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I made a mistake in posting a women-hostile picture on Twitter yesterday. This is an apology. But first, let’s start with a recap.

Yesterday I posted this tweet:

This: http://t.co/wo4gsU5NCT (By now you should know that I think gender/identity politics is a waste of society's time)

— Chewxy (@chewxy) September 25, 2015

I first saw the picture on /r/funny. And I tweeted the picture after a brief view. I mainly tweeted the comic because I believe that politicking identity issues is generally a waste of time* Politicking of any issue is generally a waste of time, in my opinion. . I had neglected to notice that it came from @AntiFemComics.

This morning, a shitstorm ensued. I woke up and the first notification was from Nick Coghlan:

@chewxy Seriously, dude? You're going to blithely ignore all the evidence of structural biases and claim women just aren't interested?

— Nick Coghlan (@ncoghlan_dev) September 25, 2015

Upon reading that, I went and re-read the comic. I realize the horror that I have in fact misread the comic. And the issue snowballed on. This blog post will stand as an official apology from me.

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Just a quick one. I was working on improving performance for a certain method of mine. I had found the hot loop * pprof is an amazing godsend. The days of dicking around in valgrind or cProfile are long a memory of the past , and I wrote a few benchmark methods to test some ideas. I was using the testing package’s benchmark function to benchmark the methods, and for a method, I had abstracted out some code so that it can run in a goroutine. [Read More]

I’ve been toying around with new ideas of coffee lately. Here is one that I think went particularly well. It started with red-eyes: you put a shot of espresso in filter coffee, just to boost acidity and body of the coffee whilst still keeping the basic aromatics in the coffee (making espresso kills quite a bit of those).

I then moved on to the idea of making cascara red-eyes. If red eyes were flavourful, perhaps then using the pulp of the fruit will yield a different thing all together? And indeed it did. The hibiscus-y nature of the cascara tea does accentuate the espresso. Then I wondered if I could push it further – what if the cascara “tea” was made under pressure – i.e. espresso?

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George Price was a rather interesting fellow. A few months ago, I was reading a rather interesting piece about his life from HN. If you follow my blog posts (hello to the two of you), you’ll note that altruism and cooperative games is one of the things I like to blog about.

Following that article, I discovered the Price equation* Funny story. I was quite surprised I hadn't heard of the Price equation, so I hit the books. I found the equation being referenced very very very very briefly in Martin Nowak's Evolutionary Dynamics, and that was all . While grokking the equation, it had suddenly occurred to me that kin selection and group selection were indeed the same thing. It was a gut feeling, and I couldn’t prove otherwise.

I recently had a lot of time on hand, so I thought I’d sit down and try to make sense of my gut feel that kin selection and group selection were in fact the same thing. Bear in mind I’m neither a professional mathematician nor am I a professional biologist. I’m not even an academic and my interest in the Price equation came from an armchair economist/philosopher point of view. And so, while I grasp a lot of concepts, I may actually have understood them wrongly. In fact, just be forewarned that this entire post was a result of me stumbling around.

So, let’s recap what the Price equations look like (per Wikipedia):

Simply put, $latex \Delta z$ is the difference in phenotype between a parent population and the child population. And that difference is a function of two things:

1. The covariance of fitness and phenotype — $latex \frac{1}{w} cov(w_i, z_i) $ where $latex w $ is the average fitness of the population, $latex w_i $ is the individual fitness of $latex i $, and $latex z_i $ is the phenotype shared in the group.
2. The expected value of the fitness of the difference between the group’s phenotype and the parent group’s phenotype.

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In The Terminator (1984), Skynet sends a T-800 to terminate Sarah Connor. And the Terminator had to look up a phone book to find three Sarah Connors, because it mainly didn’t know what Sarah Connor looked like or where she lived. That made sense in 1984. If the records had been destroyed in the war – records can be destroyed because physical drives were expensive and don’t have much capacity. Skynet wouldn’t have known how Sarah Connor looked like, or any other of her personal details. [Read More]

I gave a talk at PyConAU – about monads. This blog posts contains some thoughts about the talk, and some addendum/errata that I was not able to cover in the talk. But first, here’s the talk and associated slides.

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Carina Zona gave the Sunday keynote for PyConAU 2015. It was a very interesting talk about the ethics of insight mining from data, and algorithms. She gave examples of data mining fails – situations where Target discovered a teenage girl was pregnant before her parents even knew; or like machine learned Google search matches that implied black people were more likely to be arrested. It was her last few points that I got interested in the ethical dilemmas that may occur. And it is these last few points that I want to focus the discussion on.

One of the key points that I took away* not necessarily the key points she was trying to communicate – it could just be I have shitty comprehension, hence rendering this entire blogpost moot was that the newer and more powerful machine learning algorithms out there are inadvertantly discriminate along the various power axes out there (think race, social economic background, gender, sexual orientation etc). There was an implicit notion that we should be designing better algorithms to deal with these sorts of biases.

I have experience designing these things and I quite disagree with that notion. I noted on Twitter that the examples were basically the machine learning algorithms were exposing/mirroring what is learned from the data.

The Google example is merely algorithms exposing the inherent bias in the genpop #pyconau

— Chewxy (@chewxy) August 1, 2015

Carina did indeed point out that the data is indeed biased – she did indeed point out that for example, film stock in the 1950s were tuned for fairer skin, and therefore the amount of photographic data for darker skinned peole were lacking * This NPR article seems to be the closest reference I have, which by the way is fascinating as hell.

But before we dive in deeper, I would like to bring up some caveats:

• I very much agree with Carina that we have a problem. The points I’m disagreeing upon is the way we should go about to fix it
• I’m not a professional ethicist, nor am I a philosopher. I’m really more of an armchair expert
• I’m not an academic dealing with the topics – I consider myself fairly well read, but I am by no means an expert.
• I am moderately interested in inequality, inequity and injustice, but I am absolutely disinterested with the squabbles of identity politics, and I only have a passing familiarity of the field.
• I like to think of myself as fairly rational. It is from this point of view that I’m making my arguments. However, in my experience I have been told that this can be quite alienating/uncaring/insensitive.
• I will bring my biases to this argument, and I will disclose my known biases whereever possible. However, it may be possible that I have missed, and so please tell me.

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It’s actually quite fun that after years of using something, you still find a new way to do something. So at the last Sydney Python meet up, there were showings of how Python interfaces objects. Consider this for example: class Blah(object): ''' skipping the __init__ and stuff ''' def __add__(self, other): # skips checks and stuff return self.value + other >>> b = Blah(2) >>> b + 2 4 However, it was pointed out by my friend Julian, that the other way wouldn’t work – that operator overloading was only left associative: [Read More]

This blog has been awfully silent the past year. I guess now that my job has been made redundant, I’m going to return to writing more.

Hah! Here’s to hoping!

A few months ago, I blogged about my frustrations with logarithmic progressions with weightlifting. I highly enjoy linear progressions – who doesn’t enjoy work that is easy? But I was wrong about one thing: I hadn’t hit the logarithmic progression part. In fact as at the time of writing of this blog post, I am still firmly in the linear progression phase.

So what went wrong? The answer is form. I was basically squatting with exceedingly poor form. I was using all kinds of stabilizer muscles in an unbalanced way that left me injured often. I took notes and noticed that it was at around 55 to 60kg that I kept getting injured about and hence the weights I squatted lingered around there. There is an old saying goes: “Practice Makes Perfect”. That is wrong. The phrase that should really be passed around is “Perfect Practice Makes Perfect”.

The breakthrough came when I got got my partner to record me squatting for the first time. I had religiously read /r/fitness and /r/formcheck, so I had a fairly good idea of what good form is. I thought I had good form – I didn’t. One of the first things I noticed was that I wasn’t squatting anywhere near deep enough, despite the fact that I had all along thought that I was doing an ass-to-grass squat.

After years spending seated in front of the computer, I had no spatial awareness of how deep I was squatting. I had to learn what a deep squat was (learning the flexibility to do that is a tale on its own). I taught her how to check for correct form: the hip crease must go lower than the top of the kneecap to be counted as a good squat. And so she began to spot me. But this wasn’t fair for her as it was eating into her training time. So after a couple of sessions, I went about developing an app that used computer vision to determine if I was squatting with good form.

The thing about computer vision is while it’s easy to start, accuracy is a Difficult goal with a capital D. One indeed can spend a lot of effort to boost the accuracy a very miniscule amount. I cut down a lot of that by using various hacks like coloured sticker dots on the hip crease, knee and barbell tip to increase the accuracy of the app. By and large, I got it working, for me. But it wasn’t working for my partner, or a colleague who had begun to be interested about the app (he had separately approached me about the feasibility of an idea similar to SmartSpot, whose idea I love). The killing blow, I think was that I had irritated some fellow gym-goers by my wrapping of a gorillapod around their racks or bars in order to set up a static filming point.

And so it transpired I would need a new app. The app would have to do these things in order to teach me to have a better squat form:

• Monitor my form as I squat
• Inform me when I have hit a good form
• Only one person involved – no interfering with anyone else in the gym

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