He also did early work in cryptanalysis, AI (minimax chess algorithm and a learning robotic mouse). His student was Ivan Sutherland. More or less, whether its networking, signal processing, compression, crypto, machine learning, circuit design, basically anything to do with the digital age, you'll find Shannon did important foundational work there.
Pretty much anyone doing signal processing, which is part of the standard physics curriculum where I'm from, will be introduced to the Nyquist–Shannon theorem while Church and Turing did fundational work in logic and computability but with far less practical application.
Your comment proves my point. Shannon made greater contribution than Turing. But everyone knows Turing. There are movies made about him.
When I said "people", I thought it was obvious I meant the general public.
This is nowhere close to true.
I am willing to grant you that a random person off the street is more likely to have heard of Turing than Shannon. But much more likely still to have heard of Stephen Hawking or Jane Goodall or James Watson.
Public awareness of scientists drops off very quickly. That’s just the way life works. It’s lucky enough if random people off the street have heard of small countries or political leaders of their own country. Most people don’t remember most of the major genocides of the past few decades.
Shannon is without question one of the best known (by the general public) 30 or 50 scientists of the 20th century. It is ridiculous to pretend that nobody has heard of him. At the very least anyone with a STEM degree will have some idea.
But not everyone can capture the public imagination the way (say) Einstein did.
> At the very least anyone with a STEM degree will have some idea.
You've berated the parent for saying - obviously figuratively - "nobody outside of the computer science world has heard of him", then conceded that having a STEM degree would make a significant difference to the likelihood of whether someone knows of him.
It's a benign thing to trigger such an outburst, especially given there's so little substantive difference between your positions.
For what it's worth, I'm a self-taught software developer of 15+ years' experience and I've consumed plenty of material about science and scientific history - but without any academic STEM study.
I hadn't heard of him - at least to the extent that I remember.
Whereas of course I know plenty about Turing.
And for what it's worth I know much more of Turing's life than Hawking's, Goodall's or Watson's.
The question of how many non-CS/STEM-qualified people have heard about him is an interesting one to explore, and it can do without the poisonous tone you've introduced.
Shannon, as one of the best known and most celebrated scholars (in any field) of the 20th century, and by any reasonable standard a scientific superstar, is not one of them.
It’s like saying “Le Corbusier doesn’t get the credit he deserves. Nobody outside architecture has heard of him. Everyone knows Frank Lloyd Wright, but what about Le Corbusier?!” Or “Carl Jung doesn’t get the credit he deserves. Nobody outside of psychiatry has heard of him. Everyone knows Sigmund Freud, but what about Jung?!”
Pick whatever field you want, and I’m sure you can find a list of seminal figures who are well known to anyone with basic knowledge of the field (say, anyone who took an intro course in college) and familiar to anyone with broad cultural education, but not as recognizable to the man on the street as top athletes or rock stars. Claiming that these people are unrecognized or unheard of is absurd.
I have approximately the same (quite high) level of lay-person interest in psychology as I do in science, and I know more about Jung than I do about Freud.
On Jung's Wikipedia page, the "In Popular Culture" section contains 19 items. Freud's page doesn't have such a section - though of course he is still very well known in the mainstream, but not materially more so than Jung.
Turing's "Portrayal" section on his Wikipedia page contains 14 items across theatre, literature, music and film. No comparable section exists for Shannon, and you couldn't create one that would come close to Turing's.
This is all that your parent was trying to say. Not that Shannon is unrecognised within his field or "less recognizable to the man on the street as top athletes or rock stars", but less recognised in mainstream culture than fellow computer scientist Alan Turing.
Returning to your original comment:
> Yeah! Nobody has heard of that guy. His Mathematical Theory of Communication only has 112 thousand (!!!) google scholar citations, apparently the #4 most cited paper of all time in any field (#1–3, 5–9 are biochem/chem papers, and #10 is clinical psych).
> For comparison, Turing has 5 papers with 10–12k citations each.
The _entire point_ your parent was trying to make was that Shannon is vastly more credentialed and recognized within his field, yet little known in the mainstream.
Why get so worked up over a point on which there's basically no substantive disagreement?
Who knows though, given the public's enthusiasm for modern tech and its stars (Jobs, Musk, Gates, Bezos, etc.) maybe one day scientists' work might make a "Casey's Top 40"-like popular countdown if presented the right way!
Till then there's always Epic Rap Battles of History, here's Einstein vs. Hawking (120 million views):
The reason Claude Shannon is a legend has a lot to do with the fact that his ideas are not just correct and draw from his multidisciplinary knowledge, but also expertly communicated. It is an incredibly readable paper and only absolutely requires a little bit of mathematics when he is describing how to convert a state machine for Morse code to a matrix which would allow someone to calculate how many bits per time unit can actually be transmitted in Morse code -- and this is not absolutely essential, it is calculated in a different way and also it is communicated that you can easily take his word for it that it is whatever numerical value it was. A lot of the arguments about fitting a stream of symbols into an encoding for noisy channels have these lovely diagrams that help to elucidate exactly what he's talking about.
If you want to make that same sort of impact, it is not just important to be a great mind, but to spend a bunch of time practicing how you communicate that information.
On the other hand, with Information Theory, Shannon was considered to have been ahead by decades.
The closest related work that is considered to come from an independent line of inquiry (taking place in the USSR), but similarly fundamental is Kolmogorov complexity (algorithmic entropy) by Andrey Kolmogorov  in 1963.
While Shannon made the huge leap on his own, it should be noted that Harry Nyquist  (his colleague at Bell Labs) laid essential foundations through his Nyquist Stability Criterion and studies on bandwidth. This came after harmonic analysis (Fourier transform, etc.) appeared in the 1800s.
Sadly, many extremely smart and profound scientists are quite incapable of (or not interested in) conveying clearly their thoughts to general audiences.
The path integral formalism or the diagrams, albeit being mathematically obscure at first where quite clear from the intuitive viewpoint once he explained them.
The most insidious cause of this is that getting a permanent position in a department still often depends on publishing in the top journals within that department's subject, which means explaining it only to the point that it is coherent to that subject's experts. Any further elucidation is sometimes seen as simplification or over-analysis, and to the detriment of getting an article accepted in subject-leading venues.
Or, to put it another way, putting more bits down the channel than are needed for comprehension by the editor is seen as unnecessary redundancy.
(emphasis in the original)
I find that second sentence humorous every time I read it... I wonder if he had seen the current state of internet discussion if he would have changed "frequently" to "occasionally".
Which is accurate. A lot of Internet communication is a tracker phoning home, TLS handshakes and other communication with content but no "meaning" per se. In Shannon's day, such industrial communications--over telegraph--were common enough to warrant mentioning but not prevalent enough to make up the bulk of human communication traffic.
The bits are the same, the meaning is different.
If anyone is interested in learning more about Bell Labs and the folks who worked there, “The Idea Factory” by Jon Gertner is a fantastic book written on the subject. It’s not comprehensive but it’s a very inspiring read.
There has always been something very vexing about Bell Labs’ legacy though. They had everything they needed to start the personal computing revolution: engineers, scientists, equipment, a nationwide telephone network for god’s sake. What happened?
A formal study of some of the consequences:
Unfortunately, a lot of this research was buried in microfiche in university basements as Bell Labs' legacy was traded from company to company without a viable distribution mechanism. As a result, although this work is now all available on-line (sadly not in open-access form), there are still more forgotten gems in there than many researchers realize.
Even though that brought the lab's demise, its then-younger staff and their mentees continued their work at other organizations, including many at Google.
Another book with a lengthy section on Claude Shannon is James Gleick's The Information: A History, a Theory, a Flood [1.]. A shorter but still nice explanation in on Brain Pickings [2.].
... about the guy who gets credit for figuring out BOTH how to make computers do math, AND how information can be encoded, transmitted and manipulated.
Also: as much as people talk about information theory in various contexts, I doubt that many take the trouble to understand it better. Like thermodynamics, people want to take away an overly broad, folksy interpretation and apply it everywhere without stopping to think if it really applies in the way they claim.
>I don't see anyone claiming it applies in places it doesn't.
It applies in a lot of places, but probably in a more narrow way than people think. You can say "information theory applies here" and probably be right, but to be specific about what that means requires some work. Shannon says that himself in that piece.
Thermodynamics is an easier one to see, because people always make claims about something "because of the 2nd law of thermodynamics", something about everything always becoming more disordered. But that's too simplistic a view, and fails to consider what is a closed system and so on.
If I remember right after 20 years.
I think he was uncomfortable with the fact that the continuous signal story was not as well fleshed out. He came to revisit that after about a decade in "coding theorems for a discrete source with a fidelity criterion". This did for lossy compression what his 1948 paper did for lossless.
It rarely happens that a paper that creates a field also resolves its most important questions. That was Shannon's standard, I guess. That he revisited the question perhaps speaks to his inner discomfort that the story was not complete.
I think this "optimum discernment" at base 2 you remember is nonsense? could you provide a pointer?
That was nearly 20 years ago.
Everyday I appreciate that statement, and Claude Shannon, a little bit more.
When I saw that masters thesis. I literally hyperventilated.
> Shannon information theory provides various measures of so-called "syntactic information", which reflect the amount of statistical correlation between systems. In contrast, the concept of "semantic information" refers to those correlations which carry significance or "meaning" for a given system. Semantic information plays an important role in many fields, including biology, cognitive science, and philosophy, and there has been a long-standing interest in formulating a broadly applicable and formal theory of semantic information. In this paper we introduce such a theory. We define semantic information as the syntactic information that a physical system has about its environment which is causally necessary for the system to maintain its own existence. "Causal necessity" is defined in terms of counter-factual interventions which scramble correlations between the system and its environment, while "maintaining existence" is defined in terms of the system's ability to keep itself in a low entropy state.
Roughly speaking: The amount of computation or energy needed to perfectly reproduce a random source, such as a coin flip, is high, while the significance or meaning, for the average receiver, is low. Natural language text requires less computation to reproduce , but, for the average receiver, the significance is higher.
Also, what about crystalline forms, which are very orderly and require minimal computation to reproduce, but are equally insignificant for the average receiver?
More or less correct. The key difference is that you could not compress a random coin flip sequence (and that a compressed text is meaningless until decompressed to original).
> all minimal programs are by definition Kolmogorov random
Compression provides an upper bound to K. Kolmogorov Randomness itself is not computable. AKA: You can't ever know if you have a minimal program.
> Crystalline forms
It is possible to both have low significance and low information content. Crystalline forms were very significant to Turing though: https://en.wikipedia.org/wiki/The_Chemical_Basis_of_Morphoge...
With this in mind, you can see text as the output of an algorithm (brain) taking many such decisions. The information entropy contained in this text reveals the complexity (amount of distinct binary decisions) which needed to take place in the machine (brain) in order for the text to occur.
At my uni they only had communications theory, which covered stuff like software defined radios. Information theory was a significant part of it though.