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.

> 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.

Maybe you think you saw a black swan around the orbit of Jupiter with your new fancy telescope, but later you figure out that the telescope was falty and the swan you saw was actually white.

Because science depends on the external world, and there might always be hidden variables that we are not considering, it can only give us extremely high confidence after we make repeated observations using different methods, but it cannot give us the absolute confidence that Math can give us.

Also, this example of faulty telescopes is actually really close to what actually happened in the history of astronomy. Stars were thought to have a visible radius that made them appear to be much larger than the Sun, which was considered evidence that the Sun was not a regular star.

> it can only give us extremely high confidence

Here's an interesting thing. Even high confidence can't be verified.

Let's say you examined 10 million swans and you think you observed all possible swans. But there's no way you can know whether or not the actual population is 10 billion trillion swans or a google swans.

If you observed 10 million swans and they are all white but those swans could only represent 1/99999999999999 of all possible swans. Then that means your observation is low confidence and there's no way whether we can verify what fraction of the population our sample size represents.

So actually high confidence is just an assumption. At a very technical level the confidence that science brings to the table is very very weak. We are making tons of assumptions and jumping to conclusions all the time.

> So actually high confidence is just an assumption.

This one is actually a funny statement. Not all assumptions are born equal :)