If you haven’t read it yet, Peter Seibel’s Coders at Work (2009), is one of the best books about computer programming that doesn’t have actual code in it. It distills “nearly eighty hours of conversations with fifteen all-time great programmers and computer scientists,” with equal parts given to fascinating technical minutiae (including the respondents’ best/worst bug hunting stories) and to learning how these coders came to think the way they do.

So in a book full of interviews worth reading, it’s not quite accurate to say that Fran Allen stands out. It’s better to say that Allen is different; as a Turing Award recipient for her “pioneering contributions to the theory and practice of optimizing compiler techniques,” Allen spends much of her interview arguing that compiler optimization is woefully unstudied. Allen even argues that the popular adoption of C was a step backwards for computer science, which is kind of an alien concept for those of us today who almost exclusively study and use high-level languages.

Allen is also different in that she’s the only woman in Seibel’s book, and understandably, she has a few thoughts about their place in computer science. The summary of it is that she’s not at all optimistic about the “50/50 by 2020″ initiative (the goal to have women make up half of computer science degree earners by 2020). And the problem, Allen (who was a math teacher herself) is not in the curriculum:

I feel it’s our problem to solve. It’s not telling the educators to change their training; we in the field have to make it more appealing.

What I found particularly insightful in Allen’s interview with Seibel is that it’s not just about the need for more role models, because the current lack of women programmers is going to place a limit on that. In Allen’s opinion, girls have shown an equal aptitude for science, especially in medicine, biology, and ecology. So she suspects that the problem is with how _limited_ computer science can appear as a profession.

At my little high school in Croton, New York, we had a Westinghouse person nationally come in fifth. And they have a nice science program. Six of the seven people in it this year at the senior level are women doing amazing pieces of individual science.

What’s happening with those women is that they’re going into socially relevant fields. Computer science could be extremely socially relevant, but they’re going into earth sciences, biological sciences, medicine. Medicine is going to be 50/50 very soon. A lot of fields have belied that theory, but we [in computer science] haven’t.

I don’t necessarily think this perception that programming doesn’t seem to have a purpose behind obsessively sitting in front of a computer all day is exclusive to women. Even for those who’ve pursued a degree in computer science, it’s not clear how programming has relevance that is not an end to itself.

Check out this 2008 Slashdot thread, in which a recent computer science undergrad asks for suggestions of “Non-Programming Jobs for a Computer Science Major?” because he can’t think of ways to use computational thinking that doesn’t directly involve code. Or more recently, this screed by a NYU journalism professor, who sees coding as a trend du jour, little more than a pointless struggle to learn more code before a new language becomes hot and makes you obsolete.

I can’t claim to have insight myself, because when I left college with a computer engineering degree, I had no idea how to use it except to be a computer engineer, which I didn’t want to be, so I ditched it entirely at my first journalism job. Years later, I’ve slowly learned how to use programming to, well, practice journalism’s core function of interpreting and disseminating information. However, I attribute this to how much our world has become digitized with far fewer bottlenecks in applying computational thinking. So now it seems much more obvious that computer science can be as directly relevant to general society as medicine and ecology.

Non-scientists often assume that all scientists, and similarly left-brained people, can equally grok the concepts of programming. But this is as wrong an assumption as thinking that any programmer can easily pass the MCATs. Within the field of biological research, for example, there’s a difference of roles for biologists who can program and those who cannot.

The two fields of research are described as “wet-lab” and “dry-lab” work. In a recent issue of Nature, Roberta Kwok writes about how “biologists frustrated with wet-lab work can find rewards in a move to computational research“:

During her master’s programme in genetics from 2005 to 2008, Sarah Hird dreaded going into the lab. She was studying subspecies of red-tailed chipmunks and had become discouraged and frustrated by the uncertainties of molecular-biology experiments. She spent six weeks trying to amplify repetitive sequences in chipmunk DNA as part of an experiment to identify genetic differences between populations — but to no avail. Hird tried replacing reagents, switching to a different machine for running the polymerase chain reaction and decontaminating the sample-preparation area. Nothing worked. And then, for reasons that she never quite deciphered, the technique suddenly started working again.

By the end of her master’s, Hird had come to dislike working in a wet lab, and she decided not to apply for PhD programmes.

About six months after finishing her master’s degree, while working as a part-time technician at Louisiana State University in Baton Rouge, she discovered a better direction. The lab’s principal investigator had suggested that she learn a computer-programming language so that she could help with a simulation project. Hird, who had never programmed before, taught herself the language using a book and online tutorials, and quickly became engrossed.

“Once I started, it was like an addiction,” she says. She enjoyed developing algorithms, and she found the software-debugging process less frustrating than troubleshooting wet-lab problems. The work felt more under her control.

Later in her article, Kwok interviews a German biologist at the Max Planck Institute who offers this insight:

He notes that newcomers may stay more motivated if they can apply computational skills to real scientific problems rather than to the ‘toy’ exercises in a computer-science class. For example, a researcher who works with many image files could write a program to automatically perform processing steps, such as contrast enhancement, on thousands of images.

If young students – male or female – are turned off at the prospect of learning computer science, it’s not enough to just have role models. The usefulness of computational thinking are far too broad for just that. Why should only dedicated computer scientists benefit from the techniques and theory of programming, as if the importance of writing should only be left up to published writers?

Ideally the importance of computational thinking would be part of the general curriculum, and not just as a separate programming class, but integrated in the same way that you must read and write and perform calculations in your biology, physics, and economics class. But while we wait for that change to come about eventually – if at all – those of us in the field can help to increase diversity in computer science by increasing the visibility of computer science’s diverse impacts and applications.

After Allen complains about computer science’s too-narrow scope, Seibel simply asks, “So why do you like it?” She responds:

Part of it is that there’s the potential for new ideas every day. One sees something, and says, “Oh, that’s new.” The whole field gets refreshed very frequently. It’s very exciting to think about what the potential for all of this is and the impacts it can have.

Isaac Asimov made a statement about the future of computers-I don’t know whether it’s right or not-that what they’ll do is make every one of us much more creative. Computing would launch the age of creativity. One sees some of that happening-particularly in media. Kids are doing things they weren’t able to do before-make movies, create pictures.

We tend to think of creativity as a special gift that a person has, just as being able to read and write were special gifts in the Dark Ages-things only a few people were able to do. I found the idea that computers are the enablers of creativity very inspiring.

There’s a lot of other great stuff and stories in Allen’s interview, including her attempt to teach Fortran to IBM scientists, the need for compiler optimization in the age of petaflop-speed computing, and how other women in the industry, including one “who essentially was the inventor of multiprogramming”, have been robbed of their achievements. Read the rest of Allen’s interview, and 14 other equally great interviews with coders, in Seibel’s book, Coders at Work.

One of my least fulfilling reporting experiences involved a meeting between Ukrainian community leaders and local school district officials. There had been a brawl at a middle school and the Ukrainians, being the minority in that area, suspected their kids were racially targeted.

Anyway, the difficulty was that they didn’t speak English very well and many others at the meeting – the district officials and me, included – didn’t speak Ukrainian. So we had translators. The meeting had taken some time to plan and parents were nervous about their children’s safety, so everyone hoped for some kind of resolution. But after the meeting kicked off with long speeches by both sides – made doubly long by having to listen to the sterile translation that came afterwards, it seemed obvious that even a meaningful, impassioned debate was futile, nevermind reaching a satisfying resolution. The district officials told me afterwards, of course, that this was a good use of time, while the Ukrainian community members didn’t think anything came out of it.

When you think about it in theory, having a translator seems like a “good enough” solution. But when you’re sitting there, even though you and your other side share the same thought processes and general human understanding (independent of spoken language), a translator cannot:

• Connect statements to the body language and emotion nuance of the original speaker
• Mediate the kind of free-flowing give-and-take that is necessary for fulfilling debates
• Make up for the time delay between the original statement and spoken translation

And of course, there’s the problem of knowing if the translation is even correct, as we know from Suntory Time:

This is a roundabout way to argue for the need for digital literacy, i.e. the Learn to Code movement. Code.org launched with big fanfare this week, featuring just about, well, everyone – Bill Gates, Bill Clinton, will.i.am(?), and everyone’s favorite “computer geek trapped in an NBA player’s body,” Chris Bosh – telling us why we all needed to program.

And of course there’s the backlash: why does everyone need to program? Jeff Atwood wrote a well-read essay about it last year (“Please Don’t Learn to Code”), Coders Lexicon posted an essay last night:

The fact is, the whole world should not learn to code anymore than all of us should learn to be a space shuttle engine designer or a lawyer. While I understand the need for more people to get interested in computer science and to fill our ranks with people who can meet the skills of the 21st century, going out there and telling everyone that coding is as easy as putting a bit of syntax down into an IDE and hitting compile is not the way. We need passionate people who are creative and want to learn to DESIGN software in addition to coding.

There are a couple problems with this argument. The first is making programming sound as time-intensive and domain-specific as space shuttle engineering or law. The second is making programming sound like an all-for-nothing proposition: don’t get into programming unless you can do it really well, or else your program will kill people.

(Not that that hasn’t happened…but the more relevant statement would be: don’t code people-killing programs unless you know what you’re doing.)

What would schools be like if we warned children to not learn how to read or write because ideas in books and newspapers have frequently made people’s lives miserable? Or, how about, don’t learn math, or the metric system, because even Lockheed Martin and NASA engineers can screw up and lose a $125 million Mars satellite?

The reason to learn how code works is to have a direct involvement in the technology and data we rely on. Just as we learn to read and write at a basic level so that our time isn’t spent waiting for someone – who may be much better and specialized at it – to read the news or road signs or write love letters for us.

In the newspaper world, it’s assumed that every reporter knows how to write. That’s not even remotely close to reality. In the golden, fat days, a reporter could expect three to four layers of editors to go through his/her work before it got printed (today, readers just have to deal with a lot more typos and errors). But even then, you couldn’t get by as being a fantastic reporter and storyteller who was also illiterate. Think about it: you don’t need to know how to read or write to go around asking questions or dictating what you remember hearing of a blockbuster scoop. But imagine the editors who get to spend their day transcribing and retelling you what they’re actually putting down on paper.

More importantly, think about what you, the illiterate reporter loses out on. You don’t get hands-on involvement on how the facts and quotes are arranged, because you have to wait for it to be done (and then read back to you) before you have a say. And there’s a lot that you just don’t know that you don’t know. Like how a headline, by its size, color, and length, can change everything about how a reader perceives your story. Or how a story that’s in print has a much different, detached effect than what you’ve experienced with the spoken word (similarly, writing copy for broadcast is its own separate art compared to newspaper writing).

But you don’t know that because you know that there’s a story, of some sort, is on the paper and people seem to be reading it so what’s the big deal?

This is the gap of understanding for anyone who isn’t digitally literate. It’s not about knowing how to build a website or assemble your own computer. It’s not even that you need to know a lot, just as you don’t have to be able to recite the entire works of Shakespeare to justify traditional literacy. With digital literacy, it’s about knowing enough to know what’s possible with computers, data, and technology, to control more of how it affects you. Or, as Donald Rumsfeld puts it, having fewer unknown unknowns.

How much is “knowing enough”? There’s a lot that’s been written about it in better places, Code.org probably isn’t a bad place to start. Besides learning to program, which is not the only thing to do or even the ultimate end goal, the two practical steps I can suggest are:

• Learn how to use a spreadsheet – not just for numbers, but for taking notes
• Learn HTML/CSS not through a book or class, but through your web browser’s web inspector. It’s much faster, more interactive, and you can learn how to download cute cat videos from Youtube.

Also this week, Jeremy Ashkenas announced the first release of Literate CoffeeScript, a format that combines CoffeeScript with the easy-to-read-and-turn-to-HTML Markdown syntax. If you’re an experienced coder, this seems like nothing more than block-extended comments that are found in any language.

However, in practice, the power is in the details. I know that I avoid documenting code – even though we spend far more time re-reading code than actually writing it – because I just have never committed to some kind of system of making code comments easy to read (should I use asterisks to emphasize something? Or all caps? Or double space things?). With Markdown, you have the power of HTML to document your code. And more importantly, it’s intuitive and easy, which makes you more likely to write comments in the first place.

Check out Ashkenas’s demonstration: his own blog is powered by a “quick-and-dirty” blogging engine he wrote in Literate CoffeeScript. This is what the generated code and documentation look like on Github.

On a related note: if you’re a programmer who doesn’t understand why non-programmers need to be digitally literate: imagine having to write code in another language. Not programming language, but human language, such as in Chinese characters, without actually knowing that language. Sure, you could conceive an amazing program. But writing it yourself, with the help of an interpreter who types out what you say? Sounds even more laborious and mind-numbing than the worst programming project imaginable.