What is theoretical computer science?

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]

> 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.

> If I say all swans are white as my hypothesis

That fails as a scientific hypothesis on purely structural grounds, before you have made any observations. Scientific hypotheses have to explain something. It's not enough to say that all swans are white, you have to say why. "All swans are white" is an observation, not a (scientific) hypothesis.

An example of a legitimate scientific hypothesis is that all swans are white because being white provides swans with some benefit in terms of reproductive fitness (and then you have to go on to say what that benefit is). You can then go on to predict that there might be non-white swans, but that these are expected to be rare because evolutionary pressure would drive non-whiteness out of the gene pool. Or something like that. But "all swans are white" by itself is a non-starter as a scientific hypothesis.

I'm talking at a very technical level ignoring all the cultural stuff around the scientific method like "peer review" or explaining "why"

Basically a hypothesis is a statement that can be true or false. That's it.

The reason I refer to science in this very technical way is because the we are tackling the problem of classification. We are asking the question what is computer science? So to answer the question we need to use very technical definitions where the boundaries of categorization are extremely clear.

Again, at a very technical level a hypothesis is simply a statement that is true or false.

> a hypothesis is simply a statement that is true or false.

No, that is a proposition, not a hypothesis.

And the requirement that hypotheses be explanatory has nothing to do with culture, it is the distinguishing feature of the scientific method. See: https://blog.rongarret.info/2024/03/a-clean-sheet-introducti...

False. At the very technical level, a hypothesis is defined by statistics.

https://www.sciencedirect.com/topics/mathematics/statistical....

When you cut through all the cultural and human stuff we place around the scientific method, in the end it is a statistics problem at the most technical level. Everything else makes it fuzzy and hard to define.

You started by saying that in science you cannot prove things, only provide evidence, but that in math you can prove things.

Then you made an assertion that a hypothesis "is a statement that can be true or false. That's it."

Now, you're asserting that a hypothesis "is defined by statistics". Never mind that humans did a bunch of science before statistics were developed, this is different than your prior statement ("true or false. That's it."), and you seem extremely confident in it.

You're acting as if you have certainty in a statement, but you did not arrive at it by a proof. You've claimed that only the falsifiability of a statement matter, not its explanatory structure, but your own statements about hypotheses are structural and definitional ("defined by statistics", "at the most technical level", "ignoring all the cultural stuff"). You've asserted that a hypothesis is falsifiable, and intrinsically statistical, but you bring no quantitative data in support of this.

I don't know what activity you're engaged in, but it doesn't seem like a rigorous and principled search for truth. And you seem to be more willing to preach about the importance of falsifiability than to apply that concept critically.

>Then you made an assertion that a hypothesis "is a statement that can be true or false. That's it."

Yeah so? You can make a statement and the actuality of that statement is either true or false. But how you prove that statement to be true is NOT possible. That is my claim. I am also saying it is POSSIBLE to falsify the statement aka disprove it... All statements have this property.

>Now, you're asserting that a hypothesis "is defined by statistics". Never mind that humans did a bunch of science before statistics were developed, this is different than your prior statement ("true or false. That's it."), and you seem extremely confident in it.

Well you're using the "what came first argument" to say that the original scientific definition came before the statistical definition so it's more valid. I disagree.

Language emerges from concepts in attempt to explain things we only have vague understanding of. Concepts like Life and intelligence are ill defined and used for communication on topics we don't completely understand. It's useful to communicate this way but it's useful only because either we don't understand something completely or because we use it as a shortcut. Usually a new word starts off in this fuzzy state and as we understand things better the word takes on a more rigorous definition. Science started out without us understanding statistics, so that's why you have a lot of older definitions attached to it. The technical definition of hypothesis is part of mathematics. That is ultimately the most correct definition but it's not the definition with the most utility. If push comes to shove and we want to categorize a technical concept like computer science, then the technical definition is what matters more.

>You're acting as if you have certainty in a statement, but you did not arrive at it by a proof.

What. I never said this. You are entirely misunderstanding. I said statements can either be true or false. The proof of whether it's actually true or false is a separate issue. My original claim is that proving something true is impossible.

>You've claimed that only the falsifiability of a statement matter, not its explanatory structure, but your own statements about hypotheses are structural and definitional ("defined by statistics", "at the most technical level", "ignoring all the cultural stuff").

I didn't say only the falsifiability of a statement matters. I never said this. I said falsifying a statement is the only thing we logically have the ability to do. It still matters to do correlations and other types of things related to science but at a technical level we aren't proving anything. At a technical level things can only be falsified. This is not to say OTHER things don't matter. They do matter.

>You've asserted that a hypothesis is falsifiable, and intrinsically statistical, but you bring no quantitative data in support of this.

Did I not post a link to a resource stating this? This is Data supporting my point. If you want quantitative data for the English definition a word, I'm sorry but that's just not physically possible. English definitions cannot be quantified into numbers for any meaningful numerical analysis.

I know your question is just sort of rhetorical. Basically you think I'm being too pedantic and you're trying to illustrate a contradiction in my own logic. I don't deny it. In the end I'm using my own personal opinion here. But I share my opinion here because I believe if that the MAJORITY of people completely UNDERSTAND what I'm saying they will AGREE with me. That's all. But again opinion. And either way nothing can really be proven can it? Especially for english definitions.

>I don't know what activity you're engaged in, but it doesn't seem like a rigorous and principled search for truth. And you seem to be more willing to preach about the importance of falsifiability than to apply that concept critically.

What are you trying to say here? This is false. And it approaches the point of accusatory and a lie. I don't preach the importance of falsifiability. I am just saying that is the only possible technical thing we can do in terms of determining if something is true or false. I get technical because we ARE CATEGORIZING technical concepts and definitions so it's appropriate to DO THIS. If we are casually conversing or trying to understand concepts then of course we can revert to the more laid back way of communicating and fuzzy way of defining things.

But what's actually going on here is that we are determining whether or not a technical concept: "Theoretical computer science" is math or not? Such detailed and rigorous categorization REQUIRES the use of detailed and rigorous definitions.

> You can make a statement and the actuality of that statement is either true or false.

It can also be "not even wrong."