January 29, 2023 7:51 AM

A Thousand Feet of Computing

Cory Doctorow, in a recent New Yorker interview reminisces about learning to program. The family had a teletype and modem.

My mom was a kindergarten teacher at the time, and she would bring home rolls of brown bathroom hand towels from the kid's bathroom at school, and we would feed a thousand feet of paper towel into the teletype and I would get a thousand feet of computing after school at the end of the day.

Two things:

  • Tsk, tsk, Mom. Absconding with school supplies, even if for a noble cause! :-) Fortunately, the statute of limitations on pilfering paper hand towels has likely long since passed.

  • I love the idea of doing "a thousand feet of computing" each day. What a wonderful phrase. With no monitor, the teletype churns out paper for every line of code, and every line the code produces. You know what they say: A thousand feet a day makes a happy programmer.

The entire interview is a good read on the role of computing in modern society. The programmer in me also resonated with this quote from Doctorow's 2008 novel, Little Brother:

If you've never programmed a computer, there's nothing like it in the whole world. It's awesome in the truest sense: it can fill you with awe.

My older daughter recommended Little Brother to me when it first came out. I read many of her recommendations promptly, but for some reason this one sits on my shelf unread. (The PDF also sits in my to-read/ folder, unread.) I'll move it to the top of my list.

Posted by Eugene Wallingford | Permalink | Categories: Computing, Teaching and Learning

January 18, 2023 2:46 PM

Prompting AI Generators Is Like Prompting Students

Ethan Mollick tells us how to generate prompts for programs like ChatGPT and DALL-E: give direct and detailed instructions.

Don't ask it to write an essay about how human error causes catastrophes. The AI will come up with a boring and straightforward piece that does the minimum possible to satisfy your simple demand. Instead, remember you are the expert and the AI is a tool to help you write. You should push it in the direction you want. For example, provide clear bullet points to your argument: write an essay with the following points: -Humans are prone to error -Most errors are not that important -In complex systems, some errors are catastrophic -Catastrophes cannot be avoided

But even the results from such a prompt are much less interesting than if we give a more explicit prompt. Fo instance, we might add:

use an academic tone. use at least one clear example. make it concise. write for a well-informed audience. use a style like the New Yorker. make it at least 7 paragraphs. vary the language in each one. end with an ominous note.

This reminds me of setting essay topics for students, either for long-form writing or for exams. If you give a bland uninteresting question, you will generally get a bland uninteresting answer. Such essays are hard to evaluate. A squooshy question allows the student to write almost anything in response. Students are usually unhappy in this scenario, too, because they don't know what you want them to write, or how they will be evaluated.

Asking a human a more specific question has downsides, though. It increases the cognitive load placed on them, because there are more things for them to be thinking about as they write. Is my tone right? Does this sound like the New Yorker? Did I produce the correct number of paragraphs? Is my essay factually accurate? (ChatGPT doesn't seem to worry about this one...) The tradeoff is clearer expectations. Many students prefer this trade, at least on longer form assignments when they have time to consider the specific requests. A good spec reduces uncertainty.

Maybe these AI programs are closer to human than we think after all. (Some people don't worry much about correctness either.)


On a related note: As I wrote elsewhere, I refuse to call ChatGPT or any program "an AI". The phrase "artificial intelligence" is not a term for a specific agent; it is the name of an idea. Besides, none of these programs are I yet, only A.

Posted by Eugene Wallingford | Permalink | Categories: Computing, Teaching and Learning

January 15, 2023 12:07 PM

You Can Learn A Lot About People Just By Talking With Them

This morning on the exercise bike, I read a big chunk of Daniel Gross and Tyler Talk Talent, from the Conversations with Tyler series. The focus of this conversation is how to identify talent, as prelude to the release of their book on that topic.

The bit I've read so far has been like most Conversations with Tyler: interesting ideas with Cowen occasionally offering an offbeat idea seemingly for the sake of being offbeat. For example, if the person he is interviewing has read Shakespeare, he might say,

"Well, my hypothesis is that in Romeo and Juliet, Romeo and Juliet don't actually love each other at all. Does the play still make sense?" Just see what they have to say. It's a way of testing their second-order understanding of situations, diversity of characters.

This is a bit much for my taste, but the motivating idea behind talking to people about drama or literature is sound:

It's not something they can prepare for. They can't really fake it. If they don't understand the topic, well, you can switch to something else. But if you can't find anything they can understand, you figure, well, maybe they don't have that much depth or understanding of other people's characters.

It seems to me that this style of interviewing runs a risk of not being equitable to all candidates, and at the very least places high demands on both the interviewee and the interviewer. That said, Gross summarizes the potential value of talking to people about movies, music, and other popular culture in interviews:

I think that works because you can learn a lot from what someone says -- they're not likely to make up a story -- but it's also fun, and it is a common thing many people share, even in this era of HBO and Netflix.

This exchange reminded me of perhaps my favorite interview of all time, one in which I occupied the hot seat.

I was a senior in high school, hoping to study architecture at Ball State University. (Actual architecture... the software thing would come later.) I was a pretty good student, so I applied for Ball State's Whitinger Scholarship, one of the university's top awards. My initial application resulted in me being invited to campus for a personal interview. First, I sat to write an essay over the course of an hour, or perhaps half an hour. To be honest, I don't remember many details from that part of the day, only sitting in a room by myself for a while with a blue book and writing away. I wrote a lot of essays in those days.

Then I met with Dr. Warren Vander Hill, the director of the Honors College, for an interview. I'd had a few experiences on college campuses in the previous couple of years, but I still felt a little out of my element. Though I came from a home that valued reading and learning, my family background was not academic.

On a shelf behind Dr. Vander Hill, I noticed a picture of him in a Hope College basketball jersey, dribbling during a college game. I casually asked him about it and learned that he had played Division III varsity ball as an undergrad. I just now searched online in hopes of confirming my memory and learned that he is still #8 on the list of Hope's all-time career scoring leaders. I don't recall him slipping that fact into our chat... (Back then, he would have been #2!)

Anyway, we started talking basketball. Somehow, the conversation turned to Oscar Robertson, one of the NBA's all-time great players. He starred at Indianapolis's all-black Crispus Attucks High School and led the school to a state championship in 1955. From there, we talked about a lot of things -- the integration of college athletics, the civil rights movement, the state of the world in 1982 -- but it all seemed to revolve around basketball.

The time flew. Suddenly, the interview period was over, and I headed home. I enjoyed the conversation quite a bit, but on the hour drive, I wondered if I'd squandered my chances at the scholarship by using my interview time to talk sports. A few weeks later, though, I received a letter saying that I had been selected as one of the recipients.

That was the beginning of four very good years for me. Maybe I can trace some of that fortune to a conversation about sports. I certainly owe a debt to the skill of the person who interviewed me.

I got to know Dr. Vander Hill better over the next four years and slowly realized that he had probably known exactly what he was doing in that interview. He had found a common interest we shared and used it to start a conversation that opened up into bigger ideas. I couldn't have prepared answers for this conversation. He could see that I wasn't making up a story, that I was genuinely interested in the issues we were discussing and was, perhaps, genuinely interesting. The interview was a lot of fun, for both of us, I think, and he learned a lot about me from just talking.

I learned a lot from Dr. Vander Hill over the years, though what I learned from him that day took a few years to sink in.

Posted by Eugene Wallingford | Permalink | Categories: Managing and Leading, Personal, Teaching and Learning

January 10, 2023 2:20 PM

Are Major Languages' Parsers Implemented By Hand?

Someone on Mastodon posted a link to a 2021 survey of how the parsers for major languages are implemented. Are they written by hand, or automated by a parser generator? The answer was mixed: a few are generated by yacc-like tools (some of which were custom built), but many are written by hand, often for speed.

My two favorite notes:

Julia's parser is handwritten but not in Julia. It's in Scheme!

Good for the Julia team. Scheme is a fine language in which to write -- and maintain -- a parser.

Not only [is Clang's parser] handwritten but the same file handles parsing C, Objective-C and C++.

I haven't clicked through to the source code for Clang yet but, wow, that must be some file.

Finally, this closing comment in the paper hit close to the heart:

Although parser generators are still used in major language implementations, maybe it's time for universities to start teaching handwritten parsing?

I have persisted in having my compiler students write table-driven parsers by hand for over fifteen years. As I noted in this post at the beginning of the 2021 semester, my course is compilers for everyone in our major, or potentially so. Most of our students will not write another compiler in their careers, and traditional skills like implementing recursive descent and building a table-driven program are valuable to them more generally than knowing yacc or bison. Any of my compiler students who do eventually want to use a parser generator are well prepared to learn how, and they'll understand what's going on when they do, to boot.

My course is so old school that it's back to the forefront. I just had to be patient.

(I posted the seeds of this entry on Mastodon. Feel free to comment there!)

Posted by Eugene Wallingford | Permalink | Categories: Computing, Software Development, Teaching and Learning

January 09, 2023 12:19 PM

Thoughts on Software and Teaching from Last Week's Reading

I'm trying to get back into the habit of writing here more regularly. In the early days of my blog, I posted quick snippets every so often. Here's a set to start 2023.

• Falsework

From A Bridge Over a River Never Crossed:

Funnily enough, traditional arch bridges were built by first having a wood framing on which to lay all the stones in a solid arch (YouTube). That wood framing is called falsework, and is necessary until the arch is complete and can stand on its own. Only then is the falsework taken away. Without it, no such bridge would be left standing. That temporary structure, even if no trace is left of it at the end, is nevertheless critical to getting a functional bridge.

Programmers sometimes write a function or an object that helps them build something else that they couldn't easily have built otherwise, then delete the bridge code after they have written the code they really wanted. A big step in the development of a student programmer is when they do this for the first time, and feel in their bones why it was necessary and good.

• Repair as part of the history of an object

From The Art of Imperfection and its link back to a post on making repair visible, I learned about Kintsugi, a practice in Japanese art...

that treats breakage and repair as part of the history of an object, rather than something to disguise.

I have this pattern around my home, at least on occasion. I often repair my backpack, satchel, or clothing and leave evidence of the repair visible. My family thinks it's odd, but figure it's just me.

Do I do this in code? I don't think so. I tend to like clean code, with no distractions for future readers. The closest thing to Kintsugi I can think of now are comments that mention where some bit of code came from, especially if the current code is not intuitive to me at the time. Perhaps my memory is failing me, though. I'll be on the watch for this practice as I program.

• "It is good to watch the master."

I've been reading a rundown of the top 128 tennis players of the last hundred years, including this one about Pancho Gonzalez, one of the great players of the 1940s, '50s, and '60s. He was forty years old when the Open Era of tennis began in 1968, well past his prime. Even so, he could still compete with the best players in the game.

Even his opponents could appreciate the legend in their midst. Denmark's Torben Ulrich lost to him in five sets at the 1969 US Open. "Pancho gives great happiness," he said. "It is good to watch the master."

The old masters give me great happiness, too. With any luck, I can give a little happiness to my students now and then.

Posted by Eugene Wallingford | Permalink | Categories: Personal, Software Development, Teaching and Learning

January 05, 2023 12:15 PM

A Family of Functions from a Serendipitous Post, and Thoughts about Teaching

Yesterday, Ben Fulton posted on Mastodon:

TIL: C++ has a mismatch algorithm that returns the first non-equal pair of elements from two sequences. ...

C++'s mismatch was new to me, too, so I clicked through to the spec on cppreference.com to read a bit more. I learned that mismatch is an algorithm implemented as as a template function with several different signatures. My thoughts turned immediately to my spring course, Programming Languages, which starts with an introduction to Racket and functional programming. mismatch would make a great example or homework problem for my students, as they learn to work with Racket lists and functions! I stopped working on what I was doing and used the C++ spec to draw up a family of functions for my course:

    ; Return the first mismatching pair of elements from two lists.
    ; Compare using eq?.
    ;   (mismatch lst1 lst2)
    ; Compare using a given binary predicate comp?.
    ;   (mismatch comp? lst1 lst2)
    ; Compare using a given binary predicate comp?,
    ; as a higher-order function.
    ;   ((make-mismatch comp?) lst1 lst2)
    ; Return the first mismatching pair of elements from two ranges,
    ; also as a higher-order function.
    ; If last2 is not provided, it denotes first2 + (last1 - first1).
    ;   (make-mismatch first1 last1 first2 [last2]) -> (f lst1 lst2)

Of course, this list is not exhaustive, only a start. With so many related possibilities, mismatch will make a great family of examples or homework problems for the course! What a fun distraction from the other work in my backlog.

Ben's post conveniently arrived in the middle of an email discussion with the folks who teach our intro course, about ChatGPT and the role it will play in Intro. I mentioned ChatGPT in a recent post suggesting that we all think about tools like ChatGPT and DALL-E from the perspective of cultural adaptation: how do we live with new AI tools knowing that we change our world to accommodate our technologies? In that post, I mentioned only briefly the effect that these tools will have on professors, their homework assignments, and the way we evaluate student competencies and performance. The team preparing to teach Intro this spring has to focus on these implications now because they affect how the course will work. Do we want to mitigate the effects of ChatGPT and, if so, how?

I think they have decided mostly to take a wait-and-see approach this semester. We always have a couple of students who do not write their own code, and ChatGPT offers them a new way not to do so. When we think students have not written the code they submitted, we talk with them. In particular, we discuss the code and ask the student to explain or reason about it.

Unless the presence of ChatGPT greatly increases the number of students submitting code they didn't write, this approach should continue to work. I imagine we will be fine. Most students want to learn; they know that writing code is where they learn the most. I don't expect that access to ChatGPT is going to change the number of students taking shortcuts, at least not in large numbers. Let's trust our students as we keep a watchful eye out for changes in behavior.

The connection between mismatch and the conversation about teaching lies in the role that a family of related functions such as mismatch can play in building a course that is more resistant to the use of AI assistants in a way that harms student learning. I already use families of related function specs as a teaching tool in my courses, for purely pedagogical reasons. Writing different versions of the same function, or seeing related functions used to solve slightly different problems, is a good way to help students deepen understanding of an idea or to help them make connections among different concepts. My mismatches give me another way to help students in Programming Languages learn about processing lists, passing functions as arguments, returning functions as values, and accepting a variable number of arguments. I'm curious to see how this family of functions works for students.

A set of related functions also offers a tool both for helping professors determine whether students have learned to write code. We already ask students in our intro course to modify code. Asking students to convert a function with one spec into a function with a slightly different spec, like writing different versions of the same function, give them the chance benefit from their understanding the existing code. It is easier for a programmer to modify a function if they understand it. The existing code is a scaffold that enables the student to focus on the single feature or concept they need to write the new code.

Students who have not written code like the code they are modifying have a harder time reading and modifying the given code, especially when operating under any time or resource limitation. In a way, code modification exercises do something simpler to asking students to explain code to us: the modification task exposes when students don't understand code they claim to have written.

Having ChatGPT generate a function for you won't be as valuable if you will soon be asked to explain the code in detail or to modify the code in a way that requires you understand it. Increasing the use of modification tasks is one way to mitigate the benefits of a student having someone else write the code for them. Families of functions such as mismatch above are a natural source of modification tasks.

Beyond the coming semester, I am curious how our thinking about writing code will evolve in the presence of ChatGPT-like tools. Consider the example of auto-complete facilities in our editors. Few people these days think of using auto-complete as cheating, but when it first came out many professors were concerned that using auto-complete was a way for students not to learn function signatures and the details of standard libraries. (I'm old enough to still have a seed of doubt about auto-complete buried somewhere deep in my mind! But that's just me.)

If LLM-based tools become the new auto-complete, one level up from function signatures, then how we think about programming will probably change. Likewise how we think about teaching programming... or not. Did we change how we teach much as a result of auto-complete?

The existence of ChatGPT is a bit disconcerting for today's profs, but the long-term implications are kind of interesting.

In the meantime, coming across example generators like C++'s mismatch helps me deal with the new challenge and gives me unexpected fun writing code and problem descriptions.

Posted by Eugene Wallingford | Permalink | Categories: Computing, Teaching and Learning