Ask HN: What is not going to change in future? There are always few things in every field which never changed.
What are the top things according to you which not going to change or didn't change in the field of software development? |
Ask HN: What is not going to change in future? There are always few things in every field which never changed.
What are the top things according to you which not going to change or didn't change in the field of software development? |
QWERTY keyboards
Taxes
or, wind event = trees down.
and on and on.
- Unless somebody finds a polynomial algorithm for an NP-complete problem (which is a taller order than just proving P=NP), several interesting problems will continue to be infeasible to solve exactly in the general case with large data.
- If, in addition, quantum computers don't prove to be viable, commonly used cryptosystems such as RSA, AES, ECC, will probably continue to be secure provided they're used correctly.
- Results like the Two Generals Problem, the CAP theorem, etc. will still make distributed systems difficult to work with and require tradeoffs.
- Rice's theorem, that it is impossible to determine computational properties of arbitrary programs, will still apply, making static analysis (including antivirus programs, security scans, etc.) heuristic rather than exact.
- etc.
I think this is misleading. There are many exact static analyses---proof-checking in theorem provers like Coq is an exact static analysis. More generally, type checking can be an exact static analysis that guarantees semantic properties of your programs, like termination.
If you can force your programs to be in a certain form (e.g., statically rejecting type incorrect programs), you can sufficiently restrict the class of programs (Turing machines) that you're considering that you can indeed determine non-trivial computational properties of your programs.
I should probably have been more specific by writing "decide" instead of "determine", because you can absolutely 'determine' a computational property as long as you're willing to ignore false negatives. For example, it's easy enough to write a termination checker by just checking for loops and equivalent constructs (or e.g. in Idris, by requiring that all functions are total), but that will of course reject a large number of programs that do in fact terminate.
Coq is not a Turing Complete language, so Rice's theorem doesn't apply. But almost all people are not writing programs in Coq.
I think static types are great, but they don't contradict any of this.
A proof that P=NP immediately gives a polynomial-time algorithm for NP complete problems via universal search. It’s so wildly impractical as to probably not change anything, but it _is_ in P.
Can you explain more about what universal search is, and/or where I can read about how it would solve the problem?
- people give the orders
- people approve implementations (e.g., implementations handed over by an AI)
- people who approve implementations need to save face when the implementation turns out buggy
Even if AI reaches a level at which it can do all of the points above, it would dimishis its own value. Example: if I could launch an Spotify alternative with a few prompts using ChatGPT version 10, then so a million guys like can do it as well... meaning, no one will be doing it.
Of course, unless some sort of weird tech shift happens that makes the browser obsolete altogether, I suspect most HTML/CSS/JavaScript won't ever change anyway. Browsers are backwards compatible to a similar degree as Microsoft and Windows. If even stuff like the center tag are supported in 2024, most things aren't going anywhere.
On a less specific note, I guess poor planning and software development practices? Feels like planning how long things are going to take hasn't got much better in the last few decades, with things like 'agile' barely making a dent in it. I suspect projects overrunning, feature and scope creep, big budget disasters, etc will probably be issues in society til the end of time.
The Basecamp founders often talk about the advice they receive from Jeff Bezos, which was "Focus on the things that won't change in your business." [1] He was referring to things like "fast delivery" and "good customer service." But, it means a lot in a professional context, too - because it's things worth learning well.
[1] https://medium.com/@seansheikh/bezos-wisdom-focus-on-the-thi...
And of course, maths. I graduated in maths decades ago, and I always find it amusing when I see some tutorials on linear algebras making it to the top of HN, like if it was some fashionable new cool technologies. That being said, my math knowledge hasn't transferred in software engineering skills.
1. LaTeX. I can still compile my docments that I wrote 20 years ago...
2. The thirst for profit/quick releases over reliability (except for a few examples like LaTeX)
3. The existence of open source software as the one antitode to all software being horrible
4. The obsession with creating new things for the sake of creating, instead of for the good of anything
I had an initial document and development environment running within twenty minutes. That's impossible with LaTeX. In fact, for years, I had a tailor-made Docker image just for keeping LaTeX running, compiling and sane (I use more advanced features to make LaTeX bearable in $CURRENT_YEAR). That setup broke the other day.
I never investigated why, because an ecosystem where one has to go to such lengths in the first place, only to have it break, is not one I want to be a part of any longer. For typst, I can just grab the binary of the version I used and it will just work forever (or just compile it, which I have confidence will also be pretty stable for many years to come thanks to Rust).
Certainly not impossible. I don't find LaTeX hard to install, at least on a modern Linux distribution such as Ubuntu (and if memory serves, it wasn't hard on macOS either).
I agree that setting up a basic template from scratch can be tedious and I wish this was better, but the common approach for newbies is to copy a template from somewhere, and for more advanced users, they probably have some base template with personal tweaks that they keep reusing (I know I do, not only because I hate Computer Modern).
There are still a whole number of issues with LaTeX (such as incompatible packages, the inconsistency in font handling between pdflatex and xelatex, beamer is generally IMHO a mess, etc.) but what GP wrote - that old documents will continue to compile and give the same results - is true.
How companies work, how humans interact, how users behave -- I don't see how that'll change anytime soon at all.
I've seen pretty dramatic changes in both of those in the last 5 years. Human interaction seems to have become a lot shittier. Users' behaviour seems more entitled. How people behave and interact also differs quite a lot based on culture/geographic location.
Sure, at the end of the day we're all human with more or less the same wants and needs, but how we express them is neither uniform nor fixed.
https://en.wikipedia.org/wiki/Lindy_effect
(e.g. it's more likely that C, Fortran and Cobol are still around in 50 years than some of the more recent programming languages)
https://www.amazon.com/Same-Ever-Guide-Never-Changes/dp/0593...
A product manager wants our team to build something that nobody actually needs? I may wind up getting laid off for that.
Imagine ChatGPT could "translate" from grumpy-old-curmugeonish into friendly-human in realtime, then which soft skills would still be valuable? Imagine going to a shop and being served by a grumpy curmudgeon whose poor English was translated instantly into great customer service.
What changes with this? To what extent will this ever be possible?
unless you were being sarcastic.
Let's say I believe it is my destiny to make sure all humanity evolves to become enlightened and reach the next rung?
Let's also say a constraint is I disbelieve in humanity as an entire collective body can reach enlightenment by itself - and therefore must be pulled, due to human base desires.
Knowing this, how can I accomplish it? I have theories, but I don't want to pre-form the suggested solutions.
I'm in edtech building this.
it's like having 1 teacher with 25 assistants.
Yes, Einstein's theory of relativity was a change from Newtonian physics but it's a fairly minor correction for most practical purposes and Newtonian physics is still important to know and understand.
So yeah, our understanding of physics will likely change but it'll only matter in more and more extreme edge cases and will likely build on our current understanding. Maybe it'll result in us finally having fusion reactor, room temperature super conductors, or quantum computers but you're still going to get a roughly parabolic arc when you throw a ball through the air.
The whole beauty of science is that it doesn't ever claim to have static, absolute answers - it's constantly growing and changing as we learn more about everything.
Likewise, the humanities are always growing and changing and being reinterpreted, reflecting what and how we can understand now.
> Who knows if software will be nearly recognizable in 10-20 years from now
Software goes through rapid cycles of invention and forgetting what's come before. Its totem animal is a Nobel laureate goldfish. That doesn't change.
Goldfish have good memories it turns out: https://www.bbc.com/news/uk-england-oxfordshire-63242200
That's wrong for maths and, by extension, theoretical CS. I mean, sure, some of the answers come with caveats ("assuming P!= NP", etc.), and in theory, all of mathematics could be proven inconsistent (but that to me is completely unreasonable to believe), but for all intents and purposes, these answers are static and absolute.
Sure, and I agree with what you've said. But as you pointed out, you have to be very particular/exact with language with these things. I just wanted to emphasize that there are many constrained settings (but still practical) where Rice's theorem doesn't apply.
If AIs seriously threaten our real elites, AI will be destroyed.
BuT tHe DaTa SaYs We NeEd a Ux ChAnGe
Trouble arises when you're looking to use latexmk (requires Perl), bib2gls (requires a Java runtime), minted (requires Python), latexindent (requires specific Perl libraries), including SVG (requires InkScape, and I believe ImageMagick), ...
Any notion of a powerful, sane, batteries-included development environment (think Rust and Go) requires jumping through insane hoops, resulting in bespoke setups, always on the brink of breakage. I really don't want to manage Python venvs!
The LaTeX crowd is very old school and tooling isn't natively available or built with containerization in mind. I've grown to like single-binary approaches (Caddy, typst, ...). I find vanilla LaTeX documents (the type that will compile in 20 years to come) very weak. UTF-8 still isn't standard in "vanilla LaTeX" (pdflatex)!
You do raise some valid points, but most people don't really use those programs you've mentioned (I've only ever used minted), and you have to consider the insane breadth of things (often very specific to particular scientific disciplines) that LaTeX and its ecosystem are dealing with. Plus, IMHO it's a bad choice to abandon LaTeX's maths notation (despite its obvious weaknesses) entirely because the institutional inertia here is incredibly high; it's used basically everywhere even outside LaTeX itself (MathJax, Discord bots, several online forums, etc.). Mathematicians (and students / researchers from adjacent disciplines) have become _really_ used to it. And unlike programmers, researchers don't usually need or want paradigm shifts every few years.
When I listened to Sasha Shulgin (Discovered many of the known psychedelic substances) speak, he believed that psychedelic substances should be used within the realm of something like a religion or a similar societal traditions.
Imagine your problem is to write Hamlet by Shakespeare. One way to write Hamlet by Shakespeare is to enlist many monkeys who then type at random (but with the property that no two monkeys type the same thing). Each monkey also has a special "done" button that they press when they're done writing. Some monkeys never press it and write forever.
So you instruct each monkey to type one key and then you check each monkey to see if they pressed "done". If they pressed "done", you check if they wrote Hamlet. Otherwise, you continue and have the monkeys each press another key.
Since there are infinitely many different monkeys, so you can't enlist them all at once, because then checking after each key press would take you infinitely long! This is why you play that game with at the first step you enlist one monkey, then two, then three, etc; it ensures at each step there are finitely many monkeys to check.
Of all the possible monkeys, one monkey will write Hamlet exactly and then press "done". This scheme finds that monkey. Similarly, for Turing machines, there will be at least one (technically, infinitely many) that solves your problem. You just have to figure out which one it is by doing the enumeration that stephencanon detailed. If P = NP, that enumeration process can happen in P time. Keep in mind that all problems in NP have polynomial time verifiers. So you can always check a solution (i.e., checking if the monkey actually wrote Hamlet) in polynomial time.
I understand why verifying can happen in time P for each machine, but I'm still confused on how you're sure that you hit on the right machine in P time.
For writing Hamlet, you have way more than "P" options for machines, if you're typing at random (I'm not sure how you'd define the input in this case, maybe the length of the work you want them to write?). So even if you can verify in time P, you'll still need to go through way-more-than-P machines before one solves your problem.
Maybe there is a way to use universal search or something to make this happen faster, but I'm not sure how a non-constructive P=NP proof actually gives you the algorithm.
(I tried looking a bit into this and I didn't yet find something that shows otherwise, but I didn't have time to look much.)
My monkey stuff was a bit wrong; the enumeration stuff actually goes like this: let every binary string be a Turing machine. So TM 0 is the string 0, TM 1 is the string 1, TM 2 is 10, and so on. Every possible TM machine can be encoded in this way.
For half your time steps, run TM 0. For half of the remaining time steps, run TM 1. For half of those, run TM 2. So the sequence of execution actually looks like this:
0, 1, 0, 2, 0, 1, 0, 3, ...
This means that every 2^(n+1) steps, the nth TM does 1 step of computation. Say that the kth TM is the smallest TM that solves your problem. Then it takes 2^(k+1) * O(m^c) steps to solve your problem, where O(m^c) is a polynomial bound. (I've ignored accounting for the time for verification, but that's polynomial too, so it doesn't matter.) That O(m^c) comes from the fact that we know that a polynomial time algorithm exists for our problem (but we don't know what it is), since P = NP.
But 2^(k+1) is a constant; it doesn't depend on the input size. The smallest program that solves our problem (in general) will always be the kth program. (Technically on some inputs there will be smaller programs that solve the input just by getting lucky, but TM k is the smallest program that solves our problem for all inputs.) So 2^(k+1) * O(m^c) is a polynomial number of steps, just with an absurdly huge constant of 2^(k+1).
I found [1] to be helpful and it probably explains it better than I just did.
[1] https://steemit.com/steemstem/@markgritter/leonid-levin-s-un...
Also, more importantly, I still don't see how Universal Search solves what parent actually said, which is that even without knowing the algorithm this will work. The link you added seems to suggest you need to know the actual P algorithm for this to work.