That Manifesto Thing

Last week someone at work brought to my attention the Google Manifesto shenanigan over the previous weekend. As a woman in tech, I hear about things like this all the time, but I’m too caught up with other activities to respond to these things publicly. I try to be a positive person, so instead of dwelling on all the lame comments that peppered social media, I’m just going to focus on those who have rebutted the manifesto with much better articulation that I could ever have.

A Brief History of Women in Computing: What I love about this article is that it pointed out what I felt was the biggest problem in the Google Manfiesto. The manifesto presented several biological research and used it to try to justify why women could be less suited for computing. However, as this article points out, the jump was too big. The biological components pointed out may explain certain traits, but not how those traits exactly cause an interest (or lack thereof) in computing specifically. As it is, the manifesto (yes I read it) sounded like it was motivated by the author’s deeply held stereotypes about women and he tried to back up his beliefs retroactively. Additionally, the manifesto does not address how modern computing environments were shaped by men and optimized for their own behaviour. Because let’s face it: a profession’s environment affects its workers, while workers in turn affect the environment. The relationship is symbiotic. Several of the manifesto’s points pertain more to computing environments rather than the actual task. For example, it said that computing is a high-stress profession requiring less empathy and social interactivity. Is it possible that women, with their different biology, could thrive in a different, yet equally productive, computing environment? I don’t know, and I think it would be more productive to conduct research on it than to rely on stereotypes to make leaps in conclusion.

So, About this Googler’s Manifesto: What I like about this article is his explanation about how engineering isn’t an isolated endeavour. This was a misconception I had when I was younger, and it’s actually something that attracted me to the field, because I like doing solo work. I’ve grown out of that misconception though, and I love computer science enough to also appreciate its collaborative and social aspect.

Tech’s Damaging Myth of the Loner Genius Nerd: This article expands a little bit more on the misconception of engineering as a solo task. What’s even more important is that it points out one of the things that really annoy me in the Artificial Intelligence / Deep Learning sector today: engineers seem to be developing tools for things that I don’t think many people will use. Take for example, machines that beat other players in a very particular game. What is this doing for the world at large? For people who are not gamers? For people from low-income households or third-world countries? What is this doing for the environment, for our healths, for improving society in general? As a computer scientist, making a positive impact in the world is my life goal, and it can be puzzling to hear that advancements in my chosen concentration mostly serve such a tiny niche. Every week you hear about that new deep neural net that can now replace a writer or an artist, but how about helping marginalized creators reach the audience who want to read their work? When did voices of machines become more important than the voices of humans? Especially when you know that these machines have been trained on a very particular subset of work that are most likely mainstream already. This article explains a little bit more on why empathy might be the key to averting this trend.

Anyway, that’s all I have for now. Like I said, I don’t like to dwell on this kind of situations. If you have any article you’d like to share, let me know. Or if you have thoughts about computer science or AI and what you think they can do for society, let me know too!

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Writing Joys: Re-outlining

Wow, I realize that I’ve only ever had “Writing Woes” posts, where I talk about everything that goes wrong in my writing. I didn’t actually have a positive writing post until now, which is kind of sad now that I think about it.

Anyway, “joy” is probably an overzealous word for what happened, but small wins are still wins in my book. Nothing dramatic happened, except that I managed to untangle the big hairy plot that I talked about in my previous Writing Woes post. Not only did I manage to do it, but I did it in 7 days. That’s… impressive by my standards, considering that I’ve been straining against this plot since the beginning of the year. Is it super-polished? Hah, no, I don’t think I’ll get to that stage until I’ve gone through 3 drafts at least. But the good news now is that I can move forward with my 1st draft without wanting to pull all my hair out.

I took a 3-week break from my story, and when I was re-outlining, I considered every plot point up for debate. And that worked out so well for me. When I considered that some of the determining plot points didn’t need to happen, or did not need to happen the way that I envisioned it would, it was much easier to tease out the tangles in the plot. I still lost a considerable number of characters, but I think it’s for the better. (Remember Nasi, the tarsier? I don’t know why I didn’t nuke him out of the story from the get-go. The poor animal had no speaking lines, had very little motivation, and didn’t contribute to the plot. But as attached as I had become to this useless character, I wound up giving him the pink slip as well.)

I believe there’s another Camp NaNoWriMo coming up in July, and I am going to try and finish the 1st draft then. I know it’s an ambitious goal, seeing it’s taken me 7 months to write the first half. But I think it’s also pretty telling that this children’s book is only halfway done at 70,000 words. I think I need to tell the story with less verbosity. Considering this is the first draft, I’ll resort to ‘telling’ rather than ‘showing’ if I need to move the plot along.

My hope is to be able to churn out a complete 2nd draft by the end of the year. It’s actually this goal that prompted me to re-outline my plot. My initial plan was to push through the first draft and figure out the changes to the plot as I go along, but it was causing me to lag behind my goals. I’ve read many writers advice that when you’re writing a draft, you shouldn’t go back and edit right away, but keep writing with your changes in mind. This wasn’t enough for me. I really had to revisit the entire story. It was disorienting for me to keep writing without addressing the issues from the parts I’d already written. It’s hard to build upon the story without knowing what events happened previously.

Learning is Intangible

In this post, I’m going to focus for a moment on my full-time job. I know that this blog is mostly filled with my hobbies and personal projects so it might seem like those are the only things I do. However, a good chunk of my life actually revolves around my career in tech.

I started my internship as a data scientist at the beginning of the month. It’s my 2nd full-time job as a computer scientist, and in some ways, I cannot help but compare it to my 1st full-time job. I worked as a front-end software engineer for over two years in a smaller company. Both companies are great, filled with talented people I get along with. More importantly, at both companies I am doing work that I am passionate about even though they are different.

And that’s what I want to focus on in this post: the difference between my experience as a front-end software engineer from a small start-up(-ish) company, and my impression so far as a data science in a much, much larger company.

I knew that the work would be different. And yet, I think I naively assumed that the job would be similar enough that I could measure my productivity in the same way. In my old job, I knew I was being productive when I managed to finish my assigned tickets. Depending on the tasks, I could finish about five moderate bugs in a day; for new features, I could at least get some new code out to be reviewed within a day or two at most.

In my new job, the process is entirely different. We’re working in Kanban style, rather than sprints. My tasks are a little more vague. Instead of having a specific goal I could measure, like changing the header background from white to grey, or adding a pop-out to a link, I’m assigned tasks like visualizing the clusters of similar items. As you can see, this task is less measurable. For one thing, the end goal isn’t to just have a nice visualization, right? Underneath that statement, I know that my goal is also to analyze the visualization, to obtain insights from the clustering. And this means that I have to find out a clustering algorithm that can actually give me a good visualization; it means that I have to find the data that can work with such an algorithm; it means that I have to find a visualization that can actually give me insights. And in the end, how do I know if the insights are meaningful or not?

For the past four weeks, I have struggled a little with this vagueness. Yes, I know I could ask, but I get the impression that it’s also part of the job of a data scientist to figure out these things. Whenever I’m assigned a task, it’s no longer up to a product manager to break down that task for me. It’s up to me to figure out what’s involved in that process.

I think this is the biggest difference between my previous job and the current one. As a junior software developer, my job was to implement whatever the product managers told me to. This is in contrast with a research position, where my job is to discover what must be implemented.

I worry that I’m not being as productive as I can be, and my worries are compounded with the fact that I don’t actually know how to measure my productivity as a data scientist. Which leads me to this passage from The Lean Startup by Eric Ries.

When I worked as a programmer, that meant eight straight hours of programming without interruption. That was a good day. In contrast, if I was interrupted with questions, process, or — heaven forbid — meetings, I felt bad. What did I really accomplish that day? Code and product features were tangible to me; I could see them, understand them, and show them off. Learning, by contrast, is frustratingly intangible.

Wow. This book is required reading for my Technical Entrepreneurship course that runs alongside the internship. I don’t have much of an entrepreneurial spirit in me, but when I read this, I thought, “Aha! This is why this book is required reading!” I never realized what it was that bothered me as I started my career in data science, until I found this passage in the book. I could have never put it in a better way.

Learning, by contrast, is frustratingly intangible.

I realized much of what I do in my new job as a research intern is learning.  When you’re researching, what you’re doing is learning. You’re learning what works and what doesn’t. I was so used to measuring my productivity in terms of how much code I write or how many tasks I finished. Now I have to figure out a way to measure my productivity in terms of learning and the return value from what I learn.

Filipino Tech Words?

In our generation, many people,especially teenagers,are not aware of some uncommon words in Filipino.That’s because of modern technologies and stuff.Today,Im gonna show you the 10 uncommonly used Filipino Words with meanings and correct usage in a sentence.So get your vocabularies up and read attentively. 1.Haynayan (Biology) -A branch of Science that deals with life. Example […]

via Ten Uncommonly Used Filipino Words — Site Title


I never knew! What a shame!

Philippine Speculative Fiction Volume 11

psf11

I want to share a bit of good news, and that is that my submission to this year’s Philippine SpecFic anthology has been accepted! This means that this year, I will see one of my stories published for the first time!

Here is the post that lists the 24 accepted submissions.

It’s quite a triumph for me as a writer, because it marks my debut as a published author. At least it definitely feels that way for me. I announced it a bit late, because I received the acceptance last month, but I didn’t want to go announcing it until they made their own announcement and my name is officially displayed. Hehe. I know this doesn’t seem much to some people, but I honestly feel very grateful and blessed. Thanks to the editors who chose my story to be part of the 11th volume.

I’ve kept quiet about this to my family and friends, because for some strange reason, I don’t know how to tell them. Hehe.