Advanced Programming Languages (2009)

http://matt.might.net/articles/best-programming-languages/

by lelf • 6 years ago

151 133 6 years ago

matt.might.net

"If, on the other hand, you're interested in enlightenment, academic research or a start-up, the criterion by which you should choose your next language is not employability, but expressiveness."

There is a big difference between enlightenment, academic research and a start-up.

For both academic research and start-ups, employability is a very important component:

- Doing academic research on "non-employable" languages decreases the impact of your research and your own employability;

- In a start-up, you want to pick a language where you can be productive with your team members, with an healthy community, and where you won't have hiring problems because you're using an "advanced" programming language.

TomMarius3• 6 years ago

- Doing academic research on "non-employable" languages decreases the impact of your research and your own employability

That is not a metric for choice of language for academic research at all. The academic research itself is a job, paid with public money, and you better not hinder it with your career plans. Most computer science research is not on languages itself, so you need a language expressive enough to allow you study whatever computer science concept you need to study; it's mostly C and Haskell around me, but I can't imagine my friends going into embedded C development - the C they write is very different, as are the practices.

alexilliamson• 6 years ago

All the economists working in Stata would like to talk to you about their employability

marceloabsousa3• 6 years ago

I was referring to academic research in software engineering or PL related areas.

I think your view of academic research is nice but perhaps a bit naive. Academic research in most countries is filled with pressure from private money to achieve valuable outputs to feed the private sector.

neutronicus4• 6 years ago

There are some groups with large code-bases that will want "employable language" experience (mine was one), but, at least in my field, outside of the groups with big simulation packages used by other groups, the job description is mostly bodging out a nice-looking .eps file and maybe some favorable-looking benchmark numbers any way you feel like.

andybak• 6 years ago

This article might need a date added. It mentions Ruby and Perl as "hot right now"...

melling• 6 years ago

I’m making an effort to learn Scala. It’s enough like Java that I can write as I would traditionally so I quickly wrote a Word Search puzzle generator.

Next I’m going to try and write a second version in a more functional way. Then a third...

First version: https://github.com/melling/WordSearchScala/blob/master/WordS...

quelltext• 6 years ago

it is possible to write an amb macro that "chooses" the right argument to make a subsequent assert statement be true.

I'm not too familiar with Scheme's macro feature but how would one go about that? I was under the impression with macros you get to define what ever syntax is part if the call, but here in order to achieve what the author mentions the macro would need to be able to look at surrounding code, to look ahead for the assert. How is this possible? Is he maybe talking of a compiler plugin or a macro (not shown) that encloses the entire expression, e.g. `(make-assert-true-macro (let ...{the code snippet}... ))`?

Xophmeister• 6 years ago

I'm just thinking out aloud, so I'm probably -- if not completely wrong -- at best sketchy on details! That being said, `amb` could be a function/macro that uses a continuation in such a way that, when a failure is encountered (however that may be defined), it can backtrace and try with the next value. By using continuations with some encapsulation of the call stack, it doesn't need to envelope the surrounding code and can just "rewind".

rscho• 6 years ago

Sicp, the ultimate.

https://mitpress.mit.edu/sites/default/files/sicp/full-text/...

ekidd• 6 years ago

You can implement 'amb' using either continuations or monads.

Back in 2005, I wrote a very short Ruby version of 'amb' using continuations: http://www.randomhacks.net/2005/10/11/amb-operator/ This approach works if your code contains no side effects, because it involves backtracking and restarting the computation as needed. But continuations are a heavyweight feature that place significant constraints on language implementation, so few languages support them.

You can also implement 'amb' using monads: http://www.randomhacks.net/2007/03/12/monads-in-15-minutes/ (Yes, this is yet another monad tutorial. Sorry.) This benefits from a "do notation" syntax, which could be implemented using macros.

nromiun3• 6 years ago

It is possible to implement it in a lot of languages.

https://www.rosettacode.org/wiki/Amb

goto113• 6 years ago

Every developers should know at least two languages (more is of course better, but two is the minimum for a professional). The optimal way to select:

(1) Learn the language which is most prominent or employable in your preferred industry (e.g. Java for enterprises, Python for science, JavaScript for frontend).

(2) Learn the language most different to the first, regardless of its popularity and employability. Eg. Scheme if you know Java, Haskell if you know Python, Assembler if you know JavaScript. (There will probably be some disagreement about which languages are to most different to each other, but you get my point.)

KMag• 6 years ago

I broadly agree with you here. I believe what you're saying is that once you're economically productive in your first language, you get the most bang out of your second language by picking a second language that encourages concepts and idioms not encouraged by your first language. In this way, your second language is really there to improve the way you think about your first language.

However, I would word (2) a bit differently, otherwise tons of people will be learning INTERCAL and BrainFuck. For a second language, consider only languages that are at least popular enough to have good learning resources. (This is a pretty low bar... there are probably hundreds of such languages.) Look at which concepts and idioms are encouraged by each language, ignore those concepts and idioms encouraged by your first language, and try and score the usefulness of the remaining concepts and idioms. Do a bit of research reading up on the pros/cons of the idioms and concepts to help you score them.

The goal here is to pick up concepts and idioms by learning your second language, which will then diversify the way you think about your first language.

I would absolutely look at the machine code generated by one of your languages (or bytecode, if none of your languages compile to native code or have a JIT that can dump out assembly) to understand what's going on, but I wouldn't suggest learning to write assembler as one of your two first languages. Most of the usefulness of assembler can be gained from learning to read it.

Random_ernest• 6 years ago

A good dogma I think fits roughly in the same category as your opinion is a quote from Alan Perlis:

“A language that doesn't affect the way you think about programming is not worth knowing.”

gustavo-fring• 6 years ago

JS changed almost nothing about how I view languages, nor did it teach me much, outside of what several libraries have. That does not mean it was not incredibly useful to know, both monetarily, and for what it enables me to do, when necessary.

CSMastermind• 6 years ago

Assembler if you know JavaScript

Maybe go with Rust instead.

Koshkin• 6 years ago

I think Python and C are simply a must, whatever the language you use in your daily job and whatever else you might want to learn. You cannot seriously call yourself a programmer if you are afraid of C, and Python is too useful as an ad-hoc tool to be ignored.

goto113• 6 years ago

I totally disagree with gate-keeping like "you are not a serious programmer if you don't know C". You are a serious programmer if you program stuff that works and achieves it purpose.

pjmlp4• 6 years ago

Nice to know that those early years doing Z80, 8086, 68000 and Turbo Pascal TSRs and demos wasn't programming.

nv-vn• 6 years ago

I think Python and C are simply a must, whatever the language you use in your daily job and whatever else you might want to learn. You cannot seriously call yourself a programmer if you are afraid of C, and Python is too useful as an ad-hoc tool to be ignored.

I haven't ever found Python to be useful since I started program. Perl, Bash, Ruby, JavaScript, etc. work equally well for one off scripts and Haskell works much better for mathematical calculations. For large programs I would never consider using Python. Maybe if I did machine learning my opinion would be different.

krebs_liebhaber7• 6 years ago

I don't do a lot of artificial intelligence, natural-language processing or machine-learning research, but if I did, Haskell would be my first pick there too.

This is definitely a dated article, since the obvious choice today would be Python, but I have to wonder: why aren't the "more expressive" (which in this post seems to be a synonym for "functional") languages used in these fields? I don't have much experience with ML languages, but it seems to me that something like Elixir (where the whole language is centered around pipes, parallelization, and operating on data) would be ideal for data science with a performant native library to back it up.

nkozyra2• 6 years ago

why aren't the "more expressive" (which in this post seems to be a synonym for "functional") languages used in these fields

The real answer is probably inertia, but ...

I think ultimately there isn't a ton of actual "programming" happening in ML, more implementation. People like Python. It's simple, it's clean, it's elegant and it's performant when it needs to be. I'm sure a lot of people who have thought "man why don't I just re-write this in [language]?" stopped short because they soon asked themselves "wait, why?" Most of your work is on either side of the algorithm (data - [ml] - analysis).

krebs_liebhaber7• 6 years ago

Functional languages are pretty good for the cleaning and analysis process as well. This blogpost in particular blew my mind:

http://evelinag.com/blog/2015/11-18-f-tackles-james-bond/

platinumrad• 6 years ago

Elixir is a BEAM language and therefore not designed for computational throughput.

krebs_liebhaber7• 6 years ago

That's true - it's why I said you need a native library (something like numpy, but as a NIF) to back it up.

bogomipz• 6 years ago

What is it specifically about BEAM languages that make them not good for computational throughput?

microtonal3• 6 years ago

why aren't the "more expressive" (which in this post seems to be a synonym for "functional") languages used in these fields

Because very often the best solution does not win, which is why we are still using x86(_64), Unix, etc.

Python is a good example of worse is better. A functional language could provide more elegant abstractions. But for most people the ML APIs that Python provides are good enough. By taking a pragmatic approach, Python was faster to capture some niches, including machine learning and data science. Now we are at a point that even if another language had better abstractions and had similar library support, it would face an uphill battle because of inertia. Why would someone invest in learning another language for machine learning when Python is good enough?

To me it seems that only a language that has both a good model development story and a good deployment story stands a chance to compete with Python, since deployment (especially on mobile/embedded devices) is a weak point of the Python ML ecosystem.

rtkaratekid• 6 years ago

Little tangent, but what do you consider better than Unix? I don’t have much experience with anything else so I’m curious.

snidane2• 6 years ago

"Worse is better" is a myth - it all depends of your definition of better.

The reason why Unix and Python win is simplicity. For many people simple == better.

Software is so easy to create and mold that many get trapped in excess complexity. Not being able to write complex software on a platform such as Unix or Python is therefore a feature, not a bug. It prevents you from ever getting trapped in the wet sands of complexity.

If there is a silver bullet in software engineering it is simplicity - and aggressive pursuit thereof.

pydry• 6 years ago

Python prioritized pragmatism over expressive purity. That alone put it way ahead of many other languages.

Attention to language interop (especially with C) and a decision not to try and be all things to all programmers was another reason for its success - particularly in data science which relies upon heavily optimized numerical processing libraries that are mostly best written in C, C++ or Fortran.

neutronicus4• 6 years ago

Honestly I think this is a little bit too abstract.

Imo the answer is that only one mainstream language bothered to emulate MATLAB's plotting API to the best of its ability.

jlouis• 6 years ago

The current way to get a lot of efficient computation on a modern computer is to engage the GPU of said machine. This means that any language hosted on the CPU is going to be at a disadvantage.

In short: you are better off writing a compiler toward the GPU and use it as a compilation target. And a lot of things argues that language benefits from being a functional one, given how GPUs tend to work on data.

If there were no prior work, and if we have the full GPU specs, I'd reckon you want languages which are strong in program translation: Haskell or OCaml comes to mind.

Elixir---as well as its Erlang sibling---are really good at handling problems where there is concurrency involved. It is subtly different from parallel computation in that you often have many threads of execution all wanting to do different things at the same time. It is the same space e.g., Go occupies really well.

Firadeoclus• 6 years ago

As someone who has been writing hand-optimised GPU code for a while now, I'd argue that this high level view (and thus an appropriate functional high-level language) can get you reasonably decent performance on a GPU for little effort for many highly parallel problems.

And there are still plenty of cases where this will leave performance improvements of an order of magnitude on the table, because the compiler isn't smart enough to work out the optimal sequencing of operations. Often the computational core which benefits from running on a GPU is small and its complexity low enough that, if performance is a priority, you are better off writing it in an imperative low-level language as a library which can be accessed from higher level code.

physicsyogi• 6 years ago

The current way to get a lot of efficient computation on a modern computer is to engage the GPU of said machine. This means that any language hosted on the CPU is going to be at a disadvantage.

That's only true if your calculations are mostly the kind of things that take advantage of GPUs, like working with deep neural nets or graphics. For most other things using multiple CPU cores will be better.

bregma• 6 years ago

why aren't the "more expressive" (which in this post seems to be a synonym for "functional") languages used in these fields?

90% of all programmers are cargo-culters whose knowledge of programming is gained from the answers they cut-and-paste from Stack Exchange or Quora. This is as true of "data scientisis" as it is of "web developers." The result is that there is a network effect making certain answers determinant of the language or monthly flavour of xxxScript that gets pasted most often, and reduces the domain in which further answers are examined.

If you want it in simpler terms: the new dudes looked around and saw all the cool kids were using Python and only a few boffins (UK) or nerds (USA) were using anything else, so they knew who to crib the answers from if they also wanted to look valley cool.

mratsim4• 6 years ago

Seems to me like if you want to look cool nowadays you would use Rust not Python.

Different communities have different needs. Data scientists, or physicists, or biologists don't care about memory safety, the programming language is a tool to run a computation for a limited time (not a server). The tool should not get in the way of fast iteration and rapid prototyping which many languages do in varying amount: C with memory management, Haskell with category theory, Rust with borrow checking, C++ with being C++, ...

Basically you are criticizing data scientists for choosing to not invent a new thing every time and reusing an established ecosystem.

dahauns• 6 years ago

If anything, Python in science is the opposite of cargo cult - it's more like pragmatic to a fault.

Its ubiquituous in academia/science, has a low barrier to entry, and a large amount and arguably often unmatched quality of specialized libraries/frameworks/tools for all kinds of scientific fields. So the first go to language/ecosystem will of course almost always be python.

I'm definitely not the biggest fan of Python (the language). And there certainly are more elegant/fitting languages for many problem classes. But the thing is, from my experience many people in the field are quite aware of this. But it simply doesn't matter if it's the best language from a developer POV. It just has to be good enough to get the job done, apart from this the points above are simply more imporant.

And it's hard to understate the importance of ubiquity in this case:

Python is the code equivalent of a lingua franca in science.

fnord1232• 6 years ago

PrincipalSkinner.png:

Is Haskell a bad language?

No, it's the people getting actual work done that are wrong.

roosterdawn• 6 years ago

To backproject the causation of popularity of a given ecosystem and language towards cultural relativism and sociological in-groups rather than pragmatics just seems like a post-hoc rationalization. But, to take your comment in good faith (I don't want to dismiss it) I wonder if there really are generational dynamics at play here.

Earlier on, software engineering wasn't as glamorous as it is today, from either a material or cultural standpoint. I even recall a staggering difference now versus twenty years ago, when I first got into it, which even then was right before the bust of the first dot com bubble. While ostensibly by that point, the field of software engineering as a creative pursuit and an intellectual space had already thoroughly lost its pioneer spirit to the metaphorical urban and suburban "gentrifiers", there's no denying that the field itself continued to grow, and even at that point is a small fraction of what it is today. If software has truly eaten the world, isn't it possible that the languages and ecosystem that survived and reached dominance outcompeted the other ones?

That is to say, what makes you believe that another language is more fit to be in the position where Python is? While I will use whatever language is necessitated by the job (and have never had issues doing that) and even prefer the aesthetics and "handling" of Ruby and modern JS, Python is still the tool I reach for most often when I need to get some general purpose scripting or systems design task done. The ease with which I can go from zero to prototype and prototype to production and production to pain-free maintenance is unparalleled. The only two ecosystems that are remotely close are JavaScript and Java, and ostensibly Rails for web dev. Nothing else is competitive.

What would make you dispute this?

neutronicus4• 6 years ago

I think it needs to be said that Haskell is just not actually all that ergonomic for mutably banging on buffers to solve linear algebra problems. I am a partial differential equations guy, so I have tried.

In ML as in PDE, correctness ultimately springs from performance, be it processing more data or using a finer mesh. So at the end of the day impedance matching with imperative linear algebra is pretty much a must.

mratsim4• 6 years ago

AI, NLP and ML are bottleneck first by memory accesses and second by CPU.

Whatever language you end up using, the low-level implementation must allow you to precisely control memory layout.

This is what is being done with C++ + Python but pure Elixir doesn't allow you to control memory layout and will kill your performance.

nromiun3• 6 years ago

Two things. Effort and support. You can probably wrap BLAS in any language and make a framework out of that. With some more effort you can add support for GPUs and make your framework much faster. But why would you when most of that is already available? Some might really hate Python but learning Python just for the framework would probably be easier then making a ML framework for the language you like. And Python wasn't even dominant until Tensorflow. Torch used to be a good competetor.

Many people make the mistake of promoting the language instead of the framework.

And the next thing is support. When some big company releases a ML framework and starts using it for themselves people start using it for their own projects. It's mostly because people think that it will last longer then a month. There were lots of dead python ML projects before Tensorflow.

So if you can get a big company to release a ML framework for your favourite language it will surely gain at least some market share.

The language almost doesn't matter. I say almost because big companies are more likely to choose popular languages.

ken• 6 years ago

I've seen a definite shift in what "artificial intelligence" means.

Norvig's PAIP is all about manipulating symbols. It's so fundamental that in any other language you'd have to Greenspun an interpreter first, just to solve the same problems in the same way.

Today, AI seems to mostly mean matrices. If that's all you need, then you can do it in any language, especially if you're just wrapping old C or Fortran libraries.

I haven't done any new-style AI but I get the feeling that most "research" is in finding new nails to pound with the same hammer. That's a legitimate area of research, but it also should be no surprise that the concept of "AI research" expanded so much in recent years because it changed from "learn to build your own lightsaber" to "pick up a hammer and hit something with it".

newen2• 6 years ago

There seems to be a misconception among non machine learning programmers that ML is all about multiplying matrices. In reality, there is a lot of architecture involved and having a programming languages that facilitates that would be enormously helpful.

Yes, there is a lot of multiplying matrices. But you need to think of a matrix as like a <div> or something and multiplying matrices and placing two <div>s next to each other. Then, think of how to create a dynamic website from that. There is a lot of architecturing involved other than multiplying matrices.

thesz4• 6 years ago

Behind matrices, there is a lot of symbolic processing in the data cleanup part. Most of the time, Python suffices.

Most of the time, people does not leave Python when it is not satisfactory. They stick with it because they have no clue, most of the time.

For example, it is not quite natural for Python folks to even think about creating rewrite rules (as in Haskell) for programs to stay at higher level of expression. E.g., if you use dictionary with default value, why not lift all operations up (inline, so to say) and avoid calling these that are overloaded? It bit me many times, yet other users of Python just gave me blank stare when I tried to explain these things.

softwarejosh• 6 years ago

libraries, examples, classes, scaling architecture, gpu integration, ease of syntax.

mostly the same reason new developers dont use functional languages for anything else either.

DonaldPShimoda3• 6 years ago

why aren't the "more expressive" (which in this post seems to be a synonym for "functional")

Nobody else seems to have tackled this specific issue so I'd like to.

Matt's use of "expressive" is not whimsical or full of personal ideology. He uses Felleisen's definition of "expressiveness" when comparing programming languages [0][1]. The work is a continuation of Landin's earlier efforts to formalize language comparison. From the text (where Felleisen is comparing two languages of "different size", which means that one provides more first-class "constructs" than the other):

Here "more expressive" means that the translation of a program with occurrences of one of the constructs c_i to the smaller language requires a global reorganization of the entire program.

The paper goes on to provide a mathematical framework by which to make these comparisons.

The languages Matt lists are more expressive than many non-functional languages for various reasons. Each of them has some functionality that, if you were to remove it, would require a "global reorganization of the entire program" to achieve the same goal. For example: if you have a Haskell program and remove automatic currying, you may now have to rewrite almost all of your functions and call sites to accommodate (perhaps by explicitly encoding delays, or by using continuations, etc.). Python does not feature automatic currying, so in this respect Haskell is more expressive than Python — there is a method in Haskell in which you can express your code which you cannot use in Python.

Note that the relationship is not predicated entirely upon individual constructs: it is possible for two languages to be compared and to find that each has expressive capability that the other lacks. However, by and large the functional languages Matt lists have focused specifically on increases in expressive power compared to the more "traditional" languages that most programmers use daily. (Haskell did this explicitly by design, but the others Matt lists came to it by way of exploring alternate approaches to problems and arrived at increased expressiveness incidentally.)

Later in the paper, Felleisen touches on Turing completeness (a common point brought up in these discussions):

If a programming language can represent all computable functions (on the integers), it contains a functionally equivalent counterpart to each program in a more expressive language. This raises the question as to what advantages there are to programming in the more expressive language when equivalent programs in the simpler language already exist. [...] > [...W]e have come to the conclusion that programs in less expressive languages exhibit repeated occurrences of programming patterns, and that this pattern-oriented style is detrimental to the programming process.

So this provides motivation for why one might want to investigate these "more expressive" languages. Being able to reduce code duplication is (almost) always a good thing!

---

[0] https://doi.org/10.1016/0167-6423(91)90036-W

[1] I know this to be the case because I've talked to Matt about this very article. :) The Felleisen paper is pretty popular in the PL communities Matt used to hang out in.

zerr• 6 years ago

Rust is an advanced language that might go mainstream.

krebs_liebhaber7• 6 years ago

I wouldn't call Rust an advanced language. It has some functional-inspired features (everything is an expression, ADTs, composition/pipes with iterators), but the language's raison d'etre is zero-cost abstractions, and many of the features of Haskell and the like require a GC and costly runtime checks. See this post on why Rust doesn't have functors, monads, and the like:

https://varkor.github.io/blog/2018/08/28/feasible-functors-i...

zozbot2342• 6 years ago

Affine types are definitely an advanced PL feature, and the compile time checking in Rust is largely based on them. ("Affine" meaning that values of such a type cannot be implicitly copied, but can be implicitly dropped. One reason why Rust doesn't yet have functors or monads is that it's not very clear to what extent these would usefully generalize to such a construct.)

marcosdumay• 6 years ago

Those zero-cost abstractions on Rust are pretty advanced, and superior to anything comparable in any other widely used language.

Your complaint stands basically to calling Rust a low-level language... what it is. It has advanced low level features, that bring productivity for some code that is completely different form what those high-level ones you point are concerned with.

codesections• 6 years ago

2009

(The article is undated but was present on the Internet Archive in 2009, https://web.archive.org/web/20090106175608/http://matt.might...)

bencollier492• 6 years ago

Was going to say, the recommendations seem rather dated.

AnimalMuppet4• 6 years ago

What would you recommend currently?

DonaldPShimoda3• 6 years ago

All the posts on Matt's blog are pretty old. I'd asked him to please add dates to the articles to make this more clear but he was (and always is) very busy so I guess it didn't happen haha. I think the most recent post is at least a couple years old, though he does update the rest of the site (publications, current work, etc.).

dang• 6 years ago

Many previous threads as well: https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que...

zozbot2342• 6 years ago

Finally, an article about PL's that doesn't mention Go or Rust!

(Oh, wait... the article is from 2009? Never mind then. Carry on.)

7thaccount• 6 years ago

As many have indicated, this article is pretty dated now.

In some ways it argues for a thorium nuclear power plant when subsidized wind is available. Yeah thorium is great, but we know how to do wind and the buy-in is cheap right now. You'd have to invest a lot in thorium to make it worth it.

Forgive the crude analogy.

Also, the people building ML models and doing data science aren't always ubber functional programmers, so Python is easier for them to get going with. Just like the people writing Enterprise software find Java to be fine.

blunte• 6 years ago

Especially in fast changing domains like tech, essays and blog posts must state publish dates.

zests2• 6 years ago

It would be interesting to see how the Scala section would change after the release of Java 8 and especially the release of Kotlin. Java 8 introduced the bare minimum functional programming support in order for the language to be used functionally. Not to mention libraries came out like reactive streams which essentially lets you write your entire code as chained function calls inside of an error monad.

Kotlin then made functional programming a first class citizen. Java is clearly recognizable inside of Kotlin under a bunch of layers of abstraction and syntactic sugar. Once you learn Java you can learn Kotlin with minimal effort and add more language features to your toolbelt as you get more Kotlin experience -- similar to what the author says about Scala.

Kotlin is probably not as advanced as Scala, and admittedly I've never used Scala, but it does seem to be taking some of Scala's market share.

marceloabsousa3• 6 years ago

"Kotlin then made functional programming a first class citizen" - what do you mean by this?

opnotme• 6 years ago

Not OP, but I assume they meant: Kotlin encourages you to solve problems functionally, whereas Java merely allows you to do so. Kotlin provides features like top-level function declarations (don't need to be declared as methods of a class) and functions-as-arguments, both of which make it easier to think functions first.

zests2• 6 years ago

In Java if you want to map a function over a list you write `list.stream().map().collect()` and in Kotlin you write `list.map()`. The functional programming APIs in Java are atrocious. Lambdas are much easier to write thanks to anaphoric macros and other syntactic sugar. Data structures are immutable by default. A big one that isn't mentioned often is that functions can exist outside of classes so your code is not as tied to Java's style of OOP.

nromiun3• 6 years ago

I love learning new languages. But I have yet to see anything that looks like the enlightenment in the article. Most languages gives you different ways to accomplish the same tasks. Some languages make some tasks easier and others hard. It doesn't really matter weather the language is hot, popular or obscure.

agumonkey3• 6 years ago

I'm really curious what 'advanced' will mean in 2030 ?

procedural programming fully gone and most programs being about metalevel self-adapting (ml or else) parallel agents ?

eli_gottlieb2• 6 years ago

I'm really curious what 'advanced' will mean in 2030 ?

Javascript on the GPU

agumonkey3• 6 years ago

That's mainstream advanced, not advanced^2

petercooper• 6 years ago

I'm only being half tongue in cheek here, but if you follow recent patterns with the growth of functional languages, perhaps find something being mentioned in the computer science journals in the 80s or 90s and imagine it at a bigger scale and with a few twists ;-)

A reinvented version of APL? Some twist on visual programming? Prolog-style logic programming with a built-in graph database? Or just stick 'quantum' on the front of anything?

If you care to do such spelunking and guessing yourself, the ACM's currently open library is a big help! For example: https://dl.acm.org/action/doSearch?fillQuickSearch=false&exp...

agumonkey3• 6 years ago

Yeah good point, although I think the FP revival is reaching top of the current wave. And you know, time and new things have a tendancy to be out of the ordinary.

One thing that seems very near reality, is logical auto completion or partial proover edition. Right now and IDE/typechecker will verify there's a logical path between A and B, but maybe they'll try some capped graph search to find possible paths when you need to compose some functions. I know I've seen similar things at very small scale in high end cgi software. Their node editors will introduce conversion nodes if you try to add scalar to a vector.

AnimalMuppet4• 6 years ago

procedural programming fully gone

No chance. Not by 2030, anyway. (If you just want to define "advanced" that way, you can define it that way today...)

most programs being about metalevel self-adapting (ml or else) parallel agents

Also no - not by 2030, anyway.

carapace• 6 years ago

I don't have the oomph to write a whole big thing this morning but uh, here are some links that are relevant IMO:

http://conal.net/papers/compiling-to-categories/

https://www.categoricaldata.net/

https://terminusdb.com/blog/ (It's a little market-ey, dig around for the meat. Graph DB in Prolog & Rust, "git for data", etc.)

https://github.com/Co-dfns/Co-dfns

edit: Ah it's you agumonkey (I don't check usernames before replying.) Apologies if I sound like a broken record in re: the above topics.

hota_mazi• 6 years ago

the criterion by which you should choose your next language is not employability, but expressiveness.

What a weird choice.

If you are looking for an out-of-the-mainstream language, you can't really tell right away if a language is expressive or not before you actually dive in.

Myself, I go for "exotic looking". I read the brief description of the language, look at a few snippets, and the weirder it looks, or the most foreign the main concepts are, the more I'll be interested in studying it for a few hours.

The point is not to be expressive, it's to stretch my brain.

djhaskin987• 6 years ago

I'm sick of the functional programming people telling us that they are better than we are.

I have written lines of code in Scheme, Haskell, and Common Lisp. I regularly code in Clojure. I like writing in functional languages too.

But every language makes trade-offs. The imperative languages generally try to optimize for readability and speed. The functional languages are often optimized for expression, the ability to easily express yourself to the interpreter or compiler. There is a trade-off though. These languages all trade high expressibility for slow execution times and steep learning curves. This trade-off might well be worth the effort especially if the team is small but it is a trade-off. It's not like it's objectively better to always program in the functional languages.

c3534l• 6 years ago

Most people are already familiar with procedural programming. If you want to grow as a developer, don't just learn more of the same, learns something that challenges you to work in a different way. If I only knew functional programming, I don't think I'd understand half of what I understand now. It's the fact that they accomplish the same things in very different ways that allows me to see what it is I'm really trying to achieve in programming and see different ways of doing that. The author even goes so far as to call these niche. That's what they are: weird little languages that do things very differently from what you're used to. There is no air of superiority in the linked article.

hajile• 6 years ago

Common Lisp doesn't belong on that list as it isn't particularly functional in general and is rather fast.

You can have expressive, easy to read, and performant, but most people have never seen StandardML before.

luord• 6 years ago

once the program compiles, it's almost certainly correct.

I'm pretty sure I've heard/read that remark about more than one language. It's interesting how that "almost certainly" often means "around one percent of the time" or the definition of "correct" is moved to a different goalpost.

Meaning, of course, that ultimately it is no different from any other programming language: great coders will write great code and bad coders will write bad code.

Advanced Programming Languages (2009)

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