Hacker News new | past | comments | ask | show | jobs | submit | laGrenouille's comments login

> His fame may outlive Foch and Ludendorff, Wilson and Clemenceau.

Funny to think how this has aged since 1915. Over a century later Einstein is an almost universally known figure. The others on this list are, particularly outside of France, names that I would not expect the median person to be able to say something interesting about.


I wonder how much of this ultimately comes down to branding. Einstein has a memorable name, a memorable haircut/photograph, and also managed to have his name become a byword for “genius.”


Interestingly this is what he thought of the matter at the time:

> Our time, he added, "is Gothic in its spirit. Unlike the Renaissance, it is not dominated by a few outstanding personalities. The twentieth century has established the democracy of the intellect. In the republic of art and science there are many men who take an equally important part in the intellectual movements of our age. It is the epoch rather than the individual that is important. There is no one dominant personality like Galileo or Newton".

Now, there was probably a good deal of fake modesty in that statement - he was a fairly dominant personality in the first part of the 20th century. But I suspect a key reason Einstein continues to be a widely recognizable name is that current scientists (physicists etc., those who are most equipped to rank / perpetuate his status) continue being in awe of the singular nature of his contributions, more so than any of the other "greats" of that period.

Why so? He could not have predicted it himself back then, but more than a century later his work would not have been "normalized". There was no subsequent breakthrough in fundamental physics that would somehow link geometry/gravity with the rest of the physics "stuff" (or vice-versa). As he relates in the interview, during that time (1929) he was working on a unified theory of gravity and electromagnetism but his language suggests he was not at all confident. Till this day the mental models he introduced to help us grasp the workings of the universe remain a thing apart.


If you purely value scientists with some sort of Value Over RePlacement metric on their scientific contribution alone, I would like to think Einstein is tier 1 along side another 20-50 people.


I'm not familiar with the other names in the parent comment, but do their accomplishments map to Einstein's as far as impact goes? I think they would have to before we consider branding as a main factor in longevity of...reputation?


Depends on what you mean by impact. Those other figures were quite influential on European (and thus global) politics during and after WW1. One could argue that the harsh policies toward Germany had a big impact on setting the stage for WW2, the largest war in history. So I wouldn’t be too dismissive of their impact on world history.

Of course in the grand scheme of things Einstein was probably more influential, but I was more commenting on the fact that Einstein has become a kind of memetic symbol in himself, a bit like Ché, whereas the others haven’t. (Most people probably can’t even name more than a handful of people from WW1.) Maybe that only happened because his work was so impactful, but does the average person really know much about relativity? I was trying to find a paper that traced how Einstein became synonymous with genius but couldn’t come up with anything.


> So I wouldn’t be too dismissive of their impact on world history.

Not of their impact on world history, no, but we are discussing more how the idea of someone lives on after they are dead and for how long, so maybe in that context it maybe deserves to be dismissed, as in there's a reason those figures are not referenced or talked about as much as Einstein is.


Erich Ludendorff was a rather significant factor in the rise to power of the Nazis and he coined the term “total war”.

So he certainly had an impact, I’m not sure if I would use the term accomplishment though.


On the other hand, he is the last scientist who was treated as a superstar. There is no other person who can reach the same degree of his reputation.


It is almost completely due to media's frequent mention of him. He shows up in movies and TV shows a LOT, way more than any other scientist by a wide margin. Most normal people don't know what he did, but he exists in popular culture as a symbol of intelligence.


Hotels with similar amenities are usually priced at absurdly high rates for corporate clients.

The place you linked to has the equivalent of a studio apartment with no laundry machine going for over 9000 CAD for a month. AirBnB has plenty of one bedrooms going for a third of that.


These notes might be a great source for what they cover, but as a whole I find this to be a good example of what is currently wrong with data science education. While the syllabus has bullet points that include "1. data collection", "2. data management", and "5. communication", the content and schedule have a 90%+ overlap with a standard machine learning course. They even use a statistical learning textbook (a good one, but still).

Statistics departments keep trying to latch on to the excitement (and money) around data science by changing the superfluous things like department names and course titles without actually adjusting what they teach. I would love to see a version of this that actually engages at a non-superficial level with topics such as database design, theory(ies) of data visualization, methods for storytelling with data, and interactive design.


>> would love to see a version of this that actually engages at a non-superficial level with topics such as database design, theory(ies) of data visualization, methods for storytelling with data, and interactive design.

I love these discussions and taxonomies in data science. So I have a few genuine/honest questions:

1) isn't what you said more "analytics" or "analytics engineering" oriented (which also and itself is a subtopic/subfield of data science) ?

2) I think that more and more people are trying to define what "data science" is, specially for marketing purposes, and then put it in a box, like any other science (i.e. chemistry - take an undergrad chemistry textbook and they will always cover the same topics). But since it isn't well defined yet, many different courses covers different algorithms/aspects of data science, so I think it end up looking superficial and hard to please everyone. Would you agree w/ that? For ex. I'm trying to find a good and in depth course that applies Data Science/Machine Learning in Big Data problems, but I just can't find any serious course covering it.


I completely agree that it's an open question about what exactly constitutes data science and what should (or at least could) be covered in a standard introduction. For me, a fairly reasonable—though certainly not definitive—set of topics are five items listed on this course's syllabus. And that's what makes this so frustrating, personally. The instructors actually have a good proposal of what should be taught, but then just turn around and teach a classical course in statistical learning.


the content and schedule have a 90%+ overlap with a standard machine learning course

Note that neural networks are not even mentioned in the content. This is not a good course to learn modern ML.


At the time the comment was made the link was https://harvard-iacs.github.io/2019-CS109A/pages/materials.h... where neural networks were mentioned.

See https://news.ycombinator.com/item?id=32295656


The other topics you mentioned aren’t exactly classified as “data science” so you likely won’t see them in most university data science courses. Database design has its own course usually but I’ve seen more of the rest as part of college/certificate programs.


The data scientists I've worked with definitely do data visualization and storytelling with data. (Schema design, not so much...)


You're thinking too narrowly about what "schema design" could mean. No, data scientists do not typically design back-end, production database systems. But defining and organizing a multi-sheet spreadsheet for manual data collection is what many data scientists spend much of their time doing (i.e., in the biomedical space). Doing that well definitely requires some understanding of concepts such as functional dependency, normal forms, and data types.


LexisNexis, which does other things now but started in the legal space, offers a huge collection of legal opinions with a fairly good search and linking capabilities. Most clerks and law professionals would have access to it.

I think the benefit of Wikipedia is not access to materials so much as it is the succinct summarization of the legal opinions. Perhaps now NLP could help with this, but it's a very complicated problem to provide a summary of the important bits from a 100+ page legal document.


> ... the succinct summarization of the legal opinions.

LexisNexis and Westlaw produce succinct summaries of legal opinions. That's the basis of their value, because the legal opinions themselves are not copyrighted. They also categorize everything about an opinion so that their database is searchable by area of law, etc.


There is a rather surprising amount of research about these kinds of approaches [1]. The various strategies and extensions are an interesting read.

^[1] https://en.wikipedia.org/wiki/Fair_cake-cutting


But arguably the EU is even more motivated by the security issues of an emboldened Russia. Combined with the much stronger environmental push from groups in Europe, it seems reasonable that they would push as much or more for energy-based sanctions against Russia.


There is no to very little security issues. Russia is never going to attack the EU/Nato, which are many times stronger that it is, and we've seen that Russia is already struggling in Ukraine.

At the moment Europe is going through a self-inflicted crisis that is not justified, be it by security or "the environment".

It may be reasonable to decrease dependency on Russia but the sensible way is to have alternatives in place first.

Edit: Baltic states are in Nato, so my comment fully applies to them.

Saying that "Russia did invade Ukraine so they could attack Nato" is equivalent to saying "the US invaded Iraq so they could attack China". It is plainly a rhetorical fallacy.


Err, baltic states? Formerly part of USSR albeit only since 1940.

EDIT: Finland was also part of Russia.


How much are you willing to bet on that? I saw plenty of people saying before the invasion that it would be irrational for Putin to invade and therefore he wouldn't. He did it anyway and we can easily see from how badly it's gone so far that it wasn't rational, so the obvious conclusion is that he's not acting rationally.


He might be relying on people's grasp of history being terrible and that they have far more desire to be warm than concerned about naked aggression. Which is probably true, although frustrating after all of the examples Europe provided itself in the 1800's and 1900's.


>I saw plenty of people saying before the invasion that it would be irrational for Putin to invade and therefore he wouldn't.

I didn't, but nevertheless there's a difference between "it's irrational because the cost is higher than the benefit" and "it's irrational because there's no benefit to getting your ass kicked".


Isn’t one of those a subset of the other? Both look kinda like they’re applicable to Russia right now, but I’m aware my sense of scale may be wrong for stuff like this.


I see a lot of confusion about college endowments on HN. It's best to think of them not as saving accounts but financial instruments. Annual university budgets make use of the returns off of the endowment, often covering more than half of their expenses. Their long-term plan is to never tap into the principal. In fact, the hope is to increase the principal with new donations to expand the annual returns to fight against inflation and support new initiatives.


> It's best to think of them not as saving accounts but financial instruments.

I mean I get that (not that a bank isn't a financial instrument...) but to what ends? The example I gave is should be clear, because of the large wealth of money that is. $54bn is no laughing matter. Stanford has $30bn. These are sums that completely pay for the operating costs of the universities and students. On interest. If the point is to be like dividend investors, it does not appear (at least from what I'm seeing) that they are actually acting like someone with a goal to live off of dividends. There's more growth than that. Or there's something missing that I don't understand (more likely).


Using Harvard as an example, they had an operating expenses of $5.0 billion in 2021 [1]. In order to cover that, they would need to be having a consistent rate of return around 9% on their ($54 billion) endowment. That a fairly good estimate of the rate of return for the stock market over the past 25 years, so not unreasonable. Though, this ignores that most of the endowment consists of restricted funds that can only be used on certain ways. Also, generally you need to cut a few percentage points if you want to guard against inflation.

So yes, Harvard could cover just about all of their budget with the endowment returns, though they probably need some extra income to cover the holes formed by the restricted funds and avoid inflation pressure.

Is their current usage of the returns too conservative? Probably. Do they have an absurdly large pile of cash that they have no business holding on to? Not really; the investment returns roughly correspond with their current operating costs.

[^1] https://finance.harvard.edu/financial-overview


I was taught that you should estimate a 4% rate of return, which means Harvard's endowment can cover around 40% of their budget. Much of the remainder will come through research grants, and a rather small percentage will be tuition and fees.


Yes, that's what I learned as well. And that's actually close to what Harvard reported using last year ($2.1 billion from the endowment; 40% of the budget).

I was only using the more aggressive number as a thought experiment to the original poster about what it would take the cover the entire operating budget.


I admit I don't know about university endowments, so the question is: what are those $40+ billion dollars sitting there for every year? Does a university really need a balance that may be larger than some nation's GDP?


>Does a university really need a balance that may be larger than some nation's GDP?

This comparison makes no sense.

1. GDP is a per-year measure, but an endowment is accumulated over multiple years. Therefore durectly comparing them doesn't really make much sense.

2. "some nation" includes some pretty small/poor countries. Should it be a surprise that an organization in the US is bigger than a country like Liechtenstein?


> what are those $40+ billion dollars sitting there for every year?

It’s not sitting there - it’s invested and working. Investment returns pay for operating expenses.


Thanks for the answer, makes sense from a purely economical perspective, but I wonder if universities should fund themselves through speculation and financial instruments. Sounds way too detached from the actual purpose of the institution.

Would it make sense for hospitals to do the same? Why not theaters next? In the end, if keeping the balance in check is the main thing an istitution is bound to do, why not stop doing education (= expenses) and just focus on investments?


> Sounds way too detached from the actual purpose of the institution.

I literally don't understand what you mean. The purpose of the institution is to educate people (and other stuff.) They do that by investing money that they were given for this exact purpose.

What on earth could the problem with that be?

No only are they providing education to people, often for free, they're also lending people money to build and develop businesses along the way.

I think you've got some idea they have their billions in a Duck McScrooge style vault? They don't - being invested means they lend it to people to build things. It's all being actively used.

> Would it make sense for hospitals to do the same?

Many hospitals and health organisations have endowments. For example the Wellcome Trust has an endowment of $37 billion. I think they funded my wife's PhD in cancer.

> Why not theaters next?

Many art institutions have endowments. For example the Getty uses a $7 billion endowment to fund the arts so they're preserved for and accessible to people like you.

What is the issue you see here? If they kept it as cash in the bank it'd depreciate rapidly and they'd end up with none left.


It's just a very foreign concept to me as a european. Perhaps I'm more inclined to the idea that institutions like education and healthcare should be publicly funded and shouldn't aim to be economically sound, as some services can only be done properly at loss.

In 1992 in Italy hospitals became "hospital companies", meaning what was once a public service with the sole aim of providing the best possible treatments to citizens for no cost, became at all effects a business (although still state owned) with incomes, expenses and a budget. You may think this makes sense, but since then our healthcare quality has tanked.

One notable side effect is that all hospital companies cut on the intensive treatment beds to save on costs, which backfired heavily during covid since we didn't have enough beds and many patients died without proper treatment as a consequence.


> It's just a very foreign concept to me as a european.

But it comes from Europe.

https://en.wikipedia.org/wiki/Financial_endowment#History

https://en.wikipedia.org/wiki/Financial_endowment#Modern_col...

> incomes, expenses and a budget

Government-provided healthcare also has an income, expenses, and a budget. They don't just spent whatever they want with no planning and no income.


I mean, the roman empire, ancient greece and in modern times Britain. Britain is European as much as USA is. Not really common in contemporary Europe.

The article itself notes that it's common practice only in North america, and when it is used outside of USA it's in a different way:

>In the United States, the endowment is often integral to the financial health of educational institutions. Alumni or friends of institutions sometimes contribute capital to the endowment. The use of endowment funding is strong in the United States and Canada but less commonly found outside of North America, with the exceptions of Cambridge and Oxford universities

> Government-provided healthcare also has an income, expenses, and a budget. They don't just spent whatever they want with no planning and no income.

Of course, but there is a big difference in having a regular accountability and starting to think in terms of business. Suddenly intensive treatment beds become "centers of cost", not vital but expensive tools required to save lives.


The whole point is that you don’t draw down the endowment. It’s a long term investment.


> they had an operating expenses of $5.0 billion in 2021

Isn’t that a crazy number? How much of it goes to a bloated bureaucracy?


You are probably under-estimating the size of Harvard. That isn't to say that large organizations tend to be ... well large organizations, but Harvard is huge.


You are probably over-estimating the size of Harvard. It’s not that huge for an R1 institution. Obviously, they have to pay huge money for cutting edge equipment and high salaries for top talent. But I don’t think that Harvard is protected from any administrative bloat so endemic in higher education. And since Harvard commands so much money the problem can theoretically be much worse than with state universities and LACs.


The specifics of administrative staff at a place like Harvard are doubtless different from your typical comparable company (What does a Dean of $X do???) but probably pretty similar in principle. Maybe less directly influenced by market pressures but you just can't operate a large organization in the same way you organize a small one.


> I mean I get that (not that a bank isn't a financial instrument...) but to what ends?

Tenure means a guaranteed job for life, and the university needs the money to guarantee it. The position exists even if there aren’t sufficient student or research grants to fund it.

Harvard is a bad example to use here because they are so massive. Other schools aren’t situated as well. And yea the endowments have taken a life of their own but tenure is a part of why they exist.


>There's more growth than that

There has been an almost unprecedented ten year period of growth in the equity markets. And the big endowment which I keep my eye on at least has done significantly better than the market. It's prudent not to count on that continuing--not that the school could turn on a dime with respect to their revenue mix anyway.


Oh so they're usurers.


Interesting quantitative take studying chord distribution. The basic takeaway seems to be that if you re-phrase a song into a different key you can play a lot with just a few chords (I IV V), and even more with a few others.

While I would agree that you'll be okay playing any major scale in C major (or whatever other major key choose to learn), playing a song in a minor scale on a major scale just doesn't sound quite right. So, I'd double all of the numbers on their final table to account for learning a full set of major and minor chords.


If you know I, ii, iii, IV, V, vi in C then you'll be able to play tunes in A natural minor. Add III to cover A harmonic minor, and II to cover A melodic minor

In real life though the key usually depends on the singer's vocal range, so you won't really get away with learning just one key


In A harmonic minor the III is already a C, you can make it augmented but that is not very common. You generally change the v to a V and keep the VII diminished in both cases where as it is major in the natural minor.


He was saying that the III of C major is the V of A minor, and so if you are planning to play in A minor using a set of chords pulled from C major, you may want to add a III alongside the iii.


Ah, when phrased that way it makes sense. I misunderstood. We're just saying the same thing in two different ways.


Yes, that's a good point in the case of six keys (I think my point holds for just learning three), but it is not the one made that the article seemed to make.


But A minor is the same notes as C major.

The chords in the key of C major will be the same chords in the key of A minor, although their patterns might be different.


This is only partially true. The chords in A natural minor will be the same but it's much more common to hear a dominant fifth using the harmonic minor so you would change the Em to E especially in classical and jazz music.


nit: jazz is the melodic minor (raised 6th as well). Such a beautiful sound; and the modes allow you to get the altered scale (7th mode of melodic minor) which sounds good(?) over the dominant


And not just that, usually they can only get it back if you're fired with cause.


> The unfortunate truth about data is that nothing much can be done with it

This is a fairly strong statement that goes against a lot of other work in data science and information visualization (John Tukey, Edward Tufte, Jacques Bertin, Hadley Wickham, ...). For example, see [0] and [1].

[0] https://en.wikipedia.org/wiki/Exploratory_data_analysis [1] https://courses.csail.mit.edu/18.337/2015/docs/50YearsDataSc...


You are leaving out a very important part of the sentence - "until we say what caused it". If you listen to the first few lectures you'll understand exactly what he intends with this sentence.


This cleaves very close to an aphorism I stole mercilessly many years ago: charts are for asking questions, not answering them.

“What caused it” is the answer, and a graph can reveal just as easily as it can conceal the cause. Lies, damn lies, and statistics.


To your point...Data has context. It has a source. It likely has flaws and/or (so to speak) bias. To get anything of it It's essential to understand what went into it. Else you'll deceive yourself or your stakeholders and bad decisions will be made.


Thanks, though I actually meant to copy the entire thing (my fault).

My point was that a lot of people working in data analysis would (strongly) disagree with the idea that we need to model the data in order to do anything with it. Visualisations and tabulations can tell a lot without any mathematical formalism.


This is taking the quote completely out of context, it's not the data itself that conveys useful information, it's the data combined with a causal model!


> The unfortunate truth about data is that nothing much can be done with it until we say what caused it

Nonparametric methods say 'hi'.


Guidelines | FAQ | Lists | API | Security | Legal | Apply to YC | Contact

Search: