Earlier tonight I was accosted by a stranger for failing to finish the excellent book Software Foundations:
I think whoever recommended it to you had a common, bad idea… burnout with nothing to show
Hear that Tyler? That time you alighted on a branch with cloudstuff in your beak and illuminated us, tugging on the golden thread separating meaning and method, brought down the lambda calculus, gave us all the Formal Languages curriculum our professor wouldn’t dare and couldn’t dream—apparently you had a bad idea and that’s why all your students “burned out with nothing to show for it.”
What a lack of imagination—yet, there I was on HN and [I got to see it again]((https://news.ycombinator.com/item?id=15784012):
I’m not sure I agree with the rationale for the dynamic typing. The designer says it means the language supports an arbitrary degree of polymorphism, but obviously it means it accepts invalid programs with undefined behavior
Fun fact you may not know: every character I am typing right now, flowing through my computer to my blog, over HTTP to be rendered on your screen, is being handled at every step by an unverified algorithm in a language that accepts invalid programs with undefined behavior. EVERY FUCKING CHARACTER.
Do not be so preoccupied with your interests that you cannot tell when you’re following a fad. Is Haskell really awesome? Yes, definitely. But there are a ton of really cool things out there that do not have an HM type system, and yet, we remain alive. Safety is important, but it’s not so important that we should rush to burn down every other language that doesn’t prioritize it. Prolog is also weakly typed, and if you run a nuclear reactor on it, Prolog’s weak typing is not your first problem. A lot of software just isn’t that important—yours, for instance.
I could be learning J right now, but it’s hard, so instead, I’m checking my mail and reading Hacker News. Programming is hard. I think if you look at Control.Unification you’ll see a totally amazing thing, something that can do things Prolog will never dream of. But… try to write a 10 line Prolog program with it, and you will suffer. It’s just not the same thing. Programming will always be hard. I’m absolutely not convinced that strong typing makes it easier. It’s just another thing in the eternal technology treadmill, something for you to worry about other than “am I writing a useful program?” or “do I have a problem worth solving with code?” which are impossible questions to answer by applying fads.
All other things being equal (they’re not, but let’s pretend), APL will always have a cult of appreciation because it represents really the only time programming has said “we need our own alphabet.” But I don’t think APL symbols are really that evocative. Where does ⍳ come from? It looks kind of like a little i—it’s the index function, after all. i. seems just as legitimate and I can type that.
Some of them have some nice caché. ⌈ and ⌊ are min and max from mathematics, and J doesn’t have those. On the other hand, every other programming language on earth uses something besides × and ÷ for multiplication and division, and while learning APL there were plenty of times I used * when I meant ×, so I’m not sure this is especially significant. In actual math, I would probably just nestle two things next to each other for multiplication or use a dot, and division would be a whole huge thing. It doesn’t seem that hard to use <. and >. for min and max.
⋆ and ⍟ for exponent and logarithm come from where exactly? Nowhere. Now, ⊥ has an actual meaning in math that has nothing to do with decoding. ⊤ has a nice symmetry with ⊥ because it is visually inverted and is the inverse function. But J has many operators whose inverse is the same operator with a . or :; this set is #. and #:, but head/tail, behead/curtail, and exponent/log all follow a rule like this.
What does the symbol ⍕ have to do with formatting? There’s nothing it’s standing for here, it’s just meant to look a bit like encode and be typeable with an IBM Selectric using overstrike. Really. There were more APL symbols than spots on the typeball, so you’d back up and hit another key. That’s how they came up with ⌹ for matrix inverse. It’s a box around a division sign!
I’m not saying there are not nice symmetries with APL’s symbolry. There definitely are. But J also has nice symmetries. APL’s symbolry is closer to mathematics really only for multiplication and division, min and max, which you’re already accustomed to writing another way on a computer. The rest of APL’s symbolry is a total fabrication—just like J!
Let’s also note that in A Programming Language, Iverson proposes lots of syntax that you can’t do in APL or any other language. The inner product syntax, which is amazing, isn’t available anywhere but that book!
I realized recently that the way I have been handling answers to Prolog questions on Stack Overflow contravenes the rules. I don’t mind ignoring rules if it serves a higher purpose, but I have also come to realize that it actually is counterproductive.
There is a constant flow of pre-beginner questions about Prolog on Stack Overflow. The questions I’m referring to typically sound like this: “my professor said a few incoherent things about Prolog and here’s my assignment and I don’t know where to start.” The pre-beginners are not at Stack Overflow to look at existing solutions (which is what Stack Overflow wants you to do). They just want someone to do their homework for them.. They don’t come back later or develop a deeper appreciation for Prolog. Some of this is unavoidable, but the flow is very high.
As much as I would like to (and have tried to) take the opportunity to foster an interest in Prolog, their questions are actually a symptom of a totally different problem that simply cannot be addressed within SO. I think the underlying problem is that Prolog is part of a mandatory curriculum, but the professor doesn’t understand it. How do you teach something you don’t understand?
I would like to live in a world where professors consider Prolog worth knowing and put in the effort. That probably isn’t realistic. If I can’t have that, I would at least like to live in a world where students’ first or only exposure to Prolog is something more constructive than “Look how weird and confusing this thing is!”
I’m not in higher education, so I don’t know how to approach these problems. I would love to hear some ideas. But my first thought is a two-pronged approach:
A smallish (less than 10) solution database, where each solution is somehow annotated so that you can figure out both really low-level things (variables are uppercase) and really high-level things (why an accumulator is used). The idea is that it should be possible to answer any question, no matter how obvious, by mousing over things in the solution database.
A set of slides for use by professors to teach Prolog without really knowing it. There would be a few different versions (one class session, three class sessions) and a moratorium on providing solutions on Stack Overflow, instead pointing people who ask to the solution database.
Have other ideas? Please send me an email, let’s talk.
Every time I’ve spoken with someone about programming in the last month or so, machine learning or artificial intelligence has come up. Let me tell you the reasons why I don’t care a whole lot about it.
1. It’s not going to put me out of a job
At least 75% of the actual job of programming is figuring out what a person means when they ask for something. This is not a problem that a machine can solve for you, for the same reason that it’s harder to build a robot that builds other robots than to just build a robot. Have you had words with Siri lately? Do you think Siri is in a good position to understand your intent, or it’s maybe just searching for command words in a limited lexicon? Do you really think it could find Henry’s stool? Or does it seem more likely that it would get confused and require a human… trained to translate… high-level human-language… into language suitable for… the machine?
2. Machine learning is not creative
Machine learning is great at solving problems where you start with a thousand test cases and have no idea how to write the function to distinguish them. A great deal of the work of “software 2.0” will be a sort of data docent work involving curating data sets of different character. This isn’t going to be an effective or efficient way of solving general-purpose problems any more than it was when Lisp and Prolog claimed they could do it in the 1980s.
By the way, Lisp and Prolog are still fun and cool, and playing with them are a great way to wash the bullshit out of your eyes about AI.
3. More random failure is not a feature
When I ask Siri to set a timer for my tea, it works about 90% of the time. For reference, this means that about once a week, it fails to understand me, and I always say exactly the same thing. This failure rate is completely acceptable for toys like Siri. It’s really not acceptable for most applications we like to use computers for. Is a transfer of money involved? It seems unlikely to me that anyone who works for a living will accept a 10% chance that the money either doesn’t get sent or doesn’t arrive. It seems more likely to me you want the most straightforward code path you can imagine. A big detour through an opaque neural network for grins feels like a bad idea.
The majority of software is actually in this situation. You don’t want a 10% chance your web service does not get called. You don’t want a 10% chance that your log message does not get written or reported. You don’t want a 10% chance that the email to the user does not get sent.
When are you content with a high probability of nothing happening? When you’re making something that is essentially a toy, or perhaps, doing a task where no one can really tell if you’re doing anything at all, which brings us to…
4. ML is painstakingly squeezing the last drop of profit out of a failing business model
The nice thing about advertising is that someone can pay you $500 to do it, and then if nothing happens, you can just shrug and say “try again.” Google wants you to believe that you’re accomplishing something, but how many people out there actually know that the money they spend on Google is giving them a positive return?
Let’s play a game: if people who believe they need your product stop buying your product, and you’re left only with the customers who actually do need your product, how does your future look? I think if your primary product is advertising, you’re probably a lot more fucked than someone else who does something more tangible. To be blunt, if all fads were cancelled tomorrow, Facebook and Google would be done for, but Wolfram would get maybe a little blip. You want to work for the blip companies.
5. ML squeezes the last drop from the lemon
ML is a great tool for handling that last fuzzy piece of an interesting application. As an ingredient, it’s more like truffles than flour, potatoes or onions. It’s an expensive, complex tool that solves very specific problems in very specific circumstances.
Unfortunately, there are a few large companies that stand to make significantly more money if either A) machine learning makes significant advances in the failing advertising market, or (more likely) B) machine learning specialists glut the market and suddenly earn more like what normal programmers earn.
Does that change your understanding of why Google and Facebook might be trying to make sure this technology is as widely available and understood as possible? Bear in mind that unless you have massive data sets, machine learning is basically useless. A startup with a proprietary machine learning algorithm that beat the socks off anything Facebook or Google has would be greatly hampered by lack of data—and Facebook and Google are definitely able to compensate for any algorithmic shortcoming either by throwing a ton of compute at the problem or by throwing a ton of computational “manual labor” hacks at the problem.
6. Something else is going to end the profession of programming first
Of the 25% of my job that actually requires programming, probably about 90% of it is in the service of things users simply can’t detect. Code quality. Testing. Following the latest UI fads. Worry about the maintenance programmer. Design.
Here’s a frightening question for any programmer. How much of your job disappears if users don’t mind if the app is ugly and are able to do basic programming on their own? I’m not being facetious. If we were really honest, I think we would admit that A) most of what we do is done for reasons that ultimately amount to fashion, and B) a more computationally-literate, less faddish user base requires dramatically less effort to support.
Let’s recall Fred Brook’s program-system-product grid:
Fred Brooks program-system-product grid
What you’re seeing there is the admission of our industry that solving a problem costs 1/10th of what you’re paying, because you want a nice UI.
All it would take to decimate the population of programmers would be for people to get computer literate and not be so picky about the colors. This sounds to me like something that happens when crafts get industrialized: the faddish handcraftedness is replaced by easier-to-scale, lower quality work.
I think the emphasis on machine learning is looking at Fabergé eggs and saying “all handcrafts are going to be like this” simply because it’s very lucrative for Fabergé.
Nope.
Your software is not going to be that cool. It’s going to come in beige and all the ports are going to be standardized. But, it will actually do real work that real people need done, and that is way better than making useless decorative eggs for the new Tsars.
Conclusion
Machine learning is boring.
Here’s some Unix tools everybody should know about. They’re third-party and not especially well-known.
fish
fish - the friendly interactive shell
“Finally, a shell for the 90s” is a great tag line. For a long time we have sort of had to decide if we wanted a better programming experience or a better interactive experience with the shell. Fish has a very nice programming language, but the ooh-ahh factor is definitely the command line. Check it out.
pv
A utility that works like cat, except shows progress bars. This thing makes a huge improvement on the UI of your shell scripts. I highly recommend it.
parallel
parallel - xargs for multiple cores
It has a number of nice modes, but basically it does what it says on the tin: run a bunch of stuff concurrently. By default it will figure out how many cores you are and make your machine busy, but not create a bunch of contention. I use this in file conversion scripts now to good effect.
entr
This is a great little utility for tying behavior to filesystem changes.
cronic
cronic - a cure for cron’s email problem
Useful for making it so you only get mail from cron when something happens you should know about.
Who is your hero? Mine is Cook Ting.
Cook Ting laid down his knife and [said], “What I care about is the Way, which goes beyond skill. When I first began cutting up oxen, all I could see was the ox itself. After three years I no longer saw the whole ox. And now—now I go at it by spirit and don’t look with my eyes. Perception and understanding have come to a stop and spirit moves where it wants. I go along with the natural makeup, strike in the big hollows, guide the knife through the big openings, and follow things as they are. So I never touch the smallest ligament or tendon, much less a main joint.”
— Chuang Tzu, ch. 3: “The Secret of Caring For Life”
Cook Ting is my hero. He doesn’t care about fame. He just wants to do the thing he does, well. And in doing it, he has become so good at it, it almost defies explanation. He’s a cook, but not a chef.