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I am trying to think/know about something, but I don't know if my base premise is plausible. Here we go.

Sometimes when I'm talking with people about pure mathematics, they usually dismiss it because it has no practical utility, but I guess that according to the history of mathematics, the math that is useful today was once pure mathematics (I'm not so sure but I guess that when the calculus was invented, it hadn't a practical application).

Also, I guess that the development of pure mathematics is important because it allows us to think about non-intuitive objects before encountering some phenomena that is similar to these mathematical non-intuitive objects, with this in mind can you provide me historical examples of pure mathematics becoming "useful"?

InsideOut
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  • I would imagine [K-theory](http://en.wikipedia.org/wiki/K-theory) as used in String Theory is an example. – JavaMan Jan 17 '13 at 05:40
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    Newton invented his *fluxions* (i.e. his calculus) in order to compute the orbits of celestial objects that move according to his law of gravitation. The foundations of calculus as pure mathematics were not established until the 18th century. – Ron Gordon Jan 17 '13 at 05:42
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    @JavaMan: I think there might be some debate as to whether string theory is useful ... – Henry B. Jan 17 '13 at 05:49
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    @HenryB or whether an application of pure mathematics to pure mathematics is what the OP had in mind. – Willie Wong Jan 17 '13 at 08:48
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    Two similar posts under the heading "Useless math that became useful" [here](http://math.stackexchange.com/q/260877) and [on MO](http://mathoverflow.net/questions/116627/). – Martin Jan 17 '13 at 08:52
  • What about math for computer algorithms? like Googles search algorithm? – mal Jan 17 '13 at 11:35
  • @JavaMan, what is String Theory useful for? You know these books? http://www.amazon.com/Not-Even-Wrong-Failure-Physical/dp/0465092764; http://www.amazon.com/The-Trouble-With-Physics-Science/dp/061891868X – alancalvitti Jan 17 '13 at 15:22
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    When I'm approached with questions regarding why anyone should work on math that (as of yet) has no practical use, I respond with the same question regarding art. – Buhb Jan 17 '13 at 15:30
  • For me, it would be interesting to ask for applications of mathematical inventions later than 1970. I have the impression that only a very small fraction of "modern" maths makes its way into textbooks or into the tool box of todays practitioners. – Axel Kemper Jan 17 '13 at 13:44
  • @rglordonma: That seems to answer the converse question as an example of non-rigorous applied mathematics being given a rigorous foundation, rather than pure mathematics being given an application. – bradhd Jan 17 '13 at 17:55
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    @Brad I think rglordonma was answering the OP's point about calculus being invented without a practical application, which is false as it was invented exactly for a practical application. – user50229 Jan 17 '13 at 20:02
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    @Buhb, I think that's fine and a valid point, but I also kind of feel like it's way too defensive of a response. Someone interested in going into pure mathematics as a career has every right to ask whether or not developments in the field have utility outside of their intrinsic beauty. And I don't see what's wrong with asking what those uses are, especially when there's this nominal distinction between "pure" and "applied" math. The hyper-defensive responses tend to give people the impression that pure math has no practical use. – anonymous Jan 18 '13 at 04:03
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    This is an interesting question in itself but I intensely dislike the premise of your discussion with friends: do they dismiss art because it has no practical utility? Music? Literature? It’s all well to give them examples of usefulness but I find it more important to call them out on their fundamental misconception. To paraphrase Feynman, math is like sex: occasionally something useful comes out of it but that’s not why we are doing it. – Konrad Rudolph Jan 18 '13 at 12:16
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    This is an interesting with answers showing that pure mathematics is not useless at all. By the way, I hope people think it is more interesting then the Batman question. – AD - Stop Putin - Jan 18 '13 at 13:54
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    @AD. Well... Pehaps if I reformulate the question: "Can provide me examples of Batman using pure mathematics?" – Red Banana Jan 18 '13 at 14:31
  • While I don't know enough about the uses of these subjects to write a proper answer, I'd like to link to [Engineering Applications of Noncommutative Harmonic Analysis](http://www.amazon.ca/Engineering-Applications-Noncommutative-Harmonic-Analysis/dp/0849307481) and [Dual-Number Methods in Kinematics, Statics and Dynamics](http://www.amazon.com/Dual-Number-Methods-Kinematics-Statics-Dynamics/dp/0849391156), which apply some typically 'advanced/pure' math to standard engineering problems. – Robert Mastragostino Jan 18 '13 at 16:13
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    @GustavoBandeira I suggest you do that and see what happens... :) ... Where did my English go when I wrote that? *Errata:* This is an interesting *question* with answers showing that pure mathematics is not useless at all. By the way, I hope people think *this* is more interesting then the Batman question. – AD - Stop Putin - Jan 18 '13 at 18:59
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    @AD.: While we're copyediting: *than* not *then*. :-) – cardinal Jan 20 '13 at 03:30
  • @Legoman: What was the point in making that edit? – Asaf Karagila Feb 26 '17 at 10:43
  • I voted for reopen this question, but I digited _edit and reopen_ instead of _reopen_ and then I can't voted without I little edit. That's all. So I changed a word to vote. – InsideOut Feb 26 '17 at 10:45
  • @Legoman: There is a "cancel" button, which takes you back to the review. If nothing else works, hitting Escape usually gets you back from editing pages. – Asaf Karagila Feb 26 '17 at 15:54
  • @AsafKaragila thank you very much – InsideOut Feb 26 '17 at 19:00

33 Answers33

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Here are few such examples

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    Public-private key cryptography, the currently typical crypto for SSL connections, was also created by mathematicians: http://en.wikipedia.org/wiki/Public-key_cryptography#History. You could say "every time you shop online, your security is protected by math." – Nathan Long Jan 17 '13 at 14:18
  • @NathanLong I would only say "may be protected" , since it wasn't proven that factorisation is NP... Maybe someone actually knows a better algorithm... – N. S. Jan 17 '13 at 16:17
  • @N.S.: we know integer factorization is in $\mathcal{NP}$ (it's trivial to show), although we're not sure where exactly it fits in $\mathcal{NP}$. The real issue is pinning down exactly which complexity class factorization fits in, as well as proving $\mathcal{P}\ne \mathcal{NP}$. – Reid Jan 17 '13 at 16:36
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    To add to the first bullet, many types of cryptography are based on pure number theory that was developed long before the cryptography. I also think vectors started out on the pure side before physics started using them, but I don't have a reference off hand. – TimothyAWiseman Jan 17 '13 at 17:14
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    @N.S. Of course it's NP - I think you mean "We don't know it's NP-complete." In fact, it almost certainly isn't, since we (rather quickly) devised a way to quickly factor using quantum computers, but there is no known algorithm for quickly solving any NP-complete problems with quantum computers. In any case, I don't see how that's at all relevant - the fact is, RSA is the most widely used public-key crypto algorithm in use today, is still considered secure, and relies on the difficulty in factoring, so it fits the question. – BlueRaja - Danny Pflughoeft Jan 18 '13 at 05:57
  • For the crypto, you could also add crypto based on lattices, coding theory, hyperelliptic curves, pairings... – TMM Jan 22 '13 at 19:01
  • That some academic has published a paper with the words "application" or "robots" or "computers" or "data" in it, does not mean that actual engineers are using the result. For example Ayasdi (AT) has been a failure. – isomorphismes Feb 26 '17 at 07:37
  • Jacquard's loom predates George Boole's birth http://cgi.csc.liv.ac.uk/~ped/teachadmin/histsci/htmlform/lect4.html by a decade. – isomorphismes Feb 26 '17 at 07:41
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Negative numbers and complex numbers were regarded as absurd and useless by many mathematicians prior to $15^{th}$ century. For instance, Chuquet referred negative numbers as "absurd numbers." Michael Stifel has a chapter on negative numbers in his book "Arithmetica integra" titled "numeri absurdi". And so too were complex/imaginary numbers. Gerolamo Cardano in his book "Ars Magna" calls the square root of negative numbers as a completely useless object.

I guess the same attitude towards Quaternions and Octonions would have been prevalent, when they were initially discovered.

This is from my answer to a similar question here.

Below are some uses of negative and complex numbers.

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    By the time of Quaternions things had actually changed, and they were sought for for a long time in the hope they would be as good for modeling 3d movements as Complexs are for 2d. Unfortunately they came a bit too late and Linear Algebra had already eaten most of the cake – Thomas Ahle Jan 17 '13 at 11:52
  • Sadly I think many people today think negative numbers are absurd... why calculate 4 + -2 when you could simply do 4 - 2 instead? I'm not sure anyone thinks math is useful until they actually see it doing something. I like your answer though, very interesting and easy to explain to anyone. – Abhi Beckert Jan 17 '13 at 20:04
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    I hope people that have difficulties to accept complex numbers know why they accept real numbers, those are much harder to describe. – AD - Stop Putin - Jan 18 '13 at 11:28
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    This answer doesn't really say what is "useful" about complex numbers. Or negative numbers, for that matter... – AShelly Jan 18 '13 at 13:54
  • @AD: in what way are complex numbers easier to describe than real numbers? That piqued my interest. – Herng Yi Jan 19 '13 at 15:49
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    @HerngYi The point is that (at least once you have the reals) the complex numbers are just a 2-dimensional vector space over the reals (a complex number $a+bi$ is easily described as the pair of reals $(a,b)$ with a strange-but-simple multiplication rule. By contrast, the reals have to be constructed in a fundamentally infinitary manner, and almost all reals have no finite descriptions. – Steven Stadnicki Jan 19 '13 at 16:52
  • @AShelly I didn't add the uses of negative and complex numbers earlier, since there are plenty to say about these uses. Anyway, I have now added a wiki link. –  Jan 19 '13 at 18:19
  • @AD: but then can't we say complex numbers have to be described in an even more complicated "2D fundamentally infinitary" manner, and real numbers are easy to define as $\{z + z^\ast\ |\ z \in \mathbb{C}\}$? – Herng Yi Jan 20 '13 at 00:31
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    @HerngYi What I mean is that constructing the complex number from the reals is nothing like constructing the reals from the rational numbers (that is using Cauchy sequences or Dedekind cuts). If this is new to you, I suggest you look it up on wikipedia at least. – AD - Stop Putin - Jan 20 '13 at 19:53
  • @AD. I think the problem with complex numbers lies within the terminology we use... They are "imaginary", while real numbers are "real" ;) – N. S. Jan 27 '13 at 04:16
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    @N.S. You got a point there, perhaps we should vote for a change there? How about better numbers? :) – AD - Stop Putin - Jan 27 '13 at 06:37
  • @AD. "Complete numbers" might be a better alternative for "Real numbers" and "Closed numbers" might be a better alternative for "Complex numbers" :) –  Jan 27 '13 at 06:40
  • @Marvis Sounds fine, I fix the tags and wikipedia pages. ;) – AD - Stop Putin - Jan 27 '13 at 07:46
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    @AD: I'd say people accept real numbers because those can be used for things we routinely do with numbers: Specify lengths, angles, ratios of quantities ... however everything we measure is inherently found to be real (this is even true in quantum mechanics: measurement results are eigenvalues of hermitean operators, hence real). You can point to a square and speak about the length of the diagonal. You can point to a circle and can talk about the circumference. However there's nothing you can point to where you immediately see "oh right, *that* one has the value i". – celtschk Jul 26 '13 at 14:30
  • @ThomasAhle the quaternions were discovered Hamilton in 1843, who believed they must play a fundamental role physics and geometry. Most people thought they were a rather cute curiosity. Their importance was not really appreciated until Dirac discovered the Dirac equation. Now they play a fundamental role in our understanding of spin, often described as "the square root of geometry". I highly suggest watching any of Michael Atiyah's lectures, he invariably tells this story in most of them – Kai May 11 '21 at 23:13
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Here are some examples of pure mathematics that has shown to have real applications - however I am not sure of the origins.

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    Stochastic analysis came from finance. Louis Bachelier was the first one to treat Brownian motion mathematically in his thesis on speculation. I would also be curious where optimal control is supposed to have originated outside applied math. – Michael Greinecker Jan 17 '13 at 08:55
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    I am doubtful that the subjects of PDE or (discrete) Fourier transforms could be considered pure math, historically. – KCd Jan 17 '13 at 10:50
  • @MichaelGreinecker You are right regarding Bachelier. When it comes to the history perhaps I wrote the post a bit too fast, I will now edit.. – AD - Stop Putin - Jan 17 '13 at 11:06
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    In the case of the heat equation, I thought Fourier presented a method without a solid foundation and his paper was rejected. [Riemann then provided a basis](http://www.maths.tcd.ie/pub/HistMath/People/Riemann/Trig/Trig.pdf), including refining the definition of integrable (the birth of the Riemann integral now standard in introductory calculus). This seems more like the reverse direction, in which the application led to developements in pure mathematics. – Michael E2 Jan 17 '13 at 15:05
  • @MichaelGreinecker, did you mean optimal control or stochastic control? - two distinct fields, which overlap in optimal stochastic control. – alancalvitti Jan 18 '13 at 03:50
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    @MichaelGreinecker, what evidence is there that stochastic calculus is useful in finance? Surely it is studied in finance and earned Merton & Scholes the 1997 Econ Nobel - but don't forget (1) there is no Brownian motion in nature in the mathematical sense (2) the hedge fund LTCM which counted the Nobel winners as principals imploded in 1998 due to some bad bets. Does the mathematics lead to statistical hedging or speculation and guesswork? – alancalvitti Jan 18 '13 at 03:54
  • @alancalvitti Well, in the real world (i.e. non-mathematical world) we may strive to accomplish evidences of matter trying to a adopt mathematical models. But of course these are nothing else than models. – AD - Stop Putin - Jan 18 '13 at 07:13
  • @alancalvitti The Black-Scholes model is based on geometric BM, not BM (which is the basic difference to what Bachelier did). It is certainly applied a lot, how "useful" it is depends on what questions you are interested in. – Michael Greinecker Jan 18 '13 at 07:42
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    @MichaelGreinecker, you're splitting hairs - it doesn't really matter if its BM versus geometric BM versus long-tail Levi flights etc. These are all models, they're not the real world. - A recent graphic in Economist magazine showed that every year starting in 2005, on every continent, as many hedge funds fold as are created. So are they hedging or speculating? – alancalvitti Jan 18 '13 at 16:31
  • @alancalvitti I hav never heard anyone stating that these are not models, they are the world. I don't consider the behavior of hedge funds on topic on MSE. There is a stackexchange site for quantitative finance. – Michael Greinecker Jan 19 '13 at 19:58
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    @MichaelGreinecker, the models are applied to decision-making in the real world. I can give you several examples where, after a crash, people realize, gee we were confusing the models for the real world, but not before the crash. – alancalvitti Jan 19 '13 at 20:11
  • @KCd but what is pure math at all? Every branch of pure math listed on wikipedia, for example, has its applications: Analysis -> physics, computer science, statistics; algebra -> physics, computer science; geometry -> physics, computer science; number theory -> computer science; discrete mathematics -> computer science. So, there's no pure math then? – Pavel Feb 27 '16 at 22:05
  • @paulpaul1076, you must have misread the question and my comment since your comment misses the point. The question is asking for examples of pure math *becoming* useful, so the math should first have been developed for its own sake (the math is pure first) and only later found real-world uses. My comment was in response to the suggestion in this answer that PDE, for instance, is an example of that. It is not, since the whole point of PDE in its historical origins was to model physical processes. – KCd Feb 27 '16 at 23:07
  • @KCd no, I am not blaming you for anything, I just wanted to know what exactly is pure math, so you can consider my comment to be unrelated to the question. – Pavel Feb 27 '16 at 23:29
  • @paulpaul1076, when you write "algebra" or "geometry" you are subsuming really huge areas of math into a single label, and many parts of those areas may still have no applications outside of math (e.g., many advanced areas of number theory and algebraic geometry, like class field theory). Pure math is the part of math not being developed for applications outside of math, and there is still a lot of it. A look at the latest volumes of math research journals will show you some of it. – KCd Feb 28 '16 at 00:53
  • @KCd thanks a lot. Is theoretical computer science a branch of pure math? I mean the p vs np problem doesn't seem to be something significant outside of TCS itself. – Pavel Feb 28 '16 at 05:21
  • @paulpaul1076, you'd have to ask someone in theoretical computer science if they consider their work pure math. – KCd Feb 28 '16 at 16:06
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    @paulpaul1076 Certainly there are mathematical results that would be *hard* to apply outside mathematics. For example the Banach-Tarski theorem and Gödels incompleteness theorem. – AD - Stop Putin - Feb 29 '16 at 23:16
  • @AD. thanks for an example – Pavel Feb 29 '16 at 23:18
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    The question asks for pure mathematics which _later_ became useful – not for just any connection between industry and mathematics. – isomorphismes Feb 26 '17 at 07:43
  • @isomorphismes Yes.. – AD - Stop Putin - May 26 '17 at 08:57
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The discussion of conic sections by the ancient Greeks, see the wikipedia article, gave the basic definitions required by Kepler to formulate his law of planetary orbits. Of course the Greeks did not have term "pure mathematics".

An example from pure mathematics of the 20th century is the applications of category theory to computer science.

People also forget that the notion of the graph of a function was invented by Descartes and of course is now ubiquitous in our daily papers, to show clearly how bad things are getting! For more information on the invention of Cartesian coordinates, see the wikipedia entry on Descartes.

Ronnie Brown
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    This might be the example that had the most *direct* impact on the largest number of "normal" people. Who would have ever thought that [Microsoft would add Monad Comprehensions to BASIC](http://MSDN.Microsoft.Com/library/vstudio/bb385100.aspx)? – Jörg W Mittag Jan 17 '13 at 13:44
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    Just a comment on the term "abstract nonsense": **abstraction** is about **analogies**, and thus about saving work, doing several things at the same time. The term "abstract nonsense" comes from those who think maths ought to be hard, about solving hard problems, whereas others think one job of maths is to make difficult things easy, by developing the "right" language. It was said by Bott that Grothendieck was prepared to work very hard to make things tautological! – Ronnie Brown Jul 26 '13 at 08:47
  • @RonnieBrown: I think your interpretation of the term "abstract nonsense" is overly negative. According to https://en.wikipedia.org/wiki/Abstract_nonsense the term was coined by Steenrod and promulgated by Eilenberg-MacLane, all of them using it in a self-deprecating but affectionate way. – Nefertiti Feb 27 '17 at 13:53
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Euler's Theorem from pure number theory is at the heart of the RSA open key encryption system.

Ittay Weiss
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    Actually, it's Fermat's little theorem that is the basis of RSA. There is a widespread misconception that it is based on Euler's theorem (even though the original paper used Fermat's little theorem). The problem with relying on Euler's theorem is that it suggests the encoding and decoding procedures may not be inverses on messages that are not relatively prime to the modulus. But in fact there is no problem, by the proof using Fermat's little theorem. See the last section of the Wikipedia page on RSA at http://en.wikipedia.org/wiki/RSA_%28algorithm%29 – KCd Jan 17 '13 at 10:46
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Complex numbers are very useful in electrical engineering. An imaginary number is a hare-brained idea if you think about it: square of this thing is -1?????!?

And yet, it's very valuable when calculating alternative currents.

The "trouble" with pure mathematics or ideas is that empirical world is open world (not closed like in mathematics), and as we build newer and newer practical things on top of it, you never know what's useful.

Say, lambda calculus and functional programming. If you asked SW engineer 30 years what's functional programming good for, you'd most often get an answer "feh! silly academic toy! useless!".

Fast forward 20 years to MapReduce applied by Google and it turns out that yes, it's actually quite practical.

Werner von Braun: "research is what I'm doing when I don't know what I'm doing". Combine that with Einstein's "there's nothing as practical as good theory". Result of this combo is: since we do not know which theory is good, we have to test them; but how do you test something that you have not even formulated as pure theory first?

"Bottom up" is such an approach, but not everything can be worked out this way.

Although I feel you focus on the wrong problem: applicability of pure theory is trivial, just check if it works in practice, try to apply theory of gravity by Aristotle to shooting cannonballs and see it doesn't work (a stone goes up on a curve and at the highest point of trajectory falls vertically down to the ground - has Aristotle never thrown stones or smth?).

A harder problem is when pure theory deceives us into wrong representation of real world, for example classical logic has done huge conceptual damage to knowledge representation in AI and the way we think about the problems (all those silly logical rules that don't work, akin to the "witch" skit from Monty Python's Holy Grail).

P.S. Certain paper on fast resolution of big Horn clauses is theory behind pattern matching used for programming in Prolog and Erlang (maybe there are more applications I don't know of), although I can't remember the name of the paper.

Martin Sleziak
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  • Could you provide more information/links re: the ways in which "classical logic has done huge conceptual damage to knowledge representation in AI". I'm genuinely interested. – Ezequiel Muns Jan 18 '13 at 00:15
  • Hmm there are not many papers on this I'm afraid, my claim is mostly conclusions I drew after writing my master's thesis on knowledge representation and engineering. I wrote a summary of this to organize my thoughts on subject, but did not publish it anywhere - not that I'm alone on this, see this article by Shirky: http://www.shirky.com/writings/semantic_syllogism.html (he has made some errors in this article and I know he writes partially for publicity but many of his arguments hold). – mrkafk Jan 18 '13 at 10:44
  • Most of the useful contributions to AI have never had anything to do with classical logic: fuzzy sets, semantic graphs, CYC's "sea of assertions", Roger Schank's CD formulas, story understanding, etc. I don't think this is an accident, useful extensions to logic like constraint-based programming (see ECLIPSE extension to Prolog) mask a deeper problem: the hard AI part is conceptual work, that is, working out sound premises, not inferencing afterwards once we pretend the premises are true. My bet is that the road to hard AI leads through fancier clustering or smth similar, not through logic. – mrkafk Jan 18 '13 at 10:51
  • Although not direct critiques of logic, following works are useful to understand context: "Knowledge Level" by Allen Newell, "Structure of Intelligence" by Ben Goertzel, partially with a good of somewhat weird philosophy is "Why Heideggerian AI Failed and how Fixing it would Require making it more Heideggerian" by H. Dreyfus. Those works illuminate the context that from my viewpoint demonstrates that apart from very narrow niches classical logic is mostly inadequate as basis of modelling human knowledge. – mrkafk Jan 18 '13 at 11:00
  • @EzequielMuns: oops, sorry for not using your tag, you might not have got the notification. – mrkafk Jan 18 '13 at 15:57
  • @mrkafk: could you please link to your thesis? :) – Dan-George Filimon Jan 19 '13 at 09:44
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    @Dan-GeorgeFilimon: here it is: https://www.dropbox.com/s/o6mb944e5phsdh6/knowledge_engineering_for_knowledge_management.pdf , although it only its fraction concerns issues I have described, since the thesis subject is mostly "what has worked in AI re knowledge representation and processing" and so it is not direct critique of logic in concext of KM as such. – mrkafk Jan 20 '13 at 18:11
  • @mrkafk Thanks! – Ezequiel Muns Jan 21 '13 at 01:26
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Most of our current mathematical knowledge was developed to explain something already observed empirically. Going way back, many early civilizations had no concept of "zero" as being a numerical quantity; however, the concept of "nothing" or "none" existed, and eventually the Babylonians, around 2000 BC, began using symbols for "none" or "zero" alongside numerals, equating the concepts. Newton laid the foundations of what we know today as calculus (also developed independently by Leibniz) in order to mathematically explain and calculate the motion of celestial bodies (and also of projectiles here on earth). Einstein developed tensor calculus in order to establish the mathematical backing for general relativity.

It can also, however, happen in reverse. Usually, this is when "pure" math exhibits some "oddity", such as a divergence or discontinuity of an "ideal" formula that otherwise models real-world behavior very closely, or something originally thought of as a practical impossibility. Then, we find that in fact the real-world behavior actually follows the math even in these "edge cases", and it was our understanding of the way things worked that was wrong. Here's one from physics which touches on some of the most basic grade-school math and yet challenges those very foundations of thought: negative absolute temperature.

Temperature, classically, is the measure of thermal energy in a system. By that definition, you can never have less than no energy in the system; hence, the concept of "absolute zero". Most "normal" people hold to this concept and think of zero degrees Kelvin as a true absolute; you can't go lower than that.

However, the theoretical, more rigorous, definition of temperature has as its defining character the ratio between the change in energy and the change in entropy. As you add total energy to a system, some remains "useful" as energy, while some is lost to entropy (natural disorder). It's still there (First Law of Thermodynamics), but cannot do work (Second Law of Thermodynamics).

The graph of temperature using this definition has computable negative values; if entropy and energy are ever inversely related (entropy reduces as energy increases, or vice-versa), then this fraction, and thus the temperature, is negative. Even more interesting is that the graph of temperature as a function of energy over entropy diverges at absolute zero; the delta of entropy approaches zero for deltas of energy around absolute zero, producing infinitely positive or negative values with an undefined division by zero at the origin. That graph, therefore, predicts that absolute zero is actually a state not of zero energy, but of zero change in entropy, regardless of the amount of energy in the system. Absolute zero, therefore, could in fact be observed in systems with extreme (even infinite) amounts of energy, as long as no additional energy added was ever lost to entropy.

This used to be discounted out-of-hand; until recently, every thermal system known to man always exhibited a direct relationship between energy and entropy. You could keep adding all the energy you wanted, to infinity, and entropy would continue to increase as well. You could keep cooling a system all you wanted, until you took out all you could possibly remove, and entropy would decrease as well. Again, this is borne out by our everyday observations of the world; solid, crystalline ice, when heated, becomes more chaotic but generally predictable water, which when further heated becomes less predictable gas, and eventually decomposes into its even less predictable component atoms, which would further decompose into plasma.

However, work with lasers, and the theoretical behavior of same, gave us a thermal system that has an "upper bound" to the amount of possible energy we could add that remains contained within the system, and moreover, that limit was pretty easy to reach. This allows us to observe a system that actually becomes less chaotic as more energy is added to it, because the more energy that is in the system, the closer it gets to its upper limit of total energy state, and thus the fewer the number of particles in the system that are at a state less than the highest state (and thus the ability to accurately predict the energy state of any arbitrary particle is increased).

On the other side of the spectrum, recent news has reported that scientists have produced the opposite; they can get entropy to increase by removing energy from the system. Work with superfluids at extremely cold temperatures has demonstrated that at a critical point of energy removal from the system, particles within it no longer have sufficient energy to sustain the electromagnetic force that attracts them to and repels them from each other in their lowest energy state (which is also their most ordered state). They lose the ordered structure that defines conventional matter, and begin to "flow" around each other without resistance (zero viscosity). At that critical point, you have increased entropy as the result of removing energy; the particles become less predictable as to position and direction of motion when they're cooled, instead of our classical idea that things which are cooled become more orderly. At this point, we have reached "negative absolute temperature".

Thus, temperature seems to exhibit a "wraparound"; as energy increases to infinity, eventually the amount of it that can be in entropy will decrease, seemingly breaking the First Law of Thermodynamics and allowing us to get more energy back from the system than the incremental amount we added (but not more than the total amount of energy ever introduced to the system, so the First Law still holds). Because that threshold is attained (in an unbound system) at infinite energy states, we'll never get there with most of our everyday thermal systems, but we can see it in a bound system, and we can "wrap around" from the low end by removing energy to reach a negative absolute temperature. This is backed up by observance of the reciprocal of temperature, which is the thermodynamic beta or "perk". This fraction, by placing the zero entropy delta in the numerator, is perfectly continuous for all real values of the domain, including zero.

Najib Idrissi
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KeithS
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  • That is pretty damn cool. I was trying to wrap my head around this news of negative temperature for two weeks now. It seems that zero Kelvin is like a discontinuity of $-\frac1x$ at $x=0$. Approach from the positive you get colder and colder, but then... bam! You're back from the top! :-) – Asaf Karagila Jan 31 '13 at 20:38
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Group theory is commonplace in quantum mechanics to represent families of operators that possess particular symmetries. You can find some info here.

Ron Gordon
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Algebraic Topology has found applications data mining (thus to cancer research, I believe), in the field of topological data analysis. See http://www.guardian.co.uk/news/datablog/2013/jan/16/big-data-firm-topological-data-analysis

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Turing's development of computability which led to the theoretical basis of computing.

As a personal note, I take pride in dealing with models of ZF without the axiom of choice and all sort of strange consistency results. The only way an amorphous sets and D-finite combinatorics could be utilized for "practical uses" is when we prove that the universe is actually a good model for an infinite D-finite set, and we can apply all sort of crazy non-AC theorems to argue about properties of the universe.

The only reason this would turn out to be really awesome is that it may invalidate parts of quantum mechanics (see The Axiom of Choice in Quantum Theory).

Asaf Karagila
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  • Why not also remove the principle of Excluded Middle? - to be truly useful you should probably also not assume Axiom of Infinity, Hausdorff separation and move from equivalence classes --> tolerance relation. – alancalvitti Jan 18 '13 at 03:56
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Just to add another example:

Boolean algebra was developed in 1854: its abstract and maybe boring, but it set the basis for the development of digital circuits.

So all the digital devices that you use right one, are heavily based on abstract mathematics from 1854.

Diego
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I had a teacher that once told me that Riemann's new idea of measure (that the way we measure has to change depending on the manifold) opened the door to relativity's theory.

Also, one of the calculus pioneers was by Francois Viete, who allowed Leibniz and Newton to develop the machinery of classic mechanics.

Miguel
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  • I don't know anything about Francois Viete, but I do know that A. Einstein originally developed both special and general relativity without the idea of a manifold with a metric - those were later used to refine the exposition. – wolfen Jan 17 '13 at 20:38
  • @wolfen, Minkowski developed a lot of the geometry that Einstein applied to special relativity. – alancalvitti Jan 18 '13 at 03:58
  • @alancalvitti - I attended a class by O'Neil (RIP: http://www.palisadespost.com/obits/content.php?id=6764) at UCLA in which he said that Einstein felt self-conscious about his "observer A, observer B" approach, but went back to it again in his research after trying unsuccessfully to use more modern mathematical tools (metrics in particular). Nowadays, of course, relativity theory is used as a motivating example for studying differential geometry and Riemann manifolds - but it seems that those weren't the tools that Einstein actually used when doing his research. – wolfen Jan 19 '13 at 17:29
  • Yeah, Im not saying that he used those tools, or that Riemann inspired him, just that the change of perspective was in the air, not just in Einstein's head... – Miguel Jan 20 '13 at 22:13
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The implementation of the Fast Fourier Transform by Cooley and Tukey and maybe Shor's Quantum Algorithm to factor number in polynomial time, using the Quantum Fourier Transform...at least it might become useful somewhen...

draks ...
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Just look at the field of quantitative finance, financial mathematics(Brownian motion, Fourier Transformation ect.)

Le Chifre
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Too many to count, many "pure mathematics" in the past become "applied mathematics" now.

The problem with pure mathematics is that it has advanced too much for science and engineering to catch up now.

Btw, doing a PhD in any serious science and engineering discipline (even some social science subjects) is like doing some mathematics in the end, and of cause many of these mathematics used there were regarded as "pure mathematics" 100-200 years ago.

Martin Sleziak
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user55647
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  • If you go back a couple of centuries, they were just called scientists or "natural philosophers" - eg Newton. Universities up to the late 19th century didn't even have departments as such. – alancalvitti Jan 18 '13 at 04:02
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IMO any pure mathematics which is generated by a human brain (and there probably exists and most certainly will exist other kinds in the near future) is at least motivated by something which actually exists in the world of human experience.

But once the work actually gets underway on a new idea in some area it takes on a life of its own and will, when polished & refined, look very different from how it did at the outset. Calculus is a great example of a very refined area of mathematics - you can see this in the notation, which has been polished smooth by generations of heavy usage and is very powerful & expressive (and typically takes students a long time to learn well).

And the magic is that every time a human brain learns a new piece of pure mathematics, it monitors its own (human) experience for any relevance/connections and the chances increase for the discovery of a new application.

So I'm not sure it has ever happened that a piece of pure mathematics was invented for no reason and was absolutely useless until an application was discovered later. And conversely, I'd be willing to bet that almost every aspect of applied mathematics has been the inspiration for pure theoretical work of some sort (whether it led to any significant advances or not).

I guess what I'm trying to say is that in mathematics (as in all of science) the dialogue between theory and practice goes in both directions and never stops.

wolfen
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I'd say that basically all technological achievements are founded in pure mathematics. The relationship is often long and distant, but I'd say without pure mathematics they wouldn't be possible. In fact, I think it'd be rather hard to find a technological achievement that wouldn't be based on results of pure mathematics.

To give a few examples:

  • Computer science. Computers are based on Turing's and Church's research about what mathematical functions are computable in some sense. At that time, it was pure mathematics, yet now it's the basis of what we use every day. CS uses many concepts from pure mathematics, starting from binary numbers, number theory etc.
  • Physics. Physics evolved hand in hand with mathematics. Things that used to be purely mathematical were subsequently used in physics,. Without this pure math, we wouldn't have many achievements in physics, simply because physicists wouldn't have the required theoretical tools to work with. And this means, we wouldn't have engineering achievements that use them. To give some examples:
    • Without calculus and infinitesimals, we wouldn't have statics, which is indispensable for most today's complex architecture.
    • Lie groups, a purely theoretical idea, become very useful in particle physics, which is the basis of many nowadays technological advancements.
  • Probability and statistics are used everywhere. All empiric research is (or should be) validated using statistical methods.
  • And something less serious - without topology, we wouldn't have so many ways of putting on a necktie.
Petr
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Topology helps understanding the molecular structures. See this book, When Topology Meets Chemistry: A Topological Look at Molecular Chirality written by Erica Flapan. I skimmed a few chapters of the book and it was very interesting.


Related: Real life applications of Topology

Karatuğ Ozan Bircan
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Coding theory is mainly based on algebra; see for example the Goppa code which uses algebraic geometry tools.

Seirios
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Fractals were invented specifically to explore areas of geometry which were thought to only exists in the world of imagination of pure mathematics. They failed miserably, because it turned out that the world is chock full of fractals. Nowadays, fractals are used heavily in computer graphics and describing the patterns of nautilus shell, pine cones, coastlines, lightnings, among many other natural phenomenons.

According to Pickover, the mathematics behind fractals began to take shape in the 17th century when the mathematician and philosopher Gottfried Leibniz pondered recursive self-similarity (although he made the mistake of thinking that only the straight line was self-similar in this sense). In his writings, Leibniz used the term "fractional exponents", but lamented that "Geometry" did not yet know of them. Indeed, according to various historical accounts, after that point few mathematicians tackled the issues and the work of those who did remained obscured largely because of resistance to such unfamiliar emerging concepts, which were sometimes referred to as mathematical "monsters". (Wikipedia)

Lie Ryan
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Error correction codes!

When CDs were first being discussed, the engineers from Phillips were in discussion in Japan with the company Sony on standards, and those from Sony said they were not happy with the error correction standards set by Phillips. So their engineers went back to Eindhoven and called people together to ask who was the best expert in Europe on this new science of error correction. They were told it was a Professor of Number Theory, J. van Lint, in Eindhoven! I did check this story with him.

I have been told that the high quality of the pictures from the Voyager space probes would not be possible without error correction, because of the weak signals, and the noisy space.

Error correction is quite widespread, from hard disks, to simple ones in the ISBN, and the advanced ones, see for example the wikipedia article, use sophisticated pure mathematics.

The first such code, the Hamming code, was invented by a researcher at Bell Labs, when he ran programs over the weekend, and came back to find "your program has an error". He swore to himself, and thought: "If it can find an error, why can't it correct it?"

Ronnie Brown
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Counting processes and martingales are objects I view as purely mathematical/probabilistic objects. Nevertheless, they fundamental objects when describing the theory of survival analysis - survival analysis being a branch that is used in many registry-based studies in e.g. epidemiology.

A simple model (a model without censoring) of survival analysis is the following: Let $X_1,\ldots,X_r$ be iid random variables with values in $(0,\infty)$, where $X_i$ is the lifetime of the $i$th individual. Let $X_i$ have density $f$ and distribution function $F$ with $F(t)<1$ for all $t\in (0,\infty)$. Put $$ N_t^i=1_{\{X_i\leq t\}},\quad i=1,\ldots,r, $$ and $$ N_t=\sum_{i=1}^r N_t^i, $$ i.e. $N_t$ is the number of individuals dead before $t$. Then $(N_t^1,\ldots,N_t^r)_{t\geq 0}$ is an $r$-dimensional counting process and $(N_t)_{t\geq 0}$ is a counting process. Now, theory of local martingales and predictable covariation can be used to derive estimators such as the Nelson-Aalen estimator of the cumulative hazard rate, i.e. the function $$ \Lambda(t)=-\log S(t), $$ where $S(t)=1-F(t)$ is the survival function.

Stefan Hansen
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Group theory in physics Standard Model.

enedene
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Wavelet and Fourier transforms are used in a very long list of medical equipment (MRA, blood pressure monitor, diabetis monitor, just to mention a few), in audio-video compression (mp3, jpeg, jpeg2000,h.264 et al) and audio-video effects (audio equalization, image enhancing, etc). Linear-Algebra is the basis of the Google Page-rank algorithm, and some face-recognition algorithms. This is not by any means an extensive list of applications, just a few that I remember.

yms
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Coming from software development background I can say that functional programming languages were influenced to some degree from Lambda Calculus, a formal system. Lambda Calculus was introduced by mathematician Alonzo Church in the 1930s

DMINATOR
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How about in calculating orbital patterns (i.e. before the first satelite was ever launched). Without the work of pure mathematics laying the ground work for astro-physics, Apollo 13 would have been lost.

William
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Matrix

Not the the movie, the "array of numbers"... It was invented before computers and it's now used for all heavy 3D stuffs (real-time or not) and more.

It started as pure theory and it's now used in most of your favourite movies and all 3D video games...

Pure research is important ;)

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    I'm fairly certain matrices didn't *at all* start as "pure theory". They were developed to study systems of linear equations, which surely show up by the thousand in physics and applications. – Jack M Mar 04 '14 at 21:15
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The classical example of this for me it is just binary numbers and its properties (boolean algebra)

user58743
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    It would be nice to explicitly say when this became "useful". – robjohn Jan 18 '13 at 10:37
  • welcome to SE. Please note that your answer is quite vague. To improve your answers you might want to roam around the site some more and appreciate the style of answers. – Ittay Weiss Jan 18 '13 at 10:49
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Here are examples of Applied mathematics :

enter image description here

Martin Sleziak
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Elias Costa
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Matrix operations were used by Pauli to model electron spin.

Pauli spin matrix

  • There is a classic toast allegedly used by Pure Mathematicians: "To Pure Mathematics - may it never be any use for anything!" The boundary between Pure and Applied will of necessity be a moveable thing. – Marc Wilson Jan 30 '13 at 19:13
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This may be a little late but I think the most basic and pure proof and yet the most astonishing was Euclid's

Wikipedia Entry on Euclid

There are uses in Digital Media, Cryptography, Physics and Engineering.

A lot of Pure math is knowing how to apply it, most theories have a specific problem set they are known to solve because they are designed that way or were found to solve that problem set that way but when you apply theories in ways which are not typical of the solution you incite innovation and expand your horizons.

Jay
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    In the early 1980s Keith Dakin, who did an imaginative doctoral thesis in pure maths at Bangor, wrote to us from working in the defence industry to say among other things: "We can get as many computer scientists as we want, but a mathematician who can say which areas of mathematics are relevant to the problem at hand is worth their weight in gold." – Ronnie Brown Nov 12 '17 at 16:56
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Matroid theory, invented by Hassler Whitney, have found many applications:

http://en.wikipedia.org/wiki/Matroid

http://www.amazon.com/Matroid-Applications-Encyclopedia-Mathematics-its/dp/0521119677

kjetil b halvorsen
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Formulas for calculating the value of $\pi$ to millions, billions and Trillions of digits seemed to have no practical utility. Testing new Computer technology, which were millions or billions of times faster than old technologies and had millions of times more memory than old technologies, required the use of these formulas.

Martin Sleziak
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b.sahu
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