward. There are some fields where it wouldn't matter much.

Graduate education is a different matter. With some exceptions, like my friend who went to Harvard for political science that I mentioned above, it doesn't really matter much what school or department you go to for graduate school. The most important things will be 1) What did you do? and 2) Who did you work with? The rest is almost irrelevant. Finding a good supervisor who you can get along with, and who will help your career, is the priority when choosing graduate school.

I'm most familiar with Canada, and a bit with the US and the UK. I don't know the situation in other countries.

Fixed. put my name on the list.

Who are the moderators here, again? I don't see where to find that information. It's not on the sidebar or About page, and search doesn't yield anything for 'moderator'.

A more massive car has more energy to dissipate but also more structure (crumple zones) to apply this energy to. The net balance is not obvious me, either.

While most car accidents do not involve hitting walls, a lot involve hitting objects-other-than-cars (guard railings, trees, animals, etc.) where being heavy can be an advantage (because that excess energy that you have you dissipate into the object). As to pedestrians, the mass discrepancy is big enough to not matter -- the consequences to the pedestrian of being hit by a 1-tonne car are the same as being hit by a 2-tonne car.

I have some questions on discounting. There are a lot, so I'm fine with comments that don't answer everything (although I'd appreciate it if they do!) I'm also interested in recommendations for a detailed intuitive discussion on discounting, ala EY on Bayes' Theorem.

• Why do people focus on hyperbolic and exponential? Aren't there other options?
• Is the primary difference between them the time consistency?
• Are any types of non-exponential discounting time-consistent?
• What would it mean to be an exponential discounter? Is it achievable, and if so how?
• What about different values for the exponent? Is there any way to distinguish between them? What would affect the choice?
• Does it make sense to have different discounting functions in different circumstances?
• Why should we discount in the first place?

On a personal level, my intuition is not to discount at all, i.e. my happiness in 50 years is worth exactly the same as my happiness in the present. I'll take $50 right now over $60 next year because I'm accounting for the possibility that I won't receive it, and because I won't have to plan for receiving it either. But if the choice is between receiving it in the mail tomorrow or in 50 years (assuming it's adjusted for inflation, I believe I'm equally likely to receive it in both cases, I don't need the money to survive, there are no opportunity costs, etc), then I don't see much of a difference.

• Is this irrational?
• Or is the purpose of discounting to reflect the fact that those assumptions I made won't generally hold?
• The strongest counterargument I can think of is that I might die and not be able to receive the benefits. My response is that if I die I won't be around to care (anthropic principle). Does that make sense? (The discussions I've seen seem to assume that the person will be alive at both timepoints in any case, so it's also possible this should just be put with the other assumptions.)
• If given the choice between something bad happening now and in 10 years, I'd rather go through it now (assume there are no permanent effects, I'll be equally prepared, I'll forget about the choice so anticipation doesn't play a role, etc). Does that mean I'm "negative discounting"? Is that irrational?
• I find that increasing the length of time I anticipate something (like buying a book I really want, and then deliberately not reading it for a year) usually increases the amount of happiness I can get from it. Is that a common experience? Could that explain any of my preferences?

This will not have any practical consequences whatsoever, even in the long term. It is already possible to perform reversible computation (Paper by Bennett linked in the article) for which such lower bounds don't apply. The idea is very simple: just make sure that your individual logic gates are reversible, so you can uncompute everything after reading out the results. This is most easily achieved by writing the gate's output to a separate wire. For example an OR gate, instead of mapping 2 inputs to 1 output like

(x,y) --> (x OR y),

it would map 3 inputs to 3 outputs like

(x, y, z) --> (x,y, z XOR (x OR y)),

causing the gate to be its own inverse.

Secondly, I understand that the Landauer bound is so extremely small that worrying about it in practice is like worrying about the speed of light while designing an airplane.

Finally, I don't know how controversial the Landauer bound is among physicists, but I'm skeptical in general of any experimental result that violates established theory. Recall that just a while ago there were some experiments that appeared to show FTL communication, but were ultimately a sensor/timing problem. I can imagine many ways in which measurement errors sneak their way in, given the very small amount of energy being measured here.