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This is a great article and I especially liked the notion: >Theoretical physics is highly mathematical, but it aims to explain and predict the real world. Theo

This is a great article and I especially liked the notion: >Theoretical physics is highly mathematical, but it aims to explain and predict the real world. Theories that fail at this “explain/predict” task would ultimately be discarded. Analogously, I’d argue that the role of TCS is to explain/predict real-life computing. as well as the emphasis on the difference between TCS in Europe and the US. I remember from the University of Crete that the professors all spent serious time in the labs coding and testing. Topics like Human-Computer Interaction, Operating Systems Research and lots of Hardware (VLSI etc) were core parts of the theoretical Computer Science research areas. This is why no UoC graduate could graduate without knowledge both in Algorithms and PL theory, for instance, AND circuit design (my experience is from 2002-2007). I strongly believe that this breadth of concepts is essential to Computer Science, and the narrower emphasis of many US departments (not all) harms both the intellectual foundations and practical employment prospects of the graduate. [I will not debate this point online; I'll be happy to engage in hours long discussion in person]

wirrbel 2y ago
There is (mechanical/optical/*) engineering, experimental and theoretical physics, and then there is maths (focussing on physical problems). I think taking these four abstraction levels could be a model for computer science. Now, theoretical physics is a bit of a troubled child however in recent years. If we map computer science aspects in not the four physics disciplines, we get: Software / hardware engineering Applied computer science Theoretical computer science Mathematics dealing with problems inspired by computer science
- whatshisface 2y ago
  *Theoretical high-energy beyond-standard-model accelerator physics.
ninetyninenine 2y ago
> Theoretical physics is highly mathematical, but it aims to explain and predict the real world. Theories that fail at this “explain/predict” task would ultimately be discarded. Analogously, I’d argue that the role of TCS is to explain/predict real-life computing. No this guy doesn’t get it. He doesn’t understand what science is. In science nothing can be proven. If I say all swans are white as my hypothesis this statement can never be proven because I can never actually verify that I observed all swans. There may be some swan hidden on earth or in the universe that I haven’t seen. Since the universe is infinite in size I can never confirm ever that I’ve observed all swans. However if I observe one black swan it means I falsified the entire hypothesis. Thus in science and in reality as we know it nothing can be proven… things can only be falsified. Math on the other hand is different. Math is all about a made up universe where axioms are known absolutely. It has nothing to do with observation or evidence in the same way science does. Math is an imaginary game we play and in this game it is possible to prove things. This proof is the domain of mathematics… not science. Physics is a science because it involves gathering evidence and attempting to falsify the hypothesis. Einstein said it best: “No amount of experimentation can ever prove me right; a single experiment can prove me wrong” Basically newtons laws of motion are a perfect example of falsification via experimentation with relativity later being confirmed as the more accurate theory that matches more with observation. So what’s the deal with computer science? First of all the term already hits the first nomenclature issue. Computer science is ironically not a science. It lives in the same axiomatic based world as mathematics and therefore things can be proven in computer science but not in science itself. So this nomenclature issue is what’s confusing everyone. The op failed to identify that computer science isn’t actually a freaking science. Physics is a science but computer science isn’t. So what is computer science? Sorry to say but it’s a math. I mean it’s all axioms and theorems. It’s technically math. CS is a math in the same way algebra and geometry is math. Physics is a science and it is not a math. It’s a totally orthogonal comparison. Your job as programmers is more like applied math. It’s completely orthogonal to the whole topic but People often get this mixed up. They start thinking that because programming is applied computer science then computer science itself is not a math. Applied math ironically isn’t really math in the same way writing isn’t a pencil. Yes you use a pencil to write but they are not the same. Same thing with computer science and programming.
- moring 2y ago
  > In science nothing can be proven. Asking here because it is mostly on-topic: This phrase is repeated often, but shouldn't it actually be, "In science, a hypothesis is either fundamentally verifiable or fundamentally falsifiable, but never both"? The two simply being the logical negation of each other. "All swans are white" is fundamentally falsifiable (by seeing a black swan) but not verifiable, as you described. "Black swans exist" is fundamentally verifiable (by seeing a black swan), but not falsifiable.
  - RandomThoughts3 2y ago
    I think both are wrong. Science has a word encompasses a broad variety of definitions and practices. The philosophical question of the relationship of empiricism to truth (because fundamentally the question has more to do with the relationship between observations and deductions than science in itself) is a complex one and overall of little interest to actual practitioners of science. It can’t easily be subsumed in broad aphorisms. I would hazard that most modern sciences - and physics especially - are nowadays mostly concerned about building models and the resulting predictions. The question then often becomes: does the tool I have built allow me to correctly predict how observations will unfold? The answer to which is often more shades of grey than simply yes or no.
  - epgui 2y ago
    Science (as in the “natural sciences”, ie.: everything that is not philosophy or mathematics) is not about “proving” anything at all. Science is about measuring, quantifying and qualifying uncertainty.
  - ninetyninenine 2y ago
    I answer it here: https://news.ycombinator.com/item?id=41851265 https://news.ycombinator.com/item?id=41851265 If you want to learn more look up Karl popper. It’s his philosophy. In response to the other sibling replies I’m basically talking about science as in the scientific method. What are the limits of the scientific method in terms of determining truth? One thing that isn’t addressed is fundamentally limited accuracy and precision of observational instruments. This actually makes things not falsifiable as well but I don’t talk about this because it complicates everything.
nonameiguess 2y ago
Not all that interested in debate, either, but it's hard to tell what you're really claiming here. It isn't the case with departments I'm aware of, certainly not where I went to school myself, that someone can graduate with a CS degree having taken nothing but theory courses. Tenured researchers can eventually specialize in that, but even PhD candidates have to demonstrate broad mastery of the entire core of CS across multiple sub-disciplines. Perhaps you're talking about the split between Electrical Engineering and Computer Science? That one isn't universal as some departments only offer EECS and not CS as a major, but when CS on its own is offered, "hardware" courses tend to be about microarchitecture, with practical work done using simulators. You're not required to know much of anything about electronics. But there is no program I'm aware of where a person can do nothing but math and get a CS degree. You have to write and test code.
msravi 2y ago
>Theoretical physics is highly mathematical, but it aims to explain and predict the real world. Theories that fail at this “explain/predict” task would ultimately be discarded. This isn't really true, is it? There are mathematical models that predict but do not explain the real world. The most glaring of them is the transmission of EM waves without a medium, and the particle/wave duality of matter. In the former case, there was a concerted attempt to prove existence of the medium (luminiferous aether) that failed and ended up being discarded - we accept now that no medium is required, but we don't know the physical process of how that works.
- westurner 2y ago
  We have lower error predictions but don't know why. Methods for explaining why include Counterfactual inference and/or now quantum Causal interference. All or some of quantum statistical mechanics, fluid dynamics, and quantum chaos intend to predict with lower error too
- westurner 2y ago
  On the Cave and the Light, List of popular misconceptions and science > Science, technology, and mathematics : https://en.wikipedia.org/wiki/List_of_common_misconceptions https://en.wikipedia.org/wiki/List_of_common_misconceptions : > See also: Scientific misconceptions, Superseded theories in science, and List of topics characterized as pseudoscience Allegory of the cave > See also,: https://en.wikipedia.org/wiki/Allegory_of_the_cave https://en.wikipedia.org/wiki/Allegory_of_the_cave The only true statement: All models are wrong: https://en.wikipedia.org/wiki/All_models_are_wrong https://en.wikipedia.org/wiki/All_models_are_wrong Map–territory relation: https://en.wikipedia.org/wiki/Map%E2%80%93territory_relation https://en.wikipedia.org/wiki/Map%E2%80%93territory_relation : > A frequent coda to "all models are wrong" is that "all models are wrong (but some are useful)," which emphasizes the proper framing of recognizing map–territory differences—that is, how and why they are important, what to do about them, and how to live with them properly. The point is not that all maps are useless; rather, the point is simply to maintain critical thinking about the discrepancies: whether or not they are either negligible or significant in each context, how to reduce them (thus iterating a map, or any other model, to become a better version of itself), and so on.

wirrbel 2y ago

There is (mechanical/optical/*) engineering, experimental and theoretical physics, and then there is maths (focussing on physical problems). I think taking these four abstraction levels could be a model for computer science. Now, theoretical physics is a bit of a troubled child however in recent years. If we map computer science aspects in not the four physics disciplines, we get: Software / hardware engineering Applied computer science Theoretical computer science Mathematics dealing with problems inspired by computer science

whatshisface 2y ago
*Theoretical high-energy beyond-standard-model accelerator physics.

ninetyninenine 2y ago

> Theoretical physics is highly mathematical, but it aims to explain and predict the real world. Theories that fail at this “explain/predict” task would ultimately be discarded. Analogously, I’d argue that the role of TCS is to explain/predict real-life computing. No this guy doesn’t get it. He doesn’t understand what science is. In science nothing can be proven. If I say all swans are white as my hypothesis this statement can never be proven because I can never actually verify that I observed all swans. There may be some swan hidden on earth or in the universe that I haven’t seen. Since the universe is infinite in size I can never confirm ever that I’ve observed all swans. However if I observe one black swan it means I falsified the entire hypothesis. Thus in science and in reality as we know it nothing can be proven… things can only be falsified. Math on the other hand is different. Math is all about a made up universe where axioms are known absolutely. It has nothing to do with observation or evidence in the same way science does. Math is an imaginary game we play and in this game it is possible to prove things. This proof is the domain of mathematics… not science. Physics is a science because it involves gathering evidence and attempting to falsify the hypothesis. Einstein said it best: “No amount of experimentation can ever prove me right; a single experiment can prove me wrong” Basically newtons laws of motion are a perfect example of falsification via experimentation with relativity later being confirmed as the more accurate theory that matches more with observation. So what’s the deal with computer science? First of all the term already hits the first nomenclature issue. Computer science is ironically not a science. It lives in the same axiomatic based world as mathematics and therefore things can be proven in computer science but not in science itself. So this nomenclature issue is what’s confusing everyone. The op failed to identify that computer science isn’t actually a freaking science. Physics is a science but computer science isn’t. So what is computer science? Sorry to say but it’s a math. I mean it’s all axioms and theorems. It’s technically math. CS is a math in the same way algebra and geometry is math. Physics is a science and it is not a math. It’s a totally orthogonal comparison. Your job as programmers is more like applied math. It’s completely orthogonal to the whole topic but People often get this mixed up. They start thinking that because programming is applied computer science then computer science itself is not a math. Applied math ironically isn’t really math in the same way writing isn’t a pencil. Yes you use a pencil to write but they are not the same. Same thing with computer science and programming.

moring 2y ago
> In science nothing can be proven. Asking here because it is mostly on-topic: This phrase is repeated often, but shouldn't it actually be, "In science, a hypothesis is either fundamentally verifiable or fundamentally falsifiable, but never both"? The two simply being the logical negation of each other. "All swans are white" is fundamentally falsifiable (by seeing a black swan) but not verifiable, as you described. "Black swans exist" is fundamentally verifiable (by seeing a black swan), but not falsifiable.
- RandomThoughts3 2y ago
  I think both are wrong. Science has a word encompasses a broad variety of definitions and practices. The philosophical question of the relationship of empiricism to truth (because fundamentally the question has more to do with the relationship between observations and deductions than science in itself) is a complex one and overall of little interest to actual practitioners of science. It can’t easily be subsumed in broad aphorisms. I would hazard that most modern sciences - and physics especially - are nowadays mostly concerned about building models and the resulting predictions. The question then often becomes: does the tool I have built allow me to correctly predict how observations will unfold? The answer to which is often more shades of grey than simply yes or no.
- epgui 2y ago
  Science (as in the “natural sciences”, ie.: everything that is not philosophy or mathematics) is not about “proving” anything at all. Science is about measuring, quantifying and qualifying uncertainty.
- ninetyninenine 2y ago
  I answer it here: https://news.ycombinator.com/item?id=41851265 https://news.ycombinator.com/item?id=41851265 If you want to learn more look up Karl popper. It’s his philosophy. In response to the other sibling replies I’m basically talking about science as in the scientific method. What are the limits of the scientific method in terms of determining truth? One thing that isn’t addressed is fundamentally limited accuracy and precision of observational instruments. This actually makes things not falsifiable as well but I don’t talk about this because it complicates everything.

nonameiguess 2y ago

Not all that interested in debate, either, but it's hard to tell what you're really claiming here. It isn't the case with departments I'm aware of, certainly not where I went to school myself, that someone can graduate with a CS degree having taken nothing but theory courses. Tenured researchers can eventually specialize in that, but even PhD candidates have to demonstrate broad mastery of the entire core of CS across multiple sub-disciplines. Perhaps you're talking about the split between Electrical Engineering and Computer Science? That one isn't universal as some departments only offer EECS and not CS as a major, but when CS on its own is offered, "hardware" courses tend to be about microarchitecture, with practical work done using simulators. You're not required to know much of anything about electronics. But there is no program I'm aware of where a person can do nothing but math and get a CS degree. You have to write and test code.

msravi 2y ago

>Theoretical physics is highly mathematical, but it aims to explain and predict the real world. Theories that fail at this “explain/predict” task would ultimately be discarded. This isn't really true, is it? There are mathematical models that predict but do not explain the real world. The most glaring of them is the transmission of EM waves without a medium, and the particle/wave duality of matter. In the former case, there was a concerted attempt to prove existence of the medium (luminiferous aether) that failed and ended up being discarded - we accept now that no medium is required, but we don't know the physical process of how that works.

westurner 2y ago
We have lower error predictions but don't know why. Methods for explaining why include Counterfactual inference and/or now quantum Causal interference. All or some of quantum statistical mechanics, fluid dynamics, and quantum chaos intend to predict with lower error too
westurner 2y ago
On the Cave and the Light, List of popular misconceptions and science > Science, technology, and mathematics : https://en.wikipedia.org/wiki/List_of_common_misconceptions https://en.wikipedia.org/wiki/List_of_common_misconceptions : > See also: Scientific misconceptions, Superseded theories in science, and List of topics characterized as pseudoscience Allegory of the cave > See also,: https://en.wikipedia.org/wiki/Allegory_of_the_cave https://en.wikipedia.org/wiki/Allegory_of_the_cave The only true statement: All models are wrong: https://en.wikipedia.org/wiki/All_models_are_wrong https://en.wikipedia.org/wiki/All_models_are_wrong Map–territory relation: https://en.wikipedia.org/wiki/Map%E2%80%93territory_relation https://en.wikipedia.org/wiki/Map%E2%80%93territory_relation : > A frequent coda to "all models are wrong" is that "all models are wrong (but some are useful)," which emphasizes the proper framing of recognizing map–territory differences—that is, how and why they are important, what to do about them, and how to live with them properly. The point is not that all maps are useless; rather, the point is simply to maintain critical thinking about the discrepancies: whether or not they are either negligible or significant in each context, how to reduce them (thus iterating a map, or any other model, to become a better version of itself), and so on.