Friday, October 26, 2018

My favourite public speaker

I tend to agree with Paul Graham's blog post [1] about how easy it is for entertaining talks to have little substance at all. For example, I showed this TED talk [2] in a lecture to illustrate the power of posture, voice and body language in crafting a talk devoid of real content. The talk manages to be extremely entertaining. However, I'm secretly afraid that talks like [2] are entertaining not despite a lack of content, but because of it.

I think that really good public speakers manage to be easy to listen to despite delivering real, hard content. Digesting facts and understanding arguments is hard work, and a good public speaker should make it easy for the audience to really learn something new.

That's why my favourite speaker during undergrad was the late Hans Rosling, who had a knack for telling the stories behind that data in an entertaining way. Rosling's talks were often about the public's misconceptions about the world, about how our view of the world was outdated. He began his talks with humorous anecdotes, then delivered his message by taking his audience close to the data with a contagious enthusiasm. The choice of data visualization was most important, and can be anything from an animation showing changing income distributions [3] to stacked toilet paper rolls showing population growth [4].

A great computer science speaker is Gary Bernhardt from Destroy All Software [5]. If you are old enough, you will probably remember his "Wat" talk [6], and his less hilarious but more insightful talk on software Boundaries [7]. In his talks, Bernhardt introduces concepts in a meaningful order, and explains even complex concepts in a clear and concise manner.

I can't finish this blog post without mentioning Bret Victor [8] and Andrew Ng [9], both experts in their chosen fields. Their fields and their styles could not be any more different. But both of them tend to choose other mediums to communicate their ideas, and both their talks tend to be less entertaining than they are mindblowing. Both these people find ways to distill their ideas down to their essence, then explains them in simple words. Even though they may not make as many jokes as Berhardt, Rosling, or the talks Paul Graham refers to in his blog post, Victor and Ng's talks are worth watching.

This blog post was written so that I could follow my own prompt at



Thursday, September 20, 2018

Candy Japan's A/B Testing

The idea behind A/B testing is very simple: randomly assign users to groups A or B, change one thing between the groups, then see whether the change affects some metric of interest.

Candy Japan recently wrote an article on "A/B testing how to ask YouTubers for product reviews". I really like articles like these, where an author breaks down a real experiment and discuss what they learned. In this case, the experiment was emailing Youtubers to see whether they would be interested in making an "unboxing" video to review Candy Japan's product. The author varied the words used in the email and analyzed how the variations affected the rate of a positive response. In total, 180 YouTubers were emailed.

Considering the amount of effort required to message each YouTuber, the author took the opportunity to A/B test several different "splits" simultaneously. The author, unfortunately, does not discuss his methodology regarding the splits, so I'll assume that each split was even and independent of the others.

Some of the results presented were great. For example, including a call to action:
If you would like to receive a review box, please let me know your mailing address.
had the most impact, with the message reducing positive responses by more than 10%. It is a well-known fact in advertising technology that including the price of an item decreases the click-through rate. Here, asking for something as sensitive as a person's mailing address in the first email can come off as creepy.

I did find some of the other results less than convincing. There was only a less than 2% difference in positive response rates difference between including the following "elevator pitch" vs not.
I run a site called Candy Japan, which sends surprise boxes of Japanese sweets to members twice a month.
Since the 180 YouTubers would need to be split into two groups, with each group having around 90 YouTubers, the difference in positive responses between the two groups must be no more than 2. This is a very small difference, too small to justify even the author's toned-down conclusion that "including an elevator pitch of your service may help."

Moreover, the difference between offering viewers a discount vs not:
I can also give your viewers a discount coupon.
was also around 1-2%. It is interesting (but understandable) that the author's takeaway there was that discounts don't really matter -- at least for getting YouTubers interested in making a video. Of course, including a discount might encourage video viewers to become new CandyJapan customers, which is the real goal.

I'm as surprised as the author that the positive response rate was over a quarter. The success is a testament to the author's effort in targetting the right channels. I wonder how results would change if he targetted a wilder group of YouTubers, without the initial selection. I also wonder how he assigned channels into groups, and whether there were any correlations between the groups. Experiments like this are so difficult because there are so many features to test, and getting a large sample size is a lot of work.

The author promised a part 2, to test whether this method of advertising yields better results than buying YouTube ads. I'm very curious to see the results.

This blog post was written so that I could follow my own prompt at 

Tuesday, September 18, 2018

A (re)introduction

It is surreal that this blog has been around for almost a decade. I'm almost afraid to write here, lest someone dig up something unexpected from my undergraduate days. (Please don't. I don't know how I got away with such terrible writing.)

The reason I'm back is because I am teaching a communications skills course to undergraduate computer science students. I am asking the students to write weekly blog posts, and figured I should follow my own prompts once in a while. I want to feel the same blank screen and blinking cursors as my students.

So here I am, following this week's prompt to introduce myself to everyone in the class.

I am Lisa, currently an "Assistant Professor, Teaching Stream (CLTA)" at the University of Toronto Mississauga. The term "Teaching Stream" means that I am what is traditionally called a "Lecturer". The acronym "CLTA" means that I'll be at UTM for the next 2 years.

My path to teaching was long, winding, and full of surprises. I was once on a roller coaster called a startup. I spend several years as a data scientist, building models to make people click on ads. I published a few papers during my masters, and learned to write (more) properly. I hope that traversing the winding path of life made me a better teacher, so I that can bring together startup, industry, and academic experience to the courses I teach.

I told my students to email me if any part of my background interests them. A few students took me up on the offer. They asked, for example, about how to get an internship at Facebook. Maybe I'll share some advice here too, someday. Until then, email me with your questions about getting into grad school, getting started with machine learning, and applying for internships! It is much easier for me to share resources via email than in person.

The truth is, I never expected teaching to be a possible career option. Pursuing this career is more risky than one might realize. To add to the fun, I don't have a PhD and don't (yet) intend to get one. But what would life be if we don't take chances to do what we find meaningful?

So, if you know of universities that are hiring full time teaching staff in Computer Science, especially Machine Learning, please let me know.