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The Philosophy of Computer Science

plato.stanford.edu

71–80 of 102 posts

Re: The Philosophy of Computer Science

#71
post #62
post #47

Earlier quoted context omitted.

Conway's Law comes to mind. https://en.wikipedia.org/wiki/Conway%27s_law

Conway's Law is frequently referenced, but the claimed connection is rarely explained. From Wikipedia: > The law is, in a strict sense, only about correspondence; it does not state that communication structure is the cause of system structure, merely describes the connection. Different commentators have taken various positions on the direction of causality; that technical design causes the organization to restructure…

Yeah I think the main idea is that people who like clean organization, boundaries, tidiness, clear responsibilities, etc, will tend to congregate together. And their organizations will be that way, and so will the things they create together.

People who like experimentation, chaos, prototyping, flat hierarchies, etc, will also tend to congregate together, and the systems that they build will also have those values.

Same for lots of different qualities. It isn’t A->B or B->A, it’s more like ABC.

Re: The Philosophy of Computer Science

#73
post #19

> and from the practice of software development and its commercial and industrial deployment. More specifically, the philosophy of computer science considers the ontology and epistemology of computational systems, focusing on problems associated with their specification, programming, implementation, verification and testing. Well, Stanford doesn't have a clue what Computer Science is and isn't and is apparently tryin…

Well you could say that physics is also math. But conversely, would geometry for example be considered computer science? If not, then computer science is not identical with mathematics. Something that occurs to me is often when creating programs to run on a computer system we don't know exactly how they will perform in advance, because of the complexity of the hardware and software interrelations within the system. A…

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Re: The Philosophy of Computer Science

#74
post #4

That reads like the successor to Prof. John McCarthy's "Epistemological Problems in Artificial Intelligence" class, which I once took, back in the days of logical inference and expert systems. AI people thought back then that if you thought about thought enough, you could figure out how to mechanize it. That turned out to be a dead end, and the "AI winter" (roughly 1985-2005) followed. That class was known informally…

John, any other thoughts to share on changes in the department? I’m curious to hear your perspective.

Re: The Philosophy of Computer Science

#75
post #74
post #4

That reads like the successor to Prof. John McCarthy's "Epistemological Problems in Artificial Intelligence" class, which I once took, back in the days of logical inference and expert systems. AI people thought back then that if you thought about thought enough, you could figure out how to mechanize it. That turned out to be a dead end, and the "AI winter" (roughly 1985-2005) followed. That class was known informally…

John, any other thoughts to share on changes in the department? I’m curious to hear your perspective.

I'm too out of date. I haven't even been on the Stanford campus since the pandemic started.

Re: The Philosophy of Computer Science

#76
post #14

Earlier quoted context omitted.

First of all "this, then that" does not imply causality. The way that I heard it, it was the fact that Lisp environments on Sun workstations were able to outperform Lisp machines at a much better price point. And just like that, a significant AI specific industry collapsed, and its other promises came into question. That said, all three versions are consistent. The fact that researchers thought that they were closer…

> Lisp environments on Sun workstations were able to outperform Lisp machines I think it was just that it became clear the projects didn't deliver anything very useful. You can't keep the hype up very long if it can't be backed up by real applications. But some good stuff that got started then prevailed, like speech understanding and language translation. But it didn't come usable overnight. Classic AI was a reasonab…

It took a while to get there. One would think of end 80s / early 90s. Remember, there were probably only around 10000 (ten thousand, not ten thousands) Lisp Machines ever produced. A 40 bit Ivory 3 processor from Symbolics was basically slightly faster than a Motorola 68030 processor, but with larger memory capabilites. Memory was expensive on stock hardware, too - but not as expensive as the 48bit wide ECC memory on a Lisp Machine. Add to that a Megapixel screen, a large disk, a tape drive, a faster graphics card,...

There was little point investing money into a hardware market which did not produce cheaper and/or faster machines, given the small market.

There were a lot of interesting applications development on Lisp Machines, but there was no point to deliver them on that expensive hard- and software. Development environments were catching up. Common Lisp was actually designed to be able to deliver applications on many different platforms, even though its main influence was Lisp Machine Lisp.

So a $50k ART expert system development system was replaced by a low-cost CLIPS on machines with less hardware/software costs. It also was moved away from Lisp, as Lisp was extremely unpopular (and with almost no funding left) in the 90s.

Nowadays a native Lisp on a M2 processor from Apple is 1000 times faster than on the Lisp Machine from 1990. That's just a single CPU core, we are not even talking about GPU or Neural network functionality. Expensive 40 MB main memory from then is now 8 GB entry level.

Re: The Philosophy of Computer Science

#77
post #61

Earlier quoted context omitted.

Google Translate got a lot worse after the AI version was introduced, maybe not for english-centric translations but all other. The previous deductive translator was be much better. Same with Siri and Google Assistant, they are really bad at other languages except English

Emphatic disagreement, at least when it comes to Indo-European languages. The previous translator was effectively unusable. Then suddenly Google Translate became something that would work most of the time. At the least for average users who were dealing with English, Spanish, French, German, Russian, etc. Despite the documented failure modes (and they were many), suddenly it was possible to read articles in other lan…

here is Chinese user, I read this page by Google translate, at least English to Chinese is good for daily use.

Re: The Philosophy of Computer Science

#78
post #60

While "computer science has as much to do with computers as astronomy does with telescopes", I fear that it's become too inexorably bound to computers and too dependent on its foundation in the maths. As a result CS is seen as sort of a fancier software developer/engineering field and not a field that's about theories of computation. I think CS has the potential to reframe a huge number of fields and offer a differen…

> I think CS has the potential to reframe a huge number of fields and offer a different or unique take on how those fields might approach problems. e.g. a chemical reaction could be reframed as a computation that takes inputs and produces outputs.

Very well put. I had this sort of dream going into my studies. Unfortunately the prevailing ideology (at my school) seems to be that CS is applied mathematical induction and discrete logic.

Though I do think the dark horse of the 21st century is going to be computational biology, and that will really kick things off in the direction you describe. Imagine git fetch & make 'ing an orange tree, where the source is some amniotic goo

Re: The Philosophy of Computer Science

#79

Earlier quoted context omitted.

> CS has zero to do with computers This is extreme. Computer architecture is undoubtably a field of study within CS. Many parts of computer science are mathematical, but many parts are closer to physics or chemistry than mathematics. You can run experiments and form hypotheses in computer science.

Computer architecture used to be and imho was properly a part of electrical engineering. “Computer science” was never really scientific. Algorithms, symbolic logic, natural languages, computational complexity, finite model theory and compilers, after that it’s what, grinding gears? Can you name one significant experimental result in computer science? Nothing comes to my mind.

Lambda calculus was an experiment, I'd say. Wolfram's rule 30 too, though granted it's not that significant.

You have to do a bit of science whenever something unexpected comes around and you have to fit it into what you know.

Re: The Philosophy of Computer Science

#80
post #4

That reads like the successor to Prof. John McCarthy's "Epistemological Problems in Artificial Intelligence" class, which I once took, back in the days of logical inference and expert systems. AI people thought back then that if you thought about thought enough, you could figure out how to mechanize it. That turned out to be a dead end, and the "AI winter" (roughly 1985-2005) followed. That class was known informally…

> AI people thought back then that if you thought about thought enough, you could figure out how to mechanize it. That turned out to be a dead end, and the "AI winter" (roughly 1985-2005) followed.

To be fair, maybe we just haven't thought about it enough.

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