I think you underestimate the computational ressources that the mentioned (and similar-kinded) scientific projects needed. Also consider how much computational ressources people invested into cryptocurrency mining.

No, I think the reasons are different:

- Many companies that train AI model use training data which must not be distributed for copyright reasons (and using it is a legal gray zone)x.

- Also consider that the amount of training data is insane. Scientific projects (and cryptocurrency mining, too) have the property that typically the amount of data (storage requirements) is small (or at least the computation can be partitioned so that each sub-task needs little data), but the required computing ressources are insane.

- AI companies consider a huge part of their training data as their "secret sauce" (they often even paid lots of money to generate it, for example by paying world-renowned experts for writing an answer for some important question).

Thus: Yes, the required computation ressources are huge, but this is a problem for which I consider it to be plausible that it can be solved. The real problems are in my opinion different.

Just some thought: Wouldn't it make sense to build some kind of volunteer computing project to train the next-generation LLM by volunteers, similar to the BOINC [1] projects or Folding@home [2]?

N.B.: BOINC was particularly famous for SETI@home (completed), Einstein@Home, Rosetta@home and PrimeGrid.

I still remember the time when Einstein@Home was in its heyday, and many people who loved putting together fast PCs contributed sometimes even for the reason of showing off in the statistics [3].

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[1] https://en.wikipedia.org/wiki/Berkeley_Open_Infrastructure_f...

[2] https://en.wikipedia.org/wiki/Folding@home

[3] https://einsteinathome.org/de/community/stats

Many employers' understanding of programming is stuck in the era of the 1970s. At that time, this was indeed what programming was about. Since that time, programming has evolved a lot to encompass much more complicated concepts.

Because the premise that coding has been solved is false. :-)

I would rather claim that for my personal/hobby software, I apply a much higher quality standard than for the software that I write at work.

Believe me: I would love to apply the same high (or even higher) standard at work, too.

The problem rather is that it is a very common situation that bosses actively prohibit me from rocking the boat too much. If I make (I think typically good) suggestions, I often get very stern reactions.

At work, elegance is not celebrated - just implement the tasks of users who don't care about quality or elegance.

You clearly write from the perspective of the US-American university system.

In Germany, basically everybody can enroll into a computer science program at a university, assuming the person has a Abitur (Allgemeine Hochschulreife) certificate (these terms are difficult to translate into English) from the grammar school [1]. So, "have it made to college" is like "not having been a complete failure in school". [2]

So, making it to the university is no achievement in Germany, and also no sign of motivation either.

> If these courses are so important, they should be taught in a way that students can understand.

These courses are taught in a way that students can understand, but not in a way where you can afford to slack off.

It is basically a consensus in Germany that a university is clearly a wrong place for you if you are incapable of closing knowledge gaps on your own (for example by reading books from the library), and you don't have the self-motivation to sit over the lecture material for hours to finally understand it.

So yes, I would say that among the possible options, weed-out courses in mathematics are in my opinion likely the least bad one.

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[1] In years where there was an insane demand for places at the university to study computer science such as during the dot-com bubble, there were some restrictions (numerus clausus), but for computer science, this was always the exception to the rule.

[2] There exist good reasons for puns like "Abitur: nichts gerafft und doch geschafft" (Abitur: Didn't get a thing, yet still passed) or "A-bier-tur" (a portmenteau of "Abitur" and "beer", which suggests that even pupils who are more into drinking than learning typically get their Abitur certificate).

> https://en.wikipedia.org/w/index.php?title=Algebra_over_a_fi...

> https://en.wikipedia.org/w/index.php?title=Algebra_over_a_fi...

> https://en.wikipedia.org/w/index.php?title=Associative_algeb...

The latter is what aground asked for in https://news.ycombinator.com/user?id=agrounds

> I’m surprised that rings and algebras would come up in a CS degree. What algorithms topics used those concepts?

I studied computer science (and mathematics) in Germany. I am very certain that this was taught to computer science students, even though (compared to the lectures for math students) the lecturer did not get very deep into these topics.

> most CS students would have been terrified of that.

This is a feature, not a bug. :-)

Seriously: In Germany, the "math for ..." lectures often are intended to be "weed-out lectures" so that students who simply are not qualified for their major get to quit their degree course fast (either by realizing that the degree course is too hard for them, or by (typically) failing math exams so that they get exmatriculated), so that they don't waste many semesters on a degree course which they simply are not suited for.

In my opinion: a lot of math terminology is much older than computer science terminology, so the origin of the names of many concepts in math is much more obscure for today's people than CS terminology currently is (and least if you are not into history of science/math).

On the other hand, in my observation a lot more terms in computer science are based on obscure (often pop-cultural) puns. I guess in 50-100 years these CS terminology might seem even more obscure for then-contemporary people than math terminology is today.

* Determinant calculation:

- The Samuelson–Berkowitz algorithm is best understood in terms of general rings

- The Faddeev–LeVerrier algorithm and determinant calculation using Gaussian elimination work on rings with specific properties (for the Faddeev–LeVerrier algorithm the restriction is on the characteristic of the ring, for Gaussian elimination the ring must be an integral domain (ideally a field)).

* Ring-learning with errors (for post-quantum cryptography and homomorphic cryptography). Here, a specific ring is the central object.

* Number-Theoretic Transform (NTT): Basically a generalization of the Fourier Transform to the ring Z_n. Important for arbitrary-precision integer arithmetic

* Chinese Remainder Theorem. Often only formulated for the ring Z, but it can be generalized to larger classes of rings. Used for example in Shamir’s scheme for secret sharing (cryptography)

* The theory of BCH and Reed-Solomon codes uses a specific ring

* The AKS Primality Test (a really deep result in computational number theory) uses the ring Z_n[X]/(x^r-1).

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Algebras:

Very often, a ring is constructed from another ring. Examples:

* the polynomial ring R[X_1, ..., X_n]

* The ring of (square) matrices over a ring R

So, using algebras in algorithms often means: "we want to make use use of this additional structure that our (more sophisticated) ring has)". (Associative) R-algebras formalize this concept of "ring with additional structure".

To just give one algorithm for polynomials:

* Buchberger algorithm for computing a Gröbner basis

Other examples:

* Clifford algebras for a lot of geometric problems (special case: quaternions (a 4-dimensional \mathbb{R}-algebra) for rotations in \mathbb{R}^3).

* If you are willing to also consider semi-rings (in this case: tropical semi-rings): the Floyd-Warshall algorithm for finding shortest paths and the Viterbi algorithm for finding the most likely sequence of states in a Hidden-Markov Model (HMM) can very elegantly formulated using the matrix semiring over the tropical semiring.

"No jargon" is alwys in reference to some assumed knowledge model.

For example, when I claim that my own math texts include "no jargon", this assumes the knowledge of a person who has studied, say, mathematics, physics or computer science.

All these terms were taught to computer science (and of course math, physics, ...) students as part of getting their degree in computer science, because these concepts are important for many algorithms.

Many people believed the same about the Jacobian Conjecture.

> AI meets Cryptography 1: What AI Found in Cloudflare's CIRCL

> https://blog.zksecurity.xyz/posts/circl-bugs/

Even before electric cars got more common, basically everybody knew what an electric car is.

People have very different, often contradictory opinions what the "common good" is.

I would even claim that this question is a special case of the millennia-old philosophical problem of defining what "good" actually means (i.e. defining ethics), which has resulted in myriads of different opinions:

> https://en.wikipedia.org/wiki/Outline_of_ethics

Honest question: where should these very smart people work then in your opinion? Nearly all suitable-looking, "smarter" ideas that come to my mind will bring less revenue/profit in expectation than, for example, letting them maximize ad impressions.

I of course wish we lived in a "more intelligent" world where this is not the case. :-(

Not everybody is adept at interpreting such subtle signs. A particular problem is when bosses say things like "Your opinion is appreciated.", "I value honest feedback.", blabla, but give different body signs.

In my opinion, this problem is less marked in Germany than the USA, because people tend to use of such sugar-coated lies much less in Germany.

There is no boss involved in the HN discussion. :-)