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.

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June 2017 Reads

I wasn’t able to read a lot this month, and most of the things I read were for school. For some reason, I was just really tired most of the time, and even during my morning commute to work, I just didn’t feel like reading. I think it mostly has to do with my reading slump after finishing Thick As Thieves by Megan Whalen Turner. I’m hoping to hop out of this slump this month.

In any case, here’s what I read for school. These books are for my technical entrepreneurship class.

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I’m Glad This Came Up

Behold this gem.

Someone at work brought this up during lunch, and I read it after my break. I’m more on the ML/Big Data side of things, but as an aspiring writer, I can very, very much attest to the frustrations of NLPers. I’m glad someone finally called out the academic trend of “over-selling” especially when it pertains to deep learning, which is a buzzword that receives a lot of hype these days.

I think the problem definitely starts in academia and the sense of competitiveness there, but I also wish  that tech journalism was better. I remember reading this paper on using neural networks to separate the content and style of a piece of artwork; some articles that responded to this were so excited that they even deemed human artists obsolete. Or perhaps it wasn’t excitement so much as fear of the impending AI apocalypse. *sigh* I just wish for a more honest, more grounded coverage of what’s going on in the computer science community instead of the super-hyped up things we currently get both from the media and educational institutions.

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.

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Data Science and Social Impact

Data scientist Cathy O’Neil comments on how algorithms — “under the guise of math, fairness, and objectivity — reinforce and magnify the old biases and power dynamics that we hoped they would eliminate.”

via Cathy O’Neil on the Cold Destructiveness of Big Data — Discover


As a computer scientist, this is the kind of thing I’m concerned about. And very few people are sadly aware of it. It’s difficult for computers to solve problems that we, humans, don’t already know how to solve either.