Progress isn’t made by early risers. It’s made by lazy men trying to find easier ways to do something.
— Robert Heinlen
Yesterday a friend came to me with a problem. He and his colleagues regularly make use of articles posted on a website. It’s a useful site, but it has two shortcomings.
A recent press release from NYU celebrates the release of some new video games that “train your brain.” The games were developed by “developmental psychologists, neuroscience researchers, learning scientists, and game designers,” so you can be sure they’re thrilling. There are two issues I want to discuss. The first is their choice of control. The article is paywalled so I don’t know all the details of the experimental setup, but the abstract gives you a fairly good idea:
Cliff Stoll
Doubleday, 1989
Amazon
There was a time before we defaulted to locking our doors. In the 1980s, the nascent internet was mostly used by research scientists and the military. The community was small, and—as tends to be the case in small communities—the level of trust was high. Administrators didn’t invest much effort in locking down their systems, because the possibility of bad actors hadn’t been seriously considered.
I came across an interesting problem on the website of UofT prof David Liu (who I had the privilege of TAing for a few years back). Part of what makes the problem amusing—I think—is how we respond to it.
In physics and chemistry, better instruments and larger sample sizes raise the bar for making accurate predictions. But in psychology, software usability testing, and biomedical science—any discipline that relies heavily on significance testing—they have the opposite effect.
Suppose we hypothesize that louder background noises make people more irritable (shout out to the kid who sat next to me on the flight last week — you’re driving scientific progress). To test our hypothesis, let’s conduct an experiment.
Michael Lewis
W. W. Norton & Company, 2016
Amazon
Which is more probable:
My friend Franklyn will tweet this post, resulting in a few hundred pageviews.
If you chose the second option I have a book recommendation for you.
Provocative tweet of the day:
Peter Morville and Jeffery Callender
O’Reilly, 2010
Amazon
Data science projects can (very roughly) be divided into two types. The first is a study, aimed at providing quantitative insights to other business units. These typically involve building reports, calculating p-values, and answering product managers’ questions. Fifteen years ago the people doing this were called “statisticians” or “data analysts.” The second type of project feeds directly into customer-facing products. Examples include recommender systems, tagging/classification systems, and search engines.
Apologies to designers and UX researchers, to whom this is probably pretty boring. Like most content on this blog, this post is aimed at data scientists.
The following question caught a couple of my colleagues off guard: If you type “lisb” into Google, how are the predicted searches rendered?
In Elasticsearch, IDF values are calculated per shard (the article is glibly titled “Relevance is Broken”). The docs stress that this isn’t a cause for concern:
SAT is hard, but there are algorithms that tend to do okay empirically. I recently learned about the Davis-Putnam-Logemann-Loveland (DPLL) procedure and rolled up a short Python implementation.
Active learning is a subfield of machine learning that probably doesn’t receive as much attention as it should. The fundamental idea behind active learning is that some instances are more informative than others, and if a learner can choose the instances it trains on, it can learn faster than it would on an unbiased random sample.
A year-old stack overflow question that I’m able to answer? This is like spotting Bigfoot. I’m going to assume access to nothing more than a spark context. Let’s start by parallelizing some familiar sentences.
Since the mid-1980s, Piero Scaruffi has written essays on countless topics, and published them all for free on the internet – which he helped develop. You can learn more about him (and pretty much anything else that might interest you) on his legendary website.
A while back Kaggle introduced a database containing all the comments that were posted to reddit in May 2015. (The data is 30Gb in SQLite format and you can still download it here). Kagglers were encouraged to try NLP experiments with the data. One of the more interesting responses was a script that queried and displayed comments containing the /s flag, which indicates sarcasm.
